---
_id: '14841'
abstract:
- lang: eng
text: De novo heterozygous variants in KCNC2 encoding the voltage-gated potassium
(K+) channel subunit Kv3.2 are a recently described cause of developmental and
epileptic encephalopathy (DEE). A de novo variant in KCNC2 c.374G > A (p.Cys125Tyr)
was identified via exome sequencing in a patient with DEE. Relative to wild-type
Kv3.2, Kv3.2-p.Cys125Tyr induces K+ currents exhibiting a large hyperpolarizing
shift in the voltage dependence of activation, accelerated activation, and delayed
deactivation consistent with a relative stabilization of the open conformation,
along with increased current density. Leveraging the cryogenic electron microscopy
(cryo-EM) structure of Kv3.1, molecular dynamic simulations suggest that a strong
π-π stacking interaction between the variant Tyr125 and Tyr156 in the α-6 helix
of the T1 domain promotes a relative stabilization of the open conformation of
the channel, which underlies the observed gain of function. A multicompartment
computational model of a Kv3-expressing parvalbumin-positive cerebral cortex fast-spiking
γ-aminobutyric acidergic (GABAergic) interneuron (PV-IN) demonstrates how the
Kv3.2-Cys125Tyr variant impairs neuronal excitability and dysregulates inhibition
in cerebral cortex circuits to explain the resulting epilepsy.
acknowledgement: This work was supported by an ERC Consolidator Grant (SYNAPSEEK)
to T.P.V., the NOMIS Foundation through the NOMIS Fellowships program at IST Austria
to C.B.C., a Jefferson Synaptic Biology Center Pilot Project Grant to M.C., NIH
NINDS U54 NS108874 (PI, Alfred L. George), and NIH NINDS R01 NS122887 to E.M.G.
The computations were enabled by resources provided by the Swedish National Infrastructure
for Computing (SNIC) at the PDC Center for High-Performance Computing, KTH Royal
Institute of Technology, partially funded by the Swedish Research Council through
grant agreement no. 2018-05973. We thank Akshay Sridhar for the fruitful discussion
of the project.
article_number: e2307776121
article_processing_charge: No
article_type: original
author:
- first_name: Jerome
full_name: Clatot, Jerome
last_name: Clatot
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Qiansheng
full_name: Liang, Qiansheng
last_name: Liang
- first_name: Tanadet
full_name: Pipatpolkai, Tanadet
last_name: Pipatpolkai
- first_name: Shavonne L.
full_name: Massey, Shavonne L.
last_name: Massey
- first_name: Ingo
full_name: Helbig, Ingo
last_name: Helbig
- first_name: Lucie
full_name: Delemotte, Lucie
last_name: Delemotte
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Manuel
full_name: Covarrubias, Manuel
last_name: Covarrubias
- first_name: Ethan M.
full_name: Goldberg, Ethan M.
last_name: Goldberg
citation:
ama: Clatot J, Currin C, Liang Q, et al. A structurally precise mechanism links
an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction.
Proceedings of the National Academy of Sciences of the United States of America.
2024;121(3). doi:10.1073/pnas.2307776121
apa: Clatot, J., Currin, C., Liang, Q., Pipatpolkai, T., Massey, S. L., Helbig,
I., … Goldberg, E. M. (2024). A structurally precise mechanism links an epilepsy-associated
KCNC2 potassium channel mutation to interneuron dysfunction. Proceedings of
the National Academy of Sciences of the United States of America. Proceedings
of the National Academy of Sciences. https://doi.org/10.1073/pnas.2307776121
chicago: Clatot, Jerome, Christopher Currin, Qiansheng Liang, Tanadet Pipatpolkai,
Shavonne L. Massey, Ingo Helbig, Lucie Delemotte, Tim P Vogels, Manuel Covarrubias,
and Ethan M. Goldberg. “A Structurally Precise Mechanism Links an Epilepsy-Associated
KCNC2 Potassium Channel Mutation to Interneuron Dysfunction.” Proceedings of
the National Academy of Sciences of the United States of America. Proceedings
of the National Academy of Sciences, 2024. https://doi.org/10.1073/pnas.2307776121.
ieee: J. Clatot et al., “A structurally precise mechanism links an epilepsy-associated
KCNC2 potassium channel mutation to interneuron dysfunction,” Proceedings of
the National Academy of Sciences of the United States of America, vol. 121,
no. 3. Proceedings of the National Academy of Sciences, 2024.
ista: Clatot J, Currin C, Liang Q, Pipatpolkai T, Massey SL, Helbig I, Delemotte
L, Vogels TP, Covarrubias M, Goldberg EM. 2024. A structurally precise mechanism
links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction.
Proceedings of the National Academy of Sciences of the United States of America.
121(3), e2307776121.
mla: Clatot, Jerome, et al. “A Structurally Precise Mechanism Links an Epilepsy-Associated
KCNC2 Potassium Channel Mutation to Interneuron Dysfunction.” Proceedings of
the National Academy of Sciences of the United States of America, vol. 121,
no. 3, e2307776121, Proceedings of the National Academy of Sciences, 2024, doi:10.1073/pnas.2307776121.
short: J. Clatot, C. Currin, Q. Liang, T. Pipatpolkai, S.L. Massey, I. Helbig, L.
Delemotte, T.P. Vogels, M. Covarrubias, E.M. Goldberg, Proceedings of the National
Academy of Sciences of the United States of America 121 (2024).
date_created: 2024-01-21T23:00:56Z
date_published: 2024-01-16T00:00:00Z
date_updated: 2024-01-23T10:20:40Z
day: '16'
department:
- _id: TiVo
doi: 10.1073/pnas.2307776121
ec_funded: 1
external_id:
pmid:
- '38194456'
intvolume: ' 121'
issue: '3'
language:
- iso: eng
month: '01'
oa_version: None
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
publication_status: published
publisher: Proceedings of the National Academy of Sciences
quality_controlled: '1'
related_material:
link:
- relation: software
url: 'https://github.com/ChrisCurrin/pv-kcnc2 '
scopus_import: '1'
status: public
title: A structurally precise mechanism links an epilepsy-associated KCNC2 potassium
channel mutation to interneuron dysfunction
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 121
year: '2024'
...
---
_id: '14887'
abstract:
- lang: eng
text: 'Episodic memories are encoded by experience-activated neuronal ensembles
that remain necessary and sufficient for recall. However, the temporal evolution
of memory engrams after initial encoding is unclear. In this study, we employed
computational and experimental approaches to examine how the neural composition
and selectivity of engrams change with memory consolidation. Our spiking neural
network model yielded testable predictions: memories transition from unselective
to selective as neurons drop out of and drop into engrams; inhibitory activity
during recall is essential for memory selectivity; and inhibitory synaptic plasticity
during memory consolidation is critical for engrams to become selective. Using
activity-dependent labeling, longitudinal calcium imaging and a combination of
optogenetic and chemogenetic manipulations in mouse dentate gyrus, we conducted
contextual fear conditioning experiments that supported our model’s predictions.
Our results reveal that memory engrams are dynamic and that changes in engram
composition mediated by inhibitory plasticity are crucial for the emergence of
memory selectivity.'
acknowledgement: We thank S. Erisken from Inscopix for helping us establish in vivo
one-photon calcium imaging for this work. We thank K. Su at Tsinghua University
for assistance with this work. This work was funded by the President’s PhD Scholarship
from Imperial College London (D.F.T.), the Wellcome Trust (225412/Z/22/Z) (S.S.),
the Biotechnology and Biological Sciences Research Council (BB/N013956/1 and BB/N019008/1)
(C.C.), the Wellcome Trust (200790/Z/16/Z) (C.C.), the Simons Foundation (564408)
(C.C.) and the Engineering and Physical Sciences Research Council (EP/R035806/1)
(CC). The School of Life Sciences and the IDG/McGovern Institute for Brain Research
supported Y.Z. The Warren Alpert Distinguished Scholar Award and National Institutes
of Health 1K99NS125131-01 supported D.S.R.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Douglas
full_name: Feitosa Tomé, Douglas
id: 0eed2d40-3d48-11ec-8d38-f789cc2e40b2
last_name: Feitosa Tomé
- first_name: Ying
full_name: Zhang, Ying
last_name: Zhang
- first_name: Tomomi
full_name: Aida, Tomomi
last_name: Aida
- first_name: Olivia
full_name: Mosto, Olivia
last_name: Mosto
- first_name: Yifeng
full_name: Lu, Yifeng
last_name: Lu
- first_name: Mandy
full_name: Chen, Mandy
last_name: Chen
- first_name: Sadra
full_name: Sadeh, Sadra
last_name: Sadeh
- first_name: Dheeraj S.
full_name: Roy, Dheeraj S.
last_name: Roy
- first_name: Claudia
full_name: Clopath, Claudia
last_name: Clopath
citation:
ama: Feitosa Tomé D, Zhang Y, Aida T, et al. Dynamic and selective engrams emerge
with memory consolidation. Nature Neuroscience. 2024. doi:10.1038/s41593-023-01551-w
apa: Feitosa Tomé, D., Zhang, Y., Aida, T., Mosto, O., Lu, Y., Chen, M., … Clopath,
C. (2024). Dynamic and selective engrams emerge with memory consolidation. Nature
Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-023-01551-w
chicago: Feitosa Tomé, Douglas, Ying Zhang, Tomomi Aida, Olivia Mosto, Yifeng Lu,
Mandy Chen, Sadra Sadeh, Dheeraj S. Roy, and Claudia Clopath. “Dynamic and Selective
Engrams Emerge with Memory Consolidation.” Nature Neuroscience. Springer
Nature, 2024. https://doi.org/10.1038/s41593-023-01551-w.
ieee: D. Feitosa Tomé et al., “Dynamic and selective engrams emerge with
memory consolidation,” Nature Neuroscience. Springer Nature, 2024.
ista: Feitosa Tomé D, Zhang Y, Aida T, Mosto O, Lu Y, Chen M, Sadeh S, Roy DS, Clopath
C. 2024. Dynamic and selective engrams emerge with memory consolidation. Nature
Neuroscience.
mla: Feitosa Tomé, Douglas, et al. “Dynamic and Selective Engrams Emerge with Memory
Consolidation.” Nature Neuroscience, Springer Nature, 2024, doi:10.1038/s41593-023-01551-w.
short: D. Feitosa Tomé, Y. Zhang, T. Aida, O. Mosto, Y. Lu, M. Chen, S. Sadeh, D.S.
Roy, C. Clopath, Nature Neuroscience (2024).
date_created: 2024-01-28T23:01:43Z
date_published: 2024-01-19T00:00:00Z
date_updated: 2024-01-29T09:22:00Z
day: '19'
department:
- _id: TiVo
doi: 10.1038/s41593-023-01551-w
external_id:
isi:
- '001145442300001'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41593-023-01551-w
month: '01'
oa: 1
oa_version: Published Version
publication: Nature Neuroscience
publication_identifier:
eissn:
- 1546-1726
issn:
- 1097-6256
publication_status: epub_ahead
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '14892'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Dynamic and selective engrams emerge with memory consolidation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
_id: '14422'
abstract:
- lang: eng
text: "Animals exhibit a remarkable ability to learn and remember new behaviors,
skills, and associations throughout their lifetime. These capabilities are made
possible thanks to a variety of\r\nchanges in the brain throughout adulthood,
regrouped under the term \"plasticity\". Some cells\r\nin the brain —neurons—
and specifically changes in the connections between neurons, the\r\nsynapses,
were shown to be crucial for the formation, selection, and consolidation of memories\r\nfrom
past experiences. These ongoing changes of synapses across time are called synaptic\r\nplasticity.
Understanding how a myriad of biochemical processes operating at individual\r\nsynapses
can somehow work in concert to give rise to meaningful changes in behavior is
a\r\nfascinating problem and an active area of research.\r\nHowever, the experimental
search for the precise plasticity mechanisms at play in the brain\r\nis daunting,
as it is difficult to control and observe synapses during learning. Theoretical\r\napproaches
have thus been the default method to probe the plasticity-behavior connection.
Such\r\nstudies attempt to extract unifying principles across synapses and model
all observed synaptic\r\nchanges using plasticity rules: equations that govern
the evolution of synaptic strengths across\r\ntime in neuronal network models.
These rules can use many relevant quantities to determine\r\nthe magnitude of
synaptic changes, such as the precise timings of pre- and postsynaptic\r\naction
potentials, the recent neuronal activity levels, the state of neighboring synapses,
etc.\r\nHowever, analytical studies rely heavily on human intuition and are forced
to make simplifying\r\nassumptions about plasticity rules.\r\nIn this thesis,
we aim to assist and augment human intuition in this search for plasticity rules.\r\nWe
explore whether a numerical approach could automatically discover the plasticity
rules\r\nthat elicit desired behaviors in large networks of interconnected neurons.
This approach is\r\ndubbed meta-learning synaptic plasticity: learning plasticity
rules which themselves will make\r\nneuronal networks learn how to solve a desired
task. We first write all the potential plasticity\r\nmechanisms to consider using
a single expression with adjustable parameters. We then optimize\r\nthese plasticity
parameters using evolutionary strategies or Bayesian inference on tasks known\r\nto
involve synaptic plasticity, such as familiarity detection and network stabilization.\r\nWe
show that these automated approaches are powerful tools, able to complement established\r\nanalytical
methods. By comprehensively screening plasticity rules at all synapse types in\r\nrealistic,
spiking neuronal network models, we discover entire sets of degenerate plausible\r\nplasticity
rules that reliably elicit memory-related behaviors. Our approaches allow for
more\r\nrobust experimental predictions, by abstracting out the idiosyncrasies
of individual plasticity\r\nrules, and provide fresh insights on synaptic plasticity
in spiking network models.\r\n"
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
citation:
ama: 'Confavreux BJ. Synapseek: Meta-learning synaptic plasticity rules. 2023. doi:10.15479/at:ista:14422'
apa: 'Confavreux, B. J. (2023). Synapseek: Meta-learning synaptic plasticity
rules. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14422'
chicago: 'Confavreux, Basile J. “Synapseek: Meta-Learning Synaptic Plasticity Rules.”
Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:14422.'
ieee: 'B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute
of Science and Technology Austria, 2023.'
ista: 'Confavreux BJ. 2023. Synapseek: Meta-learning synaptic plasticity rules.
Institute of Science and Technology Austria.'
mla: 'Confavreux, Basile J. Synapseek: Meta-Learning Synaptic Plasticity Rules.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:14422.'
short: 'B.J. Confavreux, Synapseek: Meta-Learning Synaptic Plasticity Rules, Institute
of Science and Technology Austria, 2023.'
date_created: 2023-10-12T14:13:25Z
date_published: 2023-10-12T00:00:00Z
date_updated: 2023-10-18T09:20:56Z
day: '12'
ddc:
- '610'
degree_awarded: PhD
department:
- _id: GradSch
- _id: TiVo
doi: 10.15479/at:ista:14422
ec_funded: 1
file:
- access_level: closed
checksum: 7f636555eae7803323df287672fd13ed
content_type: application/pdf
creator: cchlebak
date_created: 2023-10-12T14:53:50Z
date_updated: 2023-10-12T14:54:52Z
embargo: 2024-10-12
embargo_to: open_access
file_id: '14424'
file_name: Confavreux_Thesis_2A.pdf
file_size: 30599717
relation: main_file
- access_level: closed
checksum: 725e85946db92290a4583a0de9779e1b
content_type: application/x-zip-compressed
creator: cchlebak
date_created: 2023-10-18T07:38:34Z
date_updated: 2023-10-18T07:56:08Z
file_id: '14440'
file_name: Confavreux Thesis.zip
file_size: 68406739
relation: source_file
file_date_updated: 2023-10-18T07:56:08Z
has_accepted_license: '1'
language:
- iso: eng
month: '10'
oa_version: Published Version
page: '148'
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '9633'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
title: 'Synapseek: Meta-learning synaptic plasticity rules'
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '14666'
abstract:
- lang: eng
text: So-called spontaneous activity is a central hallmark of most nervous systems.
Such non-causal firing is contrary to the tenet of spikes as a means of communication,
and its purpose remains unclear. We propose that self-initiated firing can serve
as a release valve to protect neurons from the toxic conditions arising in mitochondria
from lower-than-baseline energy consumption. To demonstrate the viability of our
hypothesis, we built a set of models that incorporate recent experimental results
indicating homeostatic control of metabolic products—Adenosine triphosphate (ATP),
adenosine diphosphate (ADP), and reactive oxygen species (ROS)—by changes in firing.
We explore the relationship of metabolic cost of spiking with its effect on the
temporal patterning of spikes and reproduce experimentally observed changes in
intrinsic firing in the fruitfly dorsal fan-shaped body neuron in a model with
ROS-modulated potassium channels. We also show that metabolic spiking homeostasis
can produce indefinitely sustained avalanche dynamics in cortical circuits. Our
theory can account for key features of neuronal activity observed in many studies
ranging from ion channel function all the way to resting state dynamics. We finish
with a set of experimental predictions that would confirm an integrated, crucial
role for metabolically regulated spiking and firmly link metabolic homeostasis
and neuronal function.
acknowledgement: We thank Prof. C. Nazaret and Prof. J.-P. Mazat for sharing the code
of their mitochondrial model. We also thank G. Miesenböck, E. Marder, L. Abbott,
A. Kempf, P. Hasenhuetl, W. Podlaski, F. Zenke, E. Agnes, P. Bozelos, J. Watson,
B. Confavreux, and G. Christodoulou, and the rest of the Vogels Lab for their feedback.
This work was funded by Wellcome Trust and Royal Society Sir Henry Dale Research
Fellowship (WT100000), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z),
and a UK Research and Innovation, Biotechnology and Biological Sciences Research
Council grant (UKRI-BBSRC BB/N019512/1).
article_number: e2306525120
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Chaitanya
full_name: Chintaluri, Chaitanya
id: E4EDB536-3485-11EA-98D2-20AF3DDC885E
last_name: Chintaluri
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Chintaluri C, Vogels TP. Metabolically regulated spiking could serve neuronal
energy homeostasis and protect from reactive oxygen species. Proceedings of
the National Academy of Sciences of the United States of America. 2023;120(48).
doi:10.1073/pnas.2306525120
apa: Chintaluri, C., & Vogels, T. P. (2023). Metabolically regulated spiking
could serve neuronal energy homeostasis and protect from reactive oxygen species.
Proceedings of the National Academy of Sciences of the United States of America.
National Academy of Sciences. https://doi.org/10.1073/pnas.2306525120
chicago: Chintaluri, Chaitanya, and Tim P Vogels. “Metabolically Regulated Spiking
Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.”
Proceedings of the National Academy of Sciences of the United States of America.
National Academy of Sciences, 2023. https://doi.org/10.1073/pnas.2306525120.
ieee: C. Chintaluri and T. P. Vogels, “Metabolically regulated spiking could serve
neuronal energy homeostasis and protect from reactive oxygen species,” Proceedings
of the National Academy of Sciences of the United States of America, vol.
120, no. 48. National Academy of Sciences, 2023.
ista: Chintaluri C, Vogels TP. 2023. Metabolically regulated spiking could serve
neuronal energy homeostasis and protect from reactive oxygen species. Proceedings
of the National Academy of Sciences of the United States of America. 120(48),
e2306525120.
mla: Chintaluri, Chaitanya, and Tim P. Vogels. “Metabolically Regulated Spiking
Could Serve Neuronal Energy Homeostasis and Protect from Reactive Oxygen Species.”
Proceedings of the National Academy of Sciences of the United States of America,
vol. 120, no. 48, e2306525120, National Academy of Sciences, 2023, doi:10.1073/pnas.2306525120.
short: C. Chintaluri, T.P. Vogels, Proceedings of the National Academy of Sciences
of the United States of America 120 (2023).
date_created: 2023-12-10T23:01:00Z
date_published: 2023-11-21T00:00:00Z
date_updated: 2023-12-11T12:47:41Z
day: '21'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1073/pnas.2306525120
external_id:
pmid:
- '37988463'
file:
- access_level: open_access
checksum: bf4ec38602a70dae4338077a5a4d497f
content_type: application/pdf
creator: dernst
date_created: 2023-12-11T12:45:12Z
date_updated: 2023-12-11T12:45:12Z
file_id: '14678'
file_name: 2023_PNAS_Chintaluri.pdf
file_size: 16891602
relation: main_file
success: 1
file_date_updated: 2023-12-11T12:45:12Z
has_accepted_license: '1'
intvolume: ' 120'
issue: '48'
language:
- iso: eng
month: '11'
oa: 1
oa_version: None
pmid: 1
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ccluri/metabolic_spiking
scopus_import: '1'
status: public
title: Metabolically regulated spiking could serve neuronal energy homeostasis and
protect from reactive oxygen species
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 120
year: '2023'
...
---
_id: '14892'
abstract:
- lang: eng
text: 'Code and data necessary to reproduce the simulations and data analyses reported
in our manuscript: Tomé, D.F., Zhang, Y., Aida, T., Mosto, O., Lu, Y., Chen, M.,
Sadeh, S., Roy, D. S., Clopath, C. Dynamic and selective engrams emerge with memory
consolidation. 2023.'
article_processing_charge: No
author:
- first_name: Douglas
full_name: Feitosa Tomé, Douglas
id: 0eed2d40-3d48-11ec-8d38-f789cc2e40b2
last_name: Feitosa Tomé
citation:
ama: 'Feitosa Tomé D. douglastome/dynamic-engrams: Dynamic and selective engrams
emerge with memory consolidation. 2023. doi:10.5281/ZENODO.10251087'
apa: 'Feitosa Tomé, D. (2023). douglastome/dynamic-engrams: Dynamic and selective
engrams emerge with memory consolidation. Zenodo. https://doi.org/10.5281/ZENODO.10251087'
chicago: 'Feitosa Tomé, Douglas. “Douglastome/Dynamic-Engrams: Dynamic and Selective
Engrams Emerge with Memory Consolidation.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.10251087.'
ieee: 'D. Feitosa Tomé, “douglastome/dynamic-engrams: Dynamic and selective engrams
emerge with memory consolidation.” Zenodo, 2023.'
ista: 'Feitosa Tomé D. 2023. douglastome/dynamic-engrams: Dynamic and selective
engrams emerge with memory consolidation, Zenodo, 10.5281/ZENODO.10251087.'
mla: 'Feitosa Tomé, Douglas. Douglastome/Dynamic-Engrams: Dynamic and Selective
Engrams Emerge with Memory Consolidation. Zenodo, 2023, doi:10.5281/ZENODO.10251087.'
short: D. Feitosa Tomé, (2023).
date_created: 2024-01-29T09:06:43Z
date_published: 2023-12-02T00:00:00Z
date_updated: 2024-01-29T09:22:01Z
day: '02'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.5281/ZENODO.10251087
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.10251087
month: '12'
oa: 1
oa_version: None
publisher: Zenodo
related_material:
record:
- id: '14887'
relation: used_in_publication
status: public
status: public
title: 'douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory
consolidation'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14993'
abstract:
- lang: eng
text: "Traditional top-down approaches for global health have historically failed
to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015).
Recently, however, a more holistic, multi-level approach termed One Health (OH)
(Osterhaus et al., 2020) is being adopted. Several sets of challenges have been
identified for the implementation of OH (dos S. Ribeiro et al., 2019), including
policy and funding, education and training, and multi-actor, multi-domain, and
multi-level collaborations. These exist despite the increasing accessibility to\r\nknowledge
and digital collaborative research tools through the internet. To address some
of these challenges, we propose a general framework for grassroots community-based
means of participatory research. Additionally, we present a specific roadmap to
create a Machine Learning for Global Health community in Africa. The proposed
framework aims to enable any small group of individuals with scarce resources
to build and sustain an online community within approximately two years. We provide
a discussion on the potential impact of the proposed framework for global health
research collaborations."
acknowledgement: "Houcemeddine Turki’s contributions to this final output have been
funded through the Adapting\r\nWikidata to support clinical practice using Data
Science, Semantic Web and Machine Learning\r\nproject, which is part of the Wikimedia
Research Fund maintained by the Wikimedia Foundation in San Francisco, California,
United States of America."
article_processing_charge: No
author:
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Mercy Nyamewaa
full_name: Asiedu , Mercy Nyamewaa
last_name: 'Asiedu '
- first_name: Chris
full_name: Fourie, Chris
last_name: Fourie
- first_name: Benjamin
full_name: Rosman, Benjamin
last_name: Rosman
- first_name: Houcemeddine
full_name: Turki, Houcemeddine
last_name: Turki
- first_name: Atnafu
full_name: Lambebo Tonja, Atnafu
last_name: Lambebo Tonja
- first_name: Jade
full_name: Abbott, Jade
last_name: Abbott
- first_name: Marvellous
full_name: Ajala, Marvellous
last_name: Ajala
- first_name: Sadiq Adewale
full_name: Adedayo, Sadiq Adewale
last_name: Adedayo
- first_name: Chris Chinenye
full_name: Emezue, Chris Chinenye
last_name: Emezue
- first_name: Daphne
full_name: Machangara, Daphne
last_name: Machangara
citation:
ama: 'Currin C, Asiedu MN, Fourie C, et al. A framework for grassroots research
collaboration in machine learning and global health. In: 1st Workshop on Machine
Learning & Global Health. OpenReview; 2023.'
apa: 'Currin, C., Asiedu , M. N., Fourie, C., Rosman, B., Turki, H., Lambebo Tonja,
A., … Machangara, D. (2023). A framework for grassroots research collaboration
in machine learning and global health. In 1st Workshop on Machine Learning
& Global Health. Kigali, Rwanda: OpenReview.'
chicago: Currin, Christopher, Mercy Nyamewaa Asiedu , Chris Fourie, Benjamin Rosman,
Houcemeddine Turki, Atnafu Lambebo Tonja, Jade Abbott, et al. “A Framework for
Grassroots Research Collaboration in Machine Learning and Global Health.” In 1st
Workshop on Machine Learning & Global Health. OpenReview, 2023.
ieee: C. Currin et al., “A framework for grassroots research collaboration
in machine learning and global health,” in 1st Workshop on Machine Learning
& Global Health, Kigali, Rwanda, 2023.
ista: 'Currin C, Asiedu MN, Fourie C, Rosman B, Turki H, Lambebo Tonja A, Abbott
J, Ajala M, Adedayo SA, Emezue CC, Machangara D. 2023. A framework for grassroots
research collaboration in machine learning and global health. 1st Workshop on
Machine Learning & Global Health. ICLR: International Conference on Learning
Representations.'
mla: Currin, Christopher, et al. “A Framework for Grassroots Research Collaboration
in Machine Learning and Global Health.” 1st Workshop on Machine Learning &
Global Health, OpenReview, 2023.
short: C. Currin, M.N. Asiedu , C. Fourie, B. Rosman, H. Turki, A. Lambebo Tonja,
J. Abbott, M. Ajala, S.A. Adedayo, C.C. Emezue, D. Machangara, in:, 1st Workshop
on Machine Learning & Global Health, OpenReview, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-05
date_created: 2024-02-14T15:11:48Z
date_published: 2023-03-02T00:00:00Z
date_updated: 2024-02-28T12:12:00Z
day: '02'
department:
- _id: TiVo
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=jHY_G91R880
month: '03'
oa: 1
oa_version: Published Version
publication: 1st Workshop on Machine Learning & Global Health
publication_status: published
publisher: OpenReview
quality_controlled: '1'
status: public
title: A framework for grassroots research collaboration in machine learning and global
health
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12866'
abstract:
- lang: eng
text: "Autism spectrum disorder (ASD) and epilepsy are frequently comorbid neurodevelopmental
disorders. Extensive research has demonstrated shared pathological pathways, etiologies,
and phenotypes. Many risk factors for these disorders, like genetic mutations
and environmental pressures, are linked to changes in childhood brain development,
which is a critical period for their manifestation.\r\nDecades of research have
yielded many signatures for ASD and epilepsy, some shared and others unique or
opposing. The anatomical, physiological, and behavioral correlates of these disorders
are discussed in this chapter in the context of understanding shared pathological
pathways. We end with important takeaways on the presentation, prevention, intervention,
and policy changes for ASD and epilepsy. This chapter aims to explore the complexity
of these disorders, both in etiology and phenotypes, with the further goal of
appreciating the expanse of unknowns still to explore about the brain."
alternative_title:
- 'Vol. 1: Biological Development and Physical Health'
article_processing_charge: No
author:
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Chad
full_name: Beyer, Chad
last_name: Beyer
citation:
ama: 'Currin C, Beyer C. Altered childhood brain development in autism and epilepsy.
In: Halpern-Felsher B, ed. Encyclopedia of Child and Adolescent Health.
1st ed. Elsevier; 2023:86-98. doi:10.1016/b978-0-12-818872-9.00129-1'
apa: Currin, C., & Beyer, C. (2023). Altered childhood brain development in
autism and epilepsy. In B. Halpern-Felsher (Ed.), Encyclopedia of Child and
Adolescent Health (1st ed., pp. 86–98). Elsevier. https://doi.org/10.1016/b978-0-12-818872-9.00129-1
chicago: Currin, Christopher, and Chad Beyer. “Altered Childhood Brain Development
in Autism and Epilepsy.” In Encyclopedia of Child and Adolescent Health,
edited by Bonnie Halpern-Felsher, 1st ed., 86–98. Elsevier, 2023. https://doi.org/10.1016/b978-0-12-818872-9.00129-1.
ieee: C. Currin and C. Beyer, “Altered childhood brain development in autism and
epilepsy,” in Encyclopedia of Child and Adolescent Health, 1st ed., B.
Halpern-Felsher, Ed. Elsevier, 2023, pp. 86–98.
ista: 'Currin C, Beyer C. 2023.Altered childhood brain development in autism and
epilepsy. In: Encyclopedia of Child and Adolescent Health. Vol. 1: Biological
Development and Physical Health, , 86–98.'
mla: Currin, Christopher, and Chad Beyer. “Altered Childhood Brain Development in
Autism and Epilepsy.” Encyclopedia of Child and Adolescent Health, edited
by Bonnie Halpern-Felsher, 1st ed., Elsevier, 2023, pp. 86–98, doi:10.1016/b978-0-12-818872-9.00129-1.
short: C. Currin, C. Beyer, in:, B. Halpern-Felsher (Ed.), Encyclopedia of Child
and Adolescent Health, 1st ed., Elsevier, 2023, pp. 86–98.
date_created: 2023-04-25T07:52:43Z
date_published: 2023-02-01T00:00:00Z
date_updated: 2023-04-25T09:25:40Z
day: '01'
department:
- _id: TiVo
doi: 10.1016/b978-0-12-818872-9.00129-1
edition: '1'
editor:
- first_name: Bonnie
full_name: Halpern-Felsher, Bonnie
last_name: Halpern-Felsher
language:
- iso: eng
month: '02'
oa_version: None
page: 86-98
publication: Encyclopedia of Child and Adolescent Health
publication_identifier:
isbn:
- '9780128188736'
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: Altered childhood brain development in autism and epilepsy
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '10753'
abstract:
- lang: eng
text: This is a comment on "Meta-learning synaptic plasticity and memory addressing
for continual familiarity detection." Neuron. 2022 Feb 2;110(3):544-557.e8.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: 'Confavreux BJ, Vogels TP. A familiar thought: Machines that replace us? Neuron.
2022;110(3):361-362. doi:10.1016/j.neuron.2022.01.014'
apa: 'Confavreux, B. J., & Vogels, T. P. (2022). A familiar thought: Machines
that replace us? Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2022.01.014'
chicago: 'Confavreux, Basile J, and Tim P Vogels. “A Familiar Thought: Machines
That Replace Us?” Neuron. Elsevier, 2022. https://doi.org/10.1016/j.neuron.2022.01.014.'
ieee: 'B. J. Confavreux and T. P. Vogels, “A familiar thought: Machines that replace
us?,” Neuron, vol. 110, no. 3. Elsevier, pp. 361–362, 2022.'
ista: 'Confavreux BJ, Vogels TP. 2022. A familiar thought: Machines that replace
us? Neuron. 110(3), 361–362.'
mla: 'Confavreux, Basile J., and Tim P. Vogels. “A Familiar Thought: Machines That
Replace Us?” Neuron, vol. 110, no. 3, Elsevier, 2022, pp. 361–62, doi:10.1016/j.neuron.2022.01.014.'
short: B.J. Confavreux, T.P. Vogels, Neuron 110 (2022) 361–362.
date_created: 2022-02-13T23:01:34Z
date_published: 2022-02-02T00:00:00Z
date_updated: 2023-10-03T10:53:17Z
day: '02'
department:
- _id: TiVo
doi: 10.1016/j.neuron.2022.01.014
external_id:
isi:
- '000751819100005'
pmid:
- '35114107'
intvolume: ' 110'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.neuron.2022.01.014
month: '02'
oa: 1
oa_version: Published Version
page: 361-362
pmid: 1
publication: Neuron
publication_identifier:
eissn:
- 1097-4199
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'A familiar thought: Machines that replace us?'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 110
year: '2022'
...
---
_id: '11143'
abstract:
- lang: eng
text: 'Dravet syndrome is a neurodevelopmental disorder characterized by epilepsy,
intellectual disability, and sudden death due to pathogenic variants in SCN1A
with loss of function of the sodium channel subunit Nav1.1. Nav1.1-expressing
parvalbumin GABAergic interneurons (PV-INs) from young Scn1a+/− mice show impaired
action potential generation. An approach assessing PV-IN function in the same
mice at two time points shows impaired spike generation in all Scn1a+/− mice at
postnatal days (P) 16–21, whether deceased prior or surviving to P35, with normalization
by P35 in surviving mice. However, PV-IN synaptic transmission is dysfunctional
in young Scn1a+/− mice that did not survive and in Scn1a+/− mice ≥ P35. Modeling
confirms that PV-IN axonal propagation is more sensitive to decreased sodium conductance
than spike generation. These results demonstrate dynamic dysfunction in Dravet
syndrome: combined abnormalities of PV-IN spike generation and propagation drives
early disease severity, while ongoing dysfunction of synaptic transmission contributes
to chronic pathology.'
acknowledgement: We would like to thank Bernardo Rudy, Joanna Mattis, and Laura Mcgarry
for comments on a previous version of the manuscript; Xiaohong Zhang for expert
technical support and mouse colony maintenance; Melody Cheng for assistance with
generation of the graphical abstract; and Jennifer Kearney for the gift of Scn1a+/−
mice. This work was supported by the National Institute of Neurological Disorders
and Stroke of the National Institutes of Health under F31NS111803 (to K.M.G.) and
K08NS097633 and R01NS110869 (to E.M.G.), the Dravet Syndrome Foundation (to A.S.),
an ERC Consolidator Grant (SYNAPSEEK) (to T.P.V.), and the NOMIS Foundation through
the NOMIS Fellowships program at IST Austria (to C.C.). The graphical abstract was
prepared using BioRender software (BioRender.com).
article_number: '110580'
article_processing_charge: No
article_type: original
author:
- first_name: Keisuke
full_name: Kaneko, Keisuke
last_name: Kaneko
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Kevin M.
full_name: Goff, Kevin M.
last_name: Goff
- first_name: Eric R.
full_name: Wengert, Eric R.
last_name: Wengert
- first_name: Ala
full_name: Somarowthu, Ala
last_name: Somarowthu
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Ethan M.
full_name: Goldberg, Ethan M.
last_name: Goldberg
citation:
ama: Kaneko K, Currin C, Goff KM, et al. Developmentally regulated impairment of
parvalbumin interneuron synaptic transmission in an experimental model of Dravet
syndrome. Cell Reports. 2022;38(13). doi:10.1016/j.celrep.2022.110580
apa: Kaneko, K., Currin, C., Goff, K. M., Wengert, E. R., Somarowthu, A., Vogels,
T. P., & Goldberg, E. M. (2022). Developmentally regulated impairment of parvalbumin
interneuron synaptic transmission in an experimental model of Dravet syndrome.
Cell Reports. Elsevier. https://doi.org/10.1016/j.celrep.2022.110580
chicago: Kaneko, Keisuke, Christopher Currin, Kevin M. Goff, Eric R. Wengert, Ala
Somarowthu, Tim P Vogels, and Ethan M. Goldberg. “Developmentally Regulated Impairment
of Parvalbumin Interneuron Synaptic Transmission in an Experimental Model of Dravet
Syndrome.” Cell Reports. Elsevier, 2022. https://doi.org/10.1016/j.celrep.2022.110580.
ieee: K. Kaneko et al., “Developmentally regulated impairment of parvalbumin
interneuron synaptic transmission in an experimental model of Dravet syndrome,”
Cell Reports, vol. 38, no. 13. Elsevier, 2022.
ista: Kaneko K, Currin C, Goff KM, Wengert ER, Somarowthu A, Vogels TP, Goldberg
EM. 2022. Developmentally regulated impairment of parvalbumin interneuron synaptic
transmission in an experimental model of Dravet syndrome. Cell Reports. 38(13),
110580.
mla: Kaneko, Keisuke, et al. “Developmentally Regulated Impairment of Parvalbumin
Interneuron Synaptic Transmission in an Experimental Model of Dravet Syndrome.”
Cell Reports, vol. 38, no. 13, 110580, Elsevier, 2022, doi:10.1016/j.celrep.2022.110580.
short: K. Kaneko, C. Currin, K.M. Goff, E.R. Wengert, A. Somarowthu, T.P. Vogels,
E.M. Goldberg, Cell Reports 38 (2022).
date_created: 2022-04-10T22:01:39Z
date_published: 2022-03-29T00:00:00Z
date_updated: 2023-08-03T06:32:55Z
day: '29'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1016/j.celrep.2022.110580
ec_funded: 1
external_id:
isi:
- '000779794000001'
file:
- access_level: open_access
checksum: 49105c6c27c9af0f37f50a8bbb4d380d
content_type: application/pdf
creator: dernst
date_created: 2022-04-15T11:00:58Z
date_updated: 2022-04-15T11:00:58Z
file_id: '11172'
file_name: 2022_CellReports_Kaneko.pdf
file_size: 4774216
relation: main_file
success: 1
file_date_updated: 2022-04-15T11:00:58Z
has_accepted_license: '1'
intvolume: ' 38'
isi: 1
issue: '13'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
- _id: 9B861AAC-BA93-11EA-9121-9846C619BF3A
name: NOMIS Fellowship Program
publication: Cell Reports
publication_identifier:
eissn:
- 2211-1247
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Developmentally regulated impairment of parvalbumin interneuron synaptic transmission
in an experimental model of Dravet syndrome
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 38
year: '2022'
...
---
_id: '8125'
abstract:
- lang: eng
text: Context, such as behavioral state, is known to modulate memory formation and
retrieval, but is usually ignored in associative memory models. Here, we propose
several types of contextual modulation for associative memory networks that greatly
increase their performance. In these networks, context inactivates specific neurons
and connections, which modulates the effective connectivity of the network. Memories
are stored only by the active components, thereby reducing interference from memories
acquired in other contexts. Such networks exhibit several beneficial characteristics,
including enhanced memory capacity, high robustness to noise, increased robustness
to memory overloading, and better memory retention during continual learning.
Furthermore, memories can be biased to have different relative strengths, or even
gated on or off, according to contextual cues, providing a candidate model for
cognitive control of memory and efficient memory search. An external context-encoding
network can dynamically switch the memory network to a desired state, which we
liken to experimentally observed contextual signals in prefrontal cortex and hippocampus.
Overall, our work illustrates the benefits of organizing memory around context,
and provides an important link between behavioral studies of memory and mechanistic
details of neural circuits.SIGNIFICANCEMemory
is context dependent — both encoding and recall vary in effectiveness and speed
depending on factors like location and brain state during a task. We apply this
idea to a simple computational model of associative memory through contextual
gating of neurons and synaptic connections. Intriguingly, this results in several
advantages, including vastly enhanced memory capacity, better robustness, and
flexible memory gating. Our model helps to explain (i) how gating and inhibition
contribute to memory processes, (ii) how memory access dynamically changes over
time, and (iii) how context representations, such as those observed in hippocampus
and prefrontal cortex, may interact with and control memory processes.
article_processing_charge: No
author:
- first_name: William F.
full_name: Podlaski, William F.
last_name: Podlaski
orcid: 0000-0001-6619-7502
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
orcid: 0000-0001-7184-7311
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Podlaski WF, Agnes EJ, Vogels TP. High capacity and dynamic accessibility in
associative memory networks with context-dependent neuronal and synaptic gating.
bioRxiv. 2022. doi:10.1101/2020.01.08.898528
apa: Podlaski, W. F., Agnes, E. J., & Vogels, T. P. (2022). High capacity and
dynamic accessibility in associative memory networks with context-dependent neuronal
and synaptic gating. bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.01.08.898528
chicago: Podlaski, William F., Everton J. Agnes, and Tim P Vogels. “High Capacity
and Dynamic Accessibility in Associative Memory Networks with Context-Dependent
Neuronal and Synaptic Gating.” BioRxiv. Cold Spring Harbor Laboratory,
2022. https://doi.org/10.1101/2020.01.08.898528.
ieee: W. F. Podlaski, E. J. Agnes, and T. P. Vogels, “High capacity and dynamic
accessibility in associative memory networks with context-dependent neuronal and
synaptic gating,” bioRxiv. Cold Spring Harbor Laboratory, 2022.
ista: Podlaski WF, Agnes EJ, Vogels TP. 2022. High capacity and dynamic accessibility
in associative memory networks with context-dependent neuronal and synaptic gating.
bioRxiv, 10.1101/2020.01.08.898528.
mla: Podlaski, William F., et al. “High Capacity and Dynamic Accessibility in Associative
Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” BioRxiv,
Cold Spring Harbor Laboratory, 2022, doi:10.1101/2020.01.08.898528.
short: W.F. Podlaski, E.J. Agnes, T.P. Vogels, BioRxiv (2022).
date_created: 2020-07-16T12:24:28Z
date_published: 2022-12-21T00:00:00Z
date_updated: 2024-03-06T12:03:59Z
day: '21'
department:
- _id: TiVo
doi: 10.1101/2020.01.08.898528
language:
- iso: eng
locked: '1'
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.1101/2020.01.08.898528 '
month: '12'
oa: 1
oa_version: Preprint
publication: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
status: public
title: High capacity and dynamic accessibility in associative memory networks with
context-dependent neuronal and synaptic gating
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13239'
abstract:
- lang: eng
text: Brains are thought to engage in predictive learning - learning to predict
upcoming stimuli - to construct an internal model of their environment. This is
especially notable for spatial navigation, as first described by Tolman’s latent
learning tasks. However, predictive learning has also been observed in sensory
cortex, in settings unrelated to spatial navigation. Apart from normative frameworks
such as active inference or efficient coding, what could be the utility of learning
to predict the patterns of occurrence of correlated stimuli? Here we show that
prediction, and thereby the construction of an internal model of sequential stimuli,
can bootstrap the learning process of a working memory task in a recurrent neural
network. We implemented predictive learning alongside working memory match-tasks,
and networks emerged to solve the prediction task first by encoding information
across time to predict upcoming stimuli, and then eavesdropped on this solution
to solve the matching task. Eavesdropping was most beneficial when neural resources
were limited. Hence, predictive learning acts as a general neural mechanism to
learn to store sensory information that can later be essential for working memory
tasks.
acknowledgement: "The authors would like to thank members of the Vogels lab and Manohar
lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans
for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP
was supported by funding from the Biotechnology and Biological Sciences Research
Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator
Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X
and Leverhulme Grant RPG-2018-310."
article_processing_charge: No
author:
- first_name: Thijs L.
full_name: Van Der Plas, Thijs L.
last_name: Van Der Plas
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Sanjay G.
full_name: Manohar, Sanjay G.
last_name: Manohar
citation:
ama: 'Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural
networks to learn complex working memory tasks. In: Proceedings of Machine
Learning Research. Vol 199. ML Research Press; 2022:518-531.'
apa: Van Der Plas, T. L., Vogels, T. P., & Manohar, S. G. (2022). Predictive
learning enables neural networks to learn complex working memory tasks. In Proceedings
of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press.
chicago: Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive
Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In Proceedings
of Machine Learning Research, 199:518–31. ML Research Press, 2022.
ieee: T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning
enables neural networks to learn complex working memory tasks,” in Proceedings
of Machine Learning Research, 2022, vol. 199, pp. 518–531.
ista: Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables
neural networks to learn complex working memory tasks. Proceedings of Machine
Learning Research. vol. 199, 518–531.
mla: Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks
to Learn Complex Working Memory Tasks.” Proceedings of Machine Learning Research,
vol. 199, ML Research Press, 2022, pp. 518–31.
short: T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine
Learning Research, ML Research Press, 2022, pp. 518–531.
date_created: 2023-07-16T22:01:12Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2023-07-18T06:36:28Z
day: '01'
ddc:
- '000'
department:
- _id: TiVo
ec_funded: 1
file:
- access_level: open_access
checksum: 7530a93ef42e10b4db1e5e4b69796e93
content_type: application/pdf
creator: dernst
date_created: 2023-07-18T06:32:38Z
date_updated: 2023-07-18T06:32:38Z
file_id: '13243'
file_name: 2022_PMLR_vanderPlas.pdf
file_size: 585135
relation: main_file
success: 1
file_date_updated: 2023-07-18T06:32:38Z
has_accepted_license: '1'
intvolume: ' 199'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 518-531
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Proceedings of Machine Learning Research
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predictive learning enables neural networks to learn complex working memory
tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 199
year: '2022'
...
---
_id: '12009'
abstract:
- lang: eng
text: Changes in the short-term dynamics of excitatory synapses over development
have been observed throughout cortex, but their purpose and consequences remain
unclear. Here, we propose that developmental changes in synaptic dynamics buffer
the effect of slow inhibitory long-term plasticity, allowing for continuously
stable neural activity. Using computational modeling we demonstrate that early
in development excitatory short-term depression quickly stabilises neural activity,
even in the face of strong, unbalanced excitation. We introduce a model of the
commonly observed developmental shift from depression to facilitation and show
that neural activity remains stable throughout development, while inhibitory synaptic
plasticity slowly balances excitation, consistent with experimental observations.
Our model predicts changes in the input responses from phasic to phasic-and-tonic
and more precise spike timings. We also observe a gradual emergence of short-lasting
memory traces governed by short-term plasticity development. We conclude that
the developmental depression-to-facilitation shift may control excitation-inhibition
balance throughout development with important functional consequences.
acknowledgement: We would like to thank the Vogels Lab for feedback on an earlier
version of this manuscript. D.W.J. was supported by a Marshall Scholarship and a
Clarendon Scholarship. R.P.C. and T.P.V. were supported by a Wellcome Trust and
Royal Society Sir Henry Dale Fellowship (WT 100000), a Wellcome Trust Senior Research
Fellowship (214316/Z/18/Z), and an ERC Consolidator Grant (SYNAPSEEK).
article_number: '873'
article_processing_charge: No
article_type: original
author:
- first_name: David W.
full_name: Jia, David W.
last_name: Jia
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Rui Ponte
full_name: Costa, Rui Ponte
last_name: Costa
citation:
ama: Jia DW, Vogels TP, Costa RP. Developmental depression-to-facilitation shift
controls excitation-inhibition balance. Communications biology. 2022;5.
doi:10.1038/s42003-022-03801-2
apa: Jia, D. W., Vogels, T. P., & Costa, R. P. (2022). Developmental depression-to-facilitation
shift controls excitation-inhibition balance. Communications Biology. Springer
Nature. https://doi.org/10.1038/s42003-022-03801-2
chicago: Jia, David W., Tim P Vogels, and Rui Ponte Costa. “Developmental Depression-to-Facilitation
Shift Controls Excitation-Inhibition Balance.” Communications Biology.
Springer Nature, 2022. https://doi.org/10.1038/s42003-022-03801-2.
ieee: D. W. Jia, T. P. Vogels, and R. P. Costa, “Developmental depression-to-facilitation
shift controls excitation-inhibition balance,” Communications biology,
vol. 5. Springer Nature, 2022.
ista: Jia DW, Vogels TP, Costa RP. 2022. Developmental depression-to-facilitation
shift controls excitation-inhibition balance. Communications biology. 5, 873.
mla: Jia, David W., et al. “Developmental Depression-to-Facilitation Shift Controls
Excitation-Inhibition Balance.” Communications Biology, vol. 5, 873, Springer
Nature, 2022, doi:10.1038/s42003-022-03801-2.
short: D.W. Jia, T.P. Vogels, R.P. Costa, Communications Biology 5 (2022).
date_created: 2022-09-04T22:02:02Z
date_published: 2022-08-25T00:00:00Z
date_updated: 2023-08-03T13:22:42Z
day: '25'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1038/s42003-022-03801-2
ec_funded: 1
external_id:
isi:
- '000844814800007'
file:
- access_level: open_access
checksum: 3ec724c4f6d3440028c217305e32915f
content_type: application/pdf
creator: dernst
date_created: 2022-09-05T08:55:11Z
date_updated: 2022-09-05T08:55:11Z
file_id: '12022'
file_name: 2022_CommBiology_Jia.pdf
file_size: 2491191
relation: main_file
success: 1
file_date_updated: 2022-09-05T08:55:11Z
has_accepted_license: '1'
intvolume: ' 5'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Communications biology
publication_identifier:
eissn:
- 2399-3642
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Developmental depression-to-facilitation shift controls excitation-inhibition
balance
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 5
year: '2022'
...
---
_id: '12084'
abstract:
- lang: eng
text: Neuronal networks encode information through patterns of activity that define
the networks’ function. The neurons’ activity relies on specific connectivity
structures, yet the link between structure and function is not fully understood.
Here, we tackle this structure-function problem with a new conceptual approach.
Instead of manipulating the connectivity directly, we focus on upper triangular
matrices, which represent the network dynamics in a given orthonormal basis obtained
by the Schur decomposition. This abstraction allows us to independently manipulate
the eigenspectrum and feedforward structures of a connectivity matrix. Using this
method, we describe a diverse repertoire of non-normal transient amplification,
and to complement the analysis of the dynamical regimes, we quantify the geometry
of output trajectories through the effective rank of both the eigenvector and
the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s
imaginary distribution leads to highly amplifying regimes in linear and long-lasting
dynamics in nonlinear networks. We also find a trade-off between amplification
and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space.
Networks that can amplify a large number of orthogonal initial conditions produce
neuronal trajectories that lie in the same subspace of the neuronal state-space.
Finally, we examine networks of excitatory and inhibitory neurons. We find that
the strength of global inhibition is directly linked with the amplitude of amplification,
such that weakening inhibitory weights also decreases amplification, and that
the eigenspectrum’s imaginary distribution grows with an increase in the ratio
between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths.
Consequently, the strength of global inhibition reveals itself as a strong signature
for amplification and a potential control mechanism to switch dynamical regimes.
Our results shed a light on how biological networks, i.e., networks constrained
by Dale’s law, may be optimised for specific dynamical regimes.
acknowledgement: 'We thank Friedemann Zenke for his comments, especially on the effect
of the self loops on the spectrum. We also thank Ken Miller and Bill Podlaski for
helpful comments. This research was funded by a Wellcome Trust and Royal Society
Henry Dale Research Fellowship (WT100000; TPV), a Wellcome Senior Research Fellowship
(214316/Z/18/Z; GC, EJA, and TPV), and a Research Project Grant by the Leverhulme
Trust (RPG-2016-446; EJA and TPV). '
article_number: e1010365
article_processing_charge: No
article_type: original
author:
- first_name: Georgia
full_name: Christodoulou, Georgia
last_name: Christodoulou
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
citation:
ama: Christodoulou G, Vogels TP, Agnes EJ. Regimes and mechanisms of transient amplification
in abstract and biological neural networks. PLoS Computational Biology.
2022;18(8). doi:10.1371/journal.pcbi.1010365
apa: Christodoulou, G., Vogels, T. P., & Agnes, E. J. (2022). Regimes and mechanisms
of transient amplification in abstract and biological neural networks. PLoS
Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010365
chicago: Christodoulou, Georgia, Tim P Vogels, and Everton J. Agnes. “Regimes and
Mechanisms of Transient Amplification in Abstract and Biological Neural Networks.”
PLoS Computational Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pcbi.1010365.
ieee: G. Christodoulou, T. P. Vogels, and E. J. Agnes, “Regimes and mechanisms of
transient amplification in abstract and biological neural networks,” PLoS Computational
Biology, vol. 18, no. 8. Public Library of Science, 2022.
ista: Christodoulou G, Vogels TP, Agnes EJ. 2022. Regimes and mechanisms of transient
amplification in abstract and biological neural networks. PLoS Computational Biology.
18(8), e1010365.
mla: Christodoulou, Georgia, et al. “Regimes and Mechanisms of Transient Amplification
in Abstract and Biological Neural Networks.” PLoS Computational Biology,
vol. 18, no. 8, e1010365, Public Library of Science, 2022, doi:10.1371/journal.pcbi.1010365.
short: G. Christodoulou, T.P. Vogels, E.J. Agnes, PLoS Computational Biology 18
(2022).
date_created: 2022-09-11T22:01:56Z
date_published: 2022-08-15T00:00:00Z
date_updated: 2023-08-03T14:06:29Z
day: '15'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1371/journal.pcbi.1010365
external_id:
isi:
- '000937227700001'
file:
- access_level: open_access
checksum: 8a81ab29f837991ee0ea770817c4a50e
content_type: application/pdf
creator: dernst
date_created: 2022-09-12T07:47:55Z
date_updated: 2022-09-12T07:47:55Z
file_id: '12090'
file_name: 2022_PLoSCompBio_Christodoulou.pdf
file_size: 2867337
relation: main_file
success: 1
file_date_updated: 2022-09-12T07:47:55Z
has_accepted_license: '1'
intvolume: ' 18'
isi: 1
issue: '8'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Regimes and mechanisms of transient amplification in abstract and biological
neural networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 18
year: '2022'
...
---
_id: '12225'
abstract:
- lang: eng
text: In social networks, users often engage with like-minded peers. This selective
exposure to opinions might result in echo chambers, i.e., political fragmentation
and social polarization of user interactions. When echo chambers form, opinions
have a bimodal distribution with two peaks on opposite sides. In certain issues,
where either extreme positions contain a degree of misinformation, neutral consensus
is preferable for promoting discourse. In this paper, we use an opinion dynamics
model that naturally forms echo chambers in order to find a feedback mechanism
that bridges these communities and leads to a neutral consensus. We introduce the
random dynamical nudge (RDN), which presents each agent
with input from a random selection of other agents’ opinions and does not require
surveillance of every person’s opinions. Our computational results in two different
models suggest that the RDN leads to a unimodal distribution of opinions centered
around the neutral consensus. Furthermore, the RDN is effective both for preventing
the formation of echo chambers and also for depolarizing existing echo chambers.
Due to the simple and robust nature of the RDN, social media networks might be
able to implement a version of this self-feedback mechanism, when appropriate,
to prevent the segregation of online communities on complex social issues.
acknowledgement: CBC and AKN would like to thank Neuromatch Academy https://www.neuromatchacademy.org
for introducing the authors to each other. We thank Dr. Krešimir Josic (University
of Houston) , Fabian Baumann (Humboldt University) and Dr. Igor M. Sokolov (Humboldt
University) for carefully reading the early versions of the manuscript and providing
constructive feedback. CBC is supported by the German Deutscher Akademischer Austauschdienst
(DAAD, https://daad.de), the South African National Research Foundation (NRF, https://nrf.ac.za),
the University of Cape Town (UCT, https://uct.ac.za), and the NOMIS Foundation through
the NOMIS Fellowships at IST Austria program (https://nomisfoundation.ch). SVV appreciate
the generosity of Tecnológico de Monterrey for covering the publication fee.
article_number: '9234'
article_processing_charge: No
article_type: original
author:
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Sebastián Vallejo
full_name: Vera, Sebastián Vallejo
last_name: Vera
- first_name: Ali
full_name: Khaledi-Nasab, Ali
last_name: Khaledi-Nasab
citation:
ama: Currin C, Vera SV, Khaledi-Nasab A. Depolarization of echo chambers by random
dynamical nudge. Scientific Reports. 2022;12. doi:10.1038/s41598-022-12494-w
apa: Currin, C., Vera, S. V., & Khaledi-Nasab, A. (2022). Depolarization of
echo chambers by random dynamical nudge. Scientific Reports. Springer Nature.
https://doi.org/10.1038/s41598-022-12494-w
chicago: Currin, Christopher, Sebastián Vallejo Vera, and Ali Khaledi-Nasab. “Depolarization
of Echo Chambers by Random Dynamical Nudge.” Scientific Reports. Springer
Nature, 2022. https://doi.org/10.1038/s41598-022-12494-w.
ieee: C. Currin, S. V. Vera, and A. Khaledi-Nasab, “Depolarization of echo chambers
by random dynamical nudge,” Scientific Reports, vol. 12. Springer Nature,
2022.
ista: Currin C, Vera SV, Khaledi-Nasab A. 2022. Depolarization of echo chambers
by random dynamical nudge. Scientific Reports. 12, 9234.
mla: Currin, Christopher, et al. “Depolarization of Echo Chambers by Random Dynamical
Nudge.” Scientific Reports, vol. 12, 9234, Springer Nature, 2022, doi:10.1038/s41598-022-12494-w.
short: C. Currin, S.V. Vera, A. Khaledi-Nasab, Scientific Reports 12 (2022).
date_created: 2023-01-16T09:48:30Z
date_published: 2022-06-02T00:00:00Z
date_updated: 2023-08-04T09:26:30Z
day: '02'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1038/s41598-022-12494-w
external_id:
isi:
- '000805561200024'
pmid:
- '35654942'
file:
- access_level: open_access
checksum: e024a75f14ce5667795a31e44a259c52
content_type: application/pdf
creator: dernst
date_created: 2023-01-27T08:56:18Z
date_updated: 2023-01-27T08:56:18Z
file_id: '12418'
file_name: 2022_ScientificReports_Currin.pdf
file_size: 3625627
relation: main_file
success: 1
file_date_updated: 2023-01-27T08:56:18Z
has_accepted_license: '1'
intvolume: ' 12'
isi: 1
keyword:
- Multidisciplinary
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
publication: Scientific Reports
publication_identifier:
issn:
- 2045-2322
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Depolarization of echo chambers by random dynamical nudge
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 12
year: '2022'
...
---
_id: '11453'
abstract:
- lang: eng
text: "Neuronal computations depend on synaptic connectivity and intrinsic electrophysiological
properties. Synaptic connectivity determines which inputs from presynaptic neurons
are integrated, while cellular properties determine how inputs are filtered over
time. Unlike their biological counterparts, most computational approaches to learning
in simulated neural networks are limited to changes in synaptic connectivity.
However, if intrinsic parameters change, neural computations are altered drastically.
Here, we include the parameters that determine the intrinsic properties,\r\ne.g.,
time constants and reset potential, into the learning paradigm. Using sparse feedback
signals that indicate target spike times, and gradient-based parameter updates,
we show that the intrinsic parameters can be learned along with the synaptic weights
to produce specific input-output functions. Specifically, we use a teacher-student
paradigm in which a randomly initialised leaky integrate-and-fire or resonate-and-fire
neuron must recover the parameters of a teacher neuron. We show that complex temporal
functions can be learned online and without backpropagation through time, relying
on event-based updates only. Our results are a step towards online learning of
neural computations from ungraded and unsigned sparse feedback signals with a
biologically inspired learning mechanism."
acknowledgement: We would like to thank Professor Dr. Henning Sprekeler for his valuable
suggestions and Dr. Andrew Saxe, Milan Klöwer and Anna Wallis for their constructive
feedback on the manuscript. Lukas Braun was supported by the Network of European
Neuroscience Schools through their NENS Exchange Grant program, by the European
Union through their European Community Action Scheme for the Mobility of University
Students, the Woodward Scholarship awarded by Wadham College, Oxford and the Medical
Research Council [MR/N013468/1]. Tim P. Vogels was supported by a Wellcome Trust
Senior Research Fellowship [214316/Z/18/Z].
article_processing_charge: No
author:
- first_name: Lukas
full_name: Braun, Lukas
last_name: Braun
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: 'Braun L, Vogels TP. Online learning of neural computations from sparse temporal
feedback. In: Advances in Neural Information Processing Systems - 35th Conference
on Neural Information Processing Systems. Vol 20. Neural Information Processing
Systems Foundation; 2021:16437-16450.'
apa: 'Braun, L., & Vogels, T. P. (2021). Online learning of neural computations
from sparse temporal feedback. In Advances in Neural Information Processing
Systems - 35th Conference on Neural Information Processing Systems (Vol. 20,
pp. 16437–16450). Virtual, Online: Neural Information Processing Systems Foundation.'
chicago: Braun, Lukas, and Tim P Vogels. “Online Learning of Neural Computations
from Sparse Temporal Feedback.” In Advances in Neural Information Processing
Systems - 35th Conference on Neural Information Processing Systems, 20:16437–50.
Neural Information Processing Systems Foundation, 2021.
ieee: L. Braun and T. P. Vogels, “Online learning of neural computations from sparse
temporal feedback,” in Advances in Neural Information Processing Systems -
35th Conference on Neural Information Processing Systems, Virtual, Online,
2021, vol. 20, pp. 16437–16450.
ista: 'Braun L, Vogels TP. 2021. Online learning of neural computations from sparse
temporal feedback. Advances in Neural Information Processing Systems - 35th Conference
on Neural Information Processing Systems. NeurIPS: Neural Information Processing
Systems vol. 20, 16437–16450.'
mla: Braun, Lukas, and Tim P. Vogels. “Online Learning of Neural Computations from
Sparse Temporal Feedback.” Advances in Neural Information Processing Systems
- 35th Conference on Neural Information Processing Systems, vol. 20, Neural
Information Processing Systems Foundation, 2021, pp. 16437–50.
short: L. Braun, T.P. Vogels, in:, Advances in Neural Information Processing Systems
- 35th Conference on Neural Information Processing Systems, Neural Information
Processing Systems Foundation, 2021, pp. 16437–16450.
conference:
end_date: 2021-12-14
location: Virtual, Online
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
date_created: 2022-06-19T22:01:59Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2022-06-20T07:12:58Z
day: '01'
department:
- _id: TiVo
intvolume: ' 20'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/file/88e1ce84f9feef5a08d0df0334c53468-Paper.pdf
month: '12'
oa: 1
oa_version: Published Version
page: 16437-16450
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Advances in Neural Information Processing Systems - 35th Conference on
Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Online learning of neural computations from sparse temporal feedback
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 20
year: '2021'
...
---
_id: '8253'
abstract:
- lang: eng
text: Brains process information in spiking neural networks. Their intricate connections
shape the diverse functions these networks perform. In comparison, the functional
capabilities of models of spiking networks are still rudimentary. This shortcoming
is mainly due to the lack of insight and practical algorithms to construct the
necessary connectivity. Any such algorithm typically attempts to build networks
by iteratively reducing the error compared to a desired output. But assigning
credit to hidden units in multi-layered spiking networks has remained challenging
due to the non-differentiable nonlinearity of spikes. To avoid this issue, one
can employ surrogate gradients to discover the required connectivity in spiking
network models. However, the choice of a surrogate is not unique, raising the
question of how its implementation influences the effectiveness of the method.
Here, we use numerical simulations to systematically study how essential design
parameters of surrogate gradients impact learning performance on a range of classification
problems. We show that surrogate gradient learning is robust to different shapes
of underlying surrogate derivatives, but the choice of the derivative’s scale
can substantially affect learning performance. When we combine surrogate gradients
with a suitable activity regularization technique, robust information processing
can be achieved in spiking networks even at the sparse activity limit. Our study
provides a systematic account of the remarkable robustness of surrogate gradient
learning and serves as a practical guide to model functional spiking neural networks.
acknowledgement: F.Z. was supported by the Wellcome Trust (110124/Z/15/Z) and the
Novartis Research Foundation. T.P.V. was supported by a Wellcome Trust Sir Henry
Dale Research fellowship (WT100000), a Wellcome Trust Senior Research Fellowship
(214316/Z/18/Z), and an ERC Consolidator Grant SYNAPSEEK.
article_processing_charge: No
article_type: original
author:
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
orcid: 0000-0003-1883-644X
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Zenke F, Vogels TP. The remarkable robustness of surrogate gradient learning
for instilling complex function in spiking neural networks. Neural Computation.
2021;33(4):899-925. doi:10.1162/neco_a_01367
apa: Zenke, F., & Vogels, T. P. (2021). The remarkable robustness of surrogate
gradient learning for instilling complex function in spiking neural networks.
Neural Computation. MIT Press. https://doi.org/10.1162/neco_a_01367
chicago: Zenke, Friedemann, and Tim P Vogels. “The Remarkable Robustness of Surrogate
Gradient Learning for Instilling Complex Function in Spiking Neural Networks.”
Neural Computation. MIT Press, 2021. https://doi.org/10.1162/neco_a_01367.
ieee: F. Zenke and T. P. Vogels, “The remarkable robustness of surrogate gradient
learning for instilling complex function in spiking neural networks,” Neural
Computation, vol. 33, no. 4. MIT Press, pp. 899–925, 2021.
ista: Zenke F, Vogels TP. 2021. The remarkable robustness of surrogate gradient
learning for instilling complex function in spiking neural networks. Neural Computation.
33(4), 899–925.
mla: Zenke, Friedemann, and Tim P. Vogels. “The Remarkable Robustness of Surrogate
Gradient Learning for Instilling Complex Function in Spiking Neural Networks.”
Neural Computation, vol. 33, no. 4, MIT Press, 2021, pp. 899–925, doi:10.1162/neco_a_01367.
short: F. Zenke, T.P. Vogels, Neural Computation 33 (2021) 899–925.
date_created: 2020-08-12T12:08:24Z
date_published: 2021-03-01T00:00:00Z
date_updated: 2023-08-04T10:53:14Z
day: '01'
ddc:
- '000'
- '570'
department:
- _id: TiVo
doi: 10.1162/neco_a_01367
ec_funded: 1
external_id:
isi:
- '000663433900003'
pmid:
- '33513328'
file:
- access_level: open_access
checksum: eac5a51c24c8989ae7cf9ae32ec3bc95
content_type: application/pdf
creator: dernst
date_created: 2022-04-08T06:05:39Z
date_updated: 2022-04-08T06:05:39Z
file_id: '11131'
file_name: 2021_NeuralComputation_Zenke.pdf
file_size: 1611614
relation: main_file
success: 1
file_date_updated: 2022-04-08T06:05:39Z
has_accepted_license: '1'
intvolume: ' 33'
isi: 1
issue: '4'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 899-925
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Neural Computation
publication_identifier:
eissn:
- 1530-888X
issn:
- 0899-7667
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: The remarkable robustness of surrogate gradient learning for instilling complex
function in spiking neural networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 33
year: '2021'
...
---
_id: '8757'
abstract:
- lang: eng
text: Traditional scientific conferences and seminar events have been hugely disrupted
by the COVID-19 pandemic, paving the way for virtual forms of scientific communication
to take hold and be put to the test.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Panagiotis
full_name: Bozelos, Panagiotis
id: 52e9c652-2982-11eb-81d4-b43d94c63700
last_name: Bozelos
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Bozelos P, Vogels TP. Talking science, online. Nature Reviews Neuroscience.
2021;22(1):1-2. doi:10.1038/s41583-020-00408-6
apa: Bozelos, P., & Vogels, T. P. (2021). Talking science, online. Nature
Reviews Neuroscience. Springer Nature. https://doi.org/10.1038/s41583-020-00408-6
chicago: Bozelos, Panagiotis, and Tim P Vogels. “Talking Science, Online.” Nature
Reviews Neuroscience. Springer Nature, 2021. https://doi.org/10.1038/s41583-020-00408-6.
ieee: P. Bozelos and T. P. Vogels, “Talking science, online,” Nature Reviews
Neuroscience, vol. 22, no. 1. Springer Nature, pp. 1–2, 2021.
ista: Bozelos P, Vogels TP. 2021. Talking science, online. Nature Reviews Neuroscience.
22(1), 1–2.
mla: Bozelos, Panagiotis, and Tim P. Vogels. “Talking Science, Online.” Nature
Reviews Neuroscience, vol. 22, no. 1, Springer Nature, 2021, pp. 1–2, doi:10.1038/s41583-020-00408-6.
short: P. Bozelos, T.P. Vogels, Nature Reviews Neuroscience 22 (2021) 1–2.
date_created: 2020-11-15T23:01:18Z
date_published: 2021-01-01T00:00:00Z
date_updated: 2023-08-04T11:10:20Z
day: '01'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1038/s41583-020-00408-6
external_id:
isi:
- '000588256300001'
pmid:
- '33173190'
file:
- access_level: open_access
checksum: 7985d7dff94c086e35b94a911d78d9ad
content_type: application/pdf
creator: dernst
date_created: 2021-02-04T10:34:22Z
date_updated: 2021-02-04T10:34:22Z
file_id: '9088'
file_name: 2021_NatureNeuroScience_Bozelos.pdf
file_size: 683634
relation: main_file
success: 1
file_date_updated: 2021-02-04T10:34:22Z
has_accepted_license: '1'
intvolume: ' 22'
isi: 1
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 1-2
pmid: 1
publication: Nature Reviews Neuroscience
publication_identifier:
eissn:
- '14710048'
issn:
- 1471003X
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Talking science, online
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 22
year: '2021'
...
---
_id: '9228'
abstract:
- lang: eng
text: Legacy conferences are costly and time consuming, and exclude scientists lacking
various resources or abilities. During the 2020 pandemic, we created an online
conference platform, Neuromatch Conferences (NMC), aimed at developing technological
and cultural changes to make conferences more democratic, scalable, and accessible.
We discuss the lessons we learned.
acknowledgement: We thank all of our volunteers from the NMC conferences (list of
names in the appendix). We also thank the NSF for support from 1734220 to B.W.,
and DARPA for support to T.A.
article_processing_charge: No
article_type: original
author:
- first_name: Titipat
full_name: Achakulvisut, Titipat
last_name: Achakulvisut
- first_name: Tulakan
full_name: Ruangrong, Tulakan
last_name: Ruangrong
- first_name: Patrick
full_name: Mineault, Patrick
last_name: Mineault
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Megan A.K.
full_name: Peters, Megan A.K.
last_name: Peters
- first_name: Panayiota
full_name: Poirazi, Panayiota
last_name: Poirazi
- first_name: Christopher
full_name: Rozell, Christopher
last_name: Rozell
- first_name: Brad
full_name: Wyble, Brad
last_name: Wyble
- first_name: Dan F.M.
full_name: Goodman, Dan F.M.
last_name: Goodman
- first_name: Konrad Paul
full_name: Kording, Konrad Paul
last_name: Kording
citation:
ama: 'Achakulvisut T, Ruangrong T, Mineault P, et al. Towards democratizing and
automating online conferences: Lessons from the Neuromatch Conferences. Trends
in Cognitive Sciences. 2021;25(4):265-268. doi:10.1016/j.tics.2021.01.007'
apa: 'Achakulvisut, T., Ruangrong, T., Mineault, P., Vogels, T. P., Peters, M. A.
K., Poirazi, P., … Kording, K. P. (2021). Towards democratizing and automating
online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive
Sciences. Elsevier. https://doi.org/10.1016/j.tics.2021.01.007'
chicago: 'Achakulvisut, Titipat, Tulakan Ruangrong, Patrick Mineault, Tim P Vogels,
Megan A.K. Peters, Panayiota Poirazi, Christopher Rozell, Brad Wyble, Dan F.M.
Goodman, and Konrad Paul Kording. “Towards Democratizing and Automating Online
Conferences: Lessons from the Neuromatch Conferences.” Trends in Cognitive
Sciences. Elsevier, 2021. https://doi.org/10.1016/j.tics.2021.01.007.'
ieee: 'T. Achakulvisut et al., “Towards democratizing and automating online
conferences: Lessons from the Neuromatch Conferences,” Trends in Cognitive
Sciences, vol. 25, no. 4. Elsevier, pp. 265–268, 2021.'
ista: 'Achakulvisut T, Ruangrong T, Mineault P, Vogels TP, Peters MAK, Poirazi P,
Rozell C, Wyble B, Goodman DFM, Kording KP. 2021. Towards democratizing and automating
online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive
Sciences. 25(4), 265–268.'
mla: 'Achakulvisut, Titipat, et al. “Towards Democratizing and Automating Online
Conferences: Lessons from the Neuromatch Conferences.” Trends in Cognitive
Sciences, vol. 25, no. 4, Elsevier, 2021, pp. 265–68, doi:10.1016/j.tics.2021.01.007.'
short: T. Achakulvisut, T. Ruangrong, P. Mineault, T.P. Vogels, M.A.K. Peters, P.
Poirazi, C. Rozell, B. Wyble, D.F.M. Goodman, K.P. Kording, Trends in Cognitive
Sciences 25 (2021) 265–268.
date_created: 2021-03-07T23:01:25Z
date_published: 2021-04-01T00:00:00Z
date_updated: 2023-08-07T13:59:07Z
day: '01'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1016/j.tics.2021.01.007
external_id:
isi:
- '000627418000001'
pmid:
- '33608214'
file:
- access_level: open_access
checksum: 87e39ea7bd266b976e8631b66979214d
content_type: application/pdf
creator: dernst
date_created: 2022-05-27T07:31:24Z
date_updated: 2022-05-27T07:31:24Z
file_id: '11415'
file_name: 2021_TrendsCognitiveSciences_Achakulvisut.pdf
file_size: 380720
relation: main_file
success: 1
file_date_updated: 2022-05-27T07:31:24Z
has_accepted_license: '1'
intvolume: ' 25'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Submitted Version
page: 265-268
pmid: 1
publication: Trends in Cognitive Sciences
publication_identifier:
eissn:
- 1879-307X
issn:
- 1364-6613
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Towards democratizing and automating online conferences: Lessons from the
Neuromatch Conferences'
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 25
year: '2021'
...
---
_id: '8126'
abstract:
- lang: eng
text: Cortical areas comprise multiple types of inhibitory interneurons with stereotypical
connectivity motifs, but their combined effect on postsynaptic dynamics has been
largely unexplored. Here, we analyse the response of a single postsynaptic model
neuron receiving tuned excitatory connections alongside inhibition from two plastic
populations. Depending on the inhibitory plasticity rule, synapses remain unspecific
(flat), become anti-correlated to, or mirror excitatory synapses. Crucially, the
neuron’s receptive field, i.e., its response to presynaptic stimuli, depends on
the modulatory state of inhibition. When both inhibitory populations are active,
inhibition balances excitation, resulting in uncorrelated postsynaptic responses
regardless of the inhibitory tuning profiles. Modulating the activity of a given
inhibitory population produces strong correlations to either preferred or non-preferred
inputs, in line with recent experimental findings showing dramatic context-dependent
changes of neurons’ receptive fields. We thus confirm that a neuron’s receptive
field doesn’t follow directly from the weight profiles of its presynaptic afferents.
article_processing_charge: No
article_type: original
author:
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
orcid: 0000-0001-7184-7311
- first_name: Andrea I.
full_name: Luppi, Andrea I.
last_name: Luppi
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Agnes EJ, Luppi AI, Vogels TP. Complementary inhibitory weight profiles emerge
from plasticity and allow attentional switching of receptive fields. The Journal
of Neuroscience. 2020;40(50):9634-9649. doi:10.1523/JNEUROSCI.0276-20.2020
apa: Agnes, E. J., Luppi, A. I., & Vogels, T. P. (2020). Complementary inhibitory
weight profiles emerge from plasticity and allow attentional switching of receptive
fields. The Journal of Neuroscience. Society for Neuroscience. https://doi.org/10.1523/JNEUROSCI.0276-20.2020
chicago: Agnes, Everton J., Andrea I. Luppi, and Tim P Vogels. “Complementary Inhibitory
Weight Profiles Emerge from Plasticity and Allow Attentional Switching of Receptive
Fields.” The Journal of Neuroscience. Society for Neuroscience, 2020. https://doi.org/10.1523/JNEUROSCI.0276-20.2020.
ieee: E. J. Agnes, A. I. Luppi, and T. P. Vogels, “Complementary inhibitory weight
profiles emerge from plasticity and allow attentional switching of receptive fields,”
The Journal of Neuroscience, vol. 40, no. 50. Society for Neuroscience,
pp. 9634–9649, 2020.
ista: Agnes EJ, Luppi AI, Vogels TP. 2020. Complementary inhibitory weight profiles
emerge from plasticity and allow attentional switching of receptive fields. The
Journal of Neuroscience. 40(50), 9634–9649.
mla: Agnes, Everton J., et al. “Complementary Inhibitory Weight Profiles Emerge
from Plasticity and Allow Attentional Switching of Receptive Fields.” The Journal
of Neuroscience, vol. 40, no. 50, Society for Neuroscience, 2020, pp. 9634–49,
doi:10.1523/JNEUROSCI.0276-20.2020.
short: E.J. Agnes, A.I. Luppi, T.P. Vogels, The Journal of Neuroscience 40 (2020)
9634–9649.
date_created: 2020-07-16T12:25:04Z
date_published: 2020-12-09T00:00:00Z
date_updated: 2023-08-22T07:54:26Z
day: '09'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1523/JNEUROSCI.0276-20.2020
external_id:
isi:
- '000606706400009'
pmid:
- '33168622'
file:
- access_level: open_access
checksum: 7977e4dd6b89357d1a5cc88babac56da
content_type: application/pdf
creator: dernst
date_created: 2020-12-28T08:31:47Z
date_updated: 2020-12-28T08:31:47Z
file_id: '8977'
file_name: 2020_JourNeuroscience_Agnes.pdf
file_size: 2750920
relation: main_file
success: 1
file_date_updated: 2020-12-28T08:31:47Z
has_accepted_license: '1'
intvolume: ' 40'
isi: 1
issue: '50'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 9634-9649
pmid: 1
publication: The Journal of Neuroscience
publication_identifier:
eissn:
- 1529-2401
publication_status: published
publisher: Society for Neuroscience
quality_controlled: '1'
scopus_import: '1'
status: public
title: Complementary inhibitory weight profiles emerge from plasticity and allow attentional
switching of receptive fields
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 40
year: '2020'
...
---
_id: '8127'
abstract:
- lang: eng
text: Mechanistic modeling in neuroscience aims to explain observed phenomena in
terms of underlying causes. However, determining which model parameters agree
with complex and stochastic neural data presents a significant challenge. We address
this challenge with a machine learning tool which uses deep neural density estimators—trained
using model simulations—to carry out Bayesian inference and retrieve the full
space of parameters compatible with raw data or selected data features. Our method
is scalable in parameters and data features and can rapidly analyze new data after
initial training. We demonstrate the power and flexibility of our approach on
receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize
the space of circuit configurations giving rise to rhythmic activity in the crustacean
stomatogastric ganglion, and use these results to derive hypotheses for underlying
compensation mechanisms. Our approach will help close the gap between data-driven
and theory-driven models of neural dynamics.
acknowledgement: We thank Mahmood S Hoseini and Michael Stryker for sharing their
data for Figure 2, and Philipp Berens, Sean Bittner, Jan Boelts, John Cunningham,
Richard Gao, Scott Linderman, Eve Marder, Iain Murray, George Papamakarios, Astrid
Prinz, Auguste Schulz and Srinivas Turaga for discussions and/or comments on the
manuscript. This work was supported by the German Research Foundation (DFG) through
SFB 1233 ‘Robust Vision’, (276693517), SFB 1089 ‘Synaptic Microcircuits’, SPP 2041
‘Computational Connectomics’ and Germany's Excellence Strategy – EXC-Number 2064/1
– Project number 390727645 and the German Federal Ministry of Education and Research
(BMBF, project ‘ADIMEM’, FKZ 01IS18052 A-D) to JHM, a Sir Henry Dale Fellowship
by the Wellcome Trust and the Royal Society (WT100000; WFP and TPV), a Wellcome
Trust Senior Research Fellowship (214316/Z/18/Z; TPV), a ERC Consolidator Grant
(SYNAPSEEK; WPF and CC), and a UK Research and Innovation, Biotechnology and Biological
Sciences Research Council (CC, UKRI-BBSRC BB/N019512/1). We gratefully acknowledge
the Leibniz Supercomputing Centre for funding this project by providing computing
time on its Linux-Cluster.
article_number: e56261
article_processing_charge: No
article_type: original
author:
- first_name: Pedro J.
full_name: Gonçalves, Pedro J.
last_name: Gonçalves
orcid: 0000-0002-6987-4836
- first_name: Jan-Matthis
full_name: Lueckmann, Jan-Matthis
last_name: Lueckmann
orcid: 0000-0003-4320-4663
- first_name: Michael
full_name: Deistler, Michael
last_name: Deistler
orcid: 0000-0002-3573-0404
- first_name: Marcel
full_name: Nonnenmacher, Marcel
last_name: Nonnenmacher
orcid: 0000-0001-6044-6627
- first_name: Kaan
full_name: Öcal, Kaan
last_name: Öcal
orcid: 0000-0002-8528-6858
- first_name: Giacomo
full_name: Bassetto, Giacomo
last_name: Bassetto
- first_name: Chaitanya
full_name: Chintaluri, Chaitanya
id: BA06AFEE-A4BA-11EA-AE5C-14673DDC885E
last_name: Chintaluri
orcid: 0000-0003-4252-1608
- first_name: William F.
full_name: Podlaski, William F.
last_name: Podlaski
orcid: 0000-0001-6619-7502
- first_name: Sara A.
full_name: Haddad, Sara A.
last_name: Haddad
orcid: 0000-0003-0807-0823
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: David S.
full_name: Greenberg, David S.
last_name: Greenberg
- first_name: Jakob H.
full_name: Macke, Jakob H.
last_name: Macke
orcid: 0000-0001-5154-8912
citation:
ama: Gonçalves PJ, Lueckmann J-M, Deistler M, et al. Training deep neural density
estimators to identify mechanistic models of neural dynamics. eLife. 2020;9.
doi:10.7554/eLife.56261
apa: Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K.,
Bassetto, G., … Macke, J. H. (2020). Training deep neural density estimators to
identify mechanistic models of neural dynamics. ELife. eLife Sciences Publications.
https://doi.org/10.7554/eLife.56261
chicago: Gonçalves, Pedro J., Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher,
Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, et al. “Training Deep Neural
Density Estimators to Identify Mechanistic Models of Neural Dynamics.” ELife.
eLife Sciences Publications, 2020. https://doi.org/10.7554/eLife.56261.
ieee: P. J. Gonçalves et al., “Training deep neural density estimators to
identify mechanistic models of neural dynamics,” eLife, vol. 9. eLife Sciences
Publications, 2020.
ista: Gonçalves PJ, Lueckmann J-M, Deistler M, Nonnenmacher M, Öcal K, Bassetto
G, Chintaluri C, Podlaski WF, Haddad SA, Vogels TP, Greenberg DS, Macke JH. 2020.
Training deep neural density estimators to identify mechanistic models of neural
dynamics. eLife. 9, e56261.
mla: Gonçalves, Pedro J., et al. “Training Deep Neural Density Estimators to Identify
Mechanistic Models of Neural Dynamics.” ELife, vol. 9, e56261, eLife Sciences
Publications, 2020, doi:10.7554/eLife.56261.
short: P.J. Gonçalves, J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G.
Bassetto, C. Chintaluri, W.F. Podlaski, S.A. Haddad, T.P. Vogels, D.S. Greenberg,
J.H. Macke, ELife 9 (2020).
date_created: 2020-07-16T12:26:04Z
date_published: 2020-09-17T00:00:00Z
date_updated: 2023-08-22T07:54:52Z
day: '17'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.7554/eLife.56261
ec_funded: 1
external_id:
isi:
- '000584989400001'
pmid:
- '32940606'
file:
- access_level: open_access
checksum: c4300ddcd93ed03fc9c6cdf1f77890be
content_type: application/pdf
creator: cziletti
date_created: 2020-10-27T11:37:32Z
date_updated: 2020-10-27T11:37:32Z
file_id: '8709'
file_name: 2020_eLife_Gonçalves.pdf
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name: Learning the shape of synaptic plasticity rules for neuronal architectures
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title: Training deep neural density estimators to identify mechanistic models of neural
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