---
_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: '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
license: https://creativecommons.org/licenses/by/4.0/
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: '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: '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
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file_id: '11131'
file_name: 2021_NeuralComputation_Zenke.pdf
file_size: 1611614
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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
file_size: 17355867
relation: main_file
success: 1
file_date_updated: 2020-10-27T11:37:32Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
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: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Training deep neural density estimators to identify mechanistic models of neural
dynamics
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: 9
year: '2020'
...
---
_id: '9633'
abstract:
- lang: eng
text: The search for biologically faithful synaptic plasticity rules has resulted
in a large body of models. They are usually inspired by – and fitted to – experimental
data, but they rarely produce neural dynamics that serve complex functions. These
failures suggest that current plasticity models are still under-constrained by
existing data. Here, we present an alternative approach that uses meta-learning
to discover plausible synaptic plasticity rules. Instead of experimental data,
the rules are constrained by the functions they implement and the structure they
are meant to produce. Briefly, we parameterize synaptic plasticity rules by a
Volterra expansion and then use supervised learning methods (gradient descent
or evolutionary strategies) to minimize a problem-dependent loss function that
quantifies how effectively a candidate plasticity rule transforms an initially
random network into one with the desired function. We first validate our approach
by re-discovering previously described plasticity rules, starting at the single-neuron
level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction
of most variability of inputs to a neuron (i.e., the first principal component).
We expand the problem to the network level and ask the framework to find Oja’s
rule together with an anti-Hebbian rule such that an initially random two-layer
firing-rate network will recover several principal components of the input space
after learning. Next, we move to networks of integrate-and-fire neurons with plastic
inhibitory afferents. We train for rules that achieve a target firing rate by
countering tuned excitation. Our algorithm discovers a specific subset of the
manifold of rules that can solve this task. Our work is a proof of principle of
an automated and unbiased approach to unveil synaptic plasticity rules that obey
biological constraints and can solve complex functions.
acknowledgement: We would like to thank Chaitanya Chintaluri, Georgia Christodoulou,
Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work
was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z),
a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme
Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.
article_processing_charge: No
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
- first_name: Timothy
full_name: Lillicrap, Timothy
last_name: Lillicrap
- 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, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning
approach to (re)discover plasticity rules that carve a desired function into a
neural network. In: Advances in Neural Information Processing Systems.
Vol 33. ; 2020:16398-16408.'
apa: Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., & Vogels, T.
P. (2020). A meta-learning approach to (re)discover plasticity rules that carve
a desired function into a neural network. In Advances in Neural Information
Processing Systems (Vol. 33, pp. 16398–16408). Vancouver, Canada.
chicago: Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap,
and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That
Carve a Desired Function into a Neural Network.” In Advances in Neural Information
Processing Systems, 33:16398–408, 2020.
ieee: B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A
meta-learning approach to (re)discover plasticity rules that carve a desired function
into a neural network,” in Advances in Neural Information Processing Systems,
Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.
ista: 'Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning
approach to (re)discover plasticity rules that carve a desired function into a
neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference
on Neural Information Processing Systems vol. 33, 16398–16408.'
mla: Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity
Rules That Carve a Desired Function into a Neural Network.” Advances in Neural
Information Processing Systems, vol. 33, 2020, pp. 16398–408.
short: B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances
in Neural Information Processing Systems, 2020, pp. 16398–16408.
conference:
end_date: 2020-12-12
location: Vancouver, Canada
name: 'NeurIPS: Conference on Neural Information Processing Systems'
start_date: 2020-12-06
date_created: 2021-07-04T22:01:27Z
date_published: 2020-12-06T00:00:00Z
date_updated: 2023-10-18T09:20:55Z
day: '06'
department:
- _id: TiVo
ec_funded: 1
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
page: 16398-16408
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: Advances in Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
quality_controlled: '1'
related_material:
link:
- relation: is_continued_by
url: https://doi.org/10.1101/2020.10.24.353409
record:
- id: '14422'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: A meta-learning approach to (re)discover plasticity rules that carve a desired
function into a neural network
type: conference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 33
year: '2020'
...
---
_id: '8013'
article_number: e1007049
article_processing_charge: No
article_type: original
author:
- first_name: Christopher B.
full_name: Currin, Christopher B.
last_name: Currin
- first_name: Phumlani N.
full_name: Khoza, Phumlani N.
last_name: Khoza
- first_name: Alexander D.
full_name: Antrobus, Alexander D.
last_name: Antrobus
- first_name: Peter E.
full_name: Latham, Peter E.
last_name: Latham
- 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: Joseph V.
full_name: Raimondo, Joseph V.
last_name: Raimondo
citation:
ama: 'Currin CB, Khoza PN, Antrobus AD, Latham PE, Vogels TP, Raimondo JV. Think:
Theory for Africa. PLOS Computational Biology. 2019;15(7). doi:10.1371/journal.pcbi.1007049'
apa: 'Currin, C. B., Khoza, P. N., Antrobus, A. D., Latham, P. E., Vogels, T. P.,
& Raimondo, J. V. (2019). Think: Theory for Africa. PLOS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1007049'
chicago: 'Currin, Christopher B., Phumlani N. Khoza, Alexander D. Antrobus, Peter
E. Latham, Tim P Vogels, and Joseph V. Raimondo. “Think: Theory for Africa.” PLOS
Computational Biology. Public Library of Science, 2019. https://doi.org/10.1371/journal.pcbi.1007049.'
ieee: 'C. B. Currin, P. N. Khoza, A. D. Antrobus, P. E. Latham, T. P. Vogels, and
J. V. Raimondo, “Think: Theory for Africa,” PLOS Computational Biology,
vol. 15, no. 7. Public Library of Science, 2019.'
ista: 'Currin CB, Khoza PN, Antrobus AD, Latham PE, Vogels TP, Raimondo JV. 2019.
Think: Theory for Africa. PLOS Computational Biology. 15(7), e1007049.'
mla: 'Currin, Christopher B., et al. “Think: Theory for Africa.” PLOS Computational
Biology, vol. 15, no. 7, e1007049, Public Library of Science, 2019, doi:10.1371/journal.pcbi.1007049.'
short: C.B. Currin, P.N. Khoza, A.D. Antrobus, P.E. Latham, T.P. Vogels, J.V. Raimondo,
PLOS Computational Biology 15 (2019).
date_created: 2020-06-25T12:50:39Z
date_published: 2019-07-11T00:00:00Z
date_updated: 2021-01-12T08:16:31Z
day: '11'
ddc:
- '570'
doi: 10.1371/journal.pcbi.1007049
extern: '1'
external_id:
pmid:
- '31295253'
file:
- access_level: open_access
checksum: 723bdfb6ee5c747cbbb32baf01d17fad
content_type: application/pdf
creator: cziletti
date_created: 2020-07-02T12:22:57Z
date_updated: 2020-07-14T12:48:08Z
file_id: '8079'
file_name: 2019_PlosCompBio_Currin.pdf
file_size: 773969
relation: main_file
file_date_updated: 2020-07-14T12:48:08Z
has_accepted_license: '1'
intvolume: ' 15'
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLOS Computational Biology
publication_identifier:
issn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
status: public
title: 'Think: Theory for Africa'
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: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 15
year: '2019'
...
---
_id: '8014'
abstract:
- lang: eng
text: 'Working memory, the ability to keep recently accessed information available
for immediate manipulation, has been proposed to rely on two mechanisms that appear
difficult to reconcile: self-sustained neural firing, or the opposite—activity-silent
synaptic traces. Here we review and contrast models of these two mechanisms, and
then show that both phenomena can co-exist within a unified system in which neurons
hold information in both activity and synapses. Rapid plasticity in flexibly-coding
neurons allows features to be bound together into objects, with an important emergent
property being the focus of attention. One memory item is held by persistent activity
in an attended or “focused” state, and is thus remembered better than other items.
Other, previously attended items can remain in memory but in the background, encoded
in activity-silent synaptic traces. This dual functional architecture provides
a unified common mechanism accounting for a diversity of perplexing attention
and memory effects that have been hitherto difficult to explain in a single theoretical
framework.'
article_processing_charge: No
article_type: original
author:
- first_name: Sanjay G.
full_name: Manohar, Sanjay G.
last_name: Manohar
- first_name: Nahid
full_name: Zokaei, Nahid
last_name: Zokaei
- first_name: Sean J.
full_name: Fallon, Sean J.
last_name: Fallon
- 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: Masud
full_name: Husain, Masud
last_name: Husain
citation:
ama: Manohar SG, Zokaei N, Fallon SJ, Vogels TP, Husain M. Neural mechanisms of
attending to items in working memory. Neuroscience and Biobehavioral Reviews.
2019;101:1-12. doi:10.1016/j.neubiorev.2019.03.017
apa: Manohar, S. G., Zokaei, N., Fallon, S. J., Vogels, T. P., & Husain, M.
(2019). Neural mechanisms of attending to items in working memory. Neuroscience
and Biobehavioral Reviews. Elsevier . https://doi.org/10.1016/j.neubiorev.2019.03.017
chicago: Manohar, Sanjay G., Nahid Zokaei, Sean J. Fallon, Tim P Vogels, and Masud
Husain. “Neural Mechanisms of Attending to Items in Working Memory.” Neuroscience
and Biobehavioral Reviews. Elsevier , 2019. https://doi.org/10.1016/j.neubiorev.2019.03.017.
ieee: S. G. Manohar, N. Zokaei, S. J. Fallon, T. P. Vogels, and M. Husain, “Neural
mechanisms of attending to items in working memory,” Neuroscience and Biobehavioral
Reviews, vol. 101. Elsevier , pp. 1–12, 2019.
ista: Manohar SG, Zokaei N, Fallon SJ, Vogels TP, Husain M. 2019. Neural mechanisms
of attending to items in working memory. Neuroscience and Biobehavioral Reviews.
101, 1–12.
mla: Manohar, Sanjay G., et al. “Neural Mechanisms of Attending to Items in Working
Memory.” Neuroscience and Biobehavioral Reviews, vol. 101, Elsevier , 2019,
pp. 1–12, doi:10.1016/j.neubiorev.2019.03.017.
short: S.G. Manohar, N. Zokaei, S.J. Fallon, T.P. Vogels, M. Husain, Neuroscience
and Biobehavioral Reviews 101 (2019) 1–12.
date_created: 2020-06-25T12:52:13Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2021-01-12T08:16:31Z
day: '01'
ddc:
- '570'
doi: 10.1016/j.neubiorev.2019.03.017
extern: '1'
external_id:
pmid:
- '30922977'
file:
- access_level: open_access
checksum: 7b972e3d6f7bb3122c8c5648f44e60ca
content_type: application/pdf
creator: cziletti
date_created: 2020-07-02T13:17:52Z
date_updated: 2020-07-14T12:48:08Z
file_id: '8080'
file_name: 2019_NeurosBiobehavRev_Manohar.pdf
file_size: 1754418
relation: main_file
file_date_updated: 2020-07-14T12:48:08Z
has_accepted_license: '1'
intvolume: ' 101'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.1101/233007 '
month: '06'
oa: 1
oa_version: Published Version
page: 1-12
pmid: 1
publication: Neuroscience and Biobehavioral Reviews
publication_identifier:
issn:
- 0149-7634
publication_status: published
publisher: 'Elsevier '
quality_controlled: '1'
status: public
title: Neural mechanisms of attending to items in working memory
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: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 101
year: '2019'
...
---
_id: '8015'
abstract:
- lang: eng
text: 'The neural code of cortical processing remains uncracked; however, it must
necessarily rely on faithful signal propagation between cortical areas. In this
issue of Neuron, Joglekar et al. (2018) show that strong inter-areal excitation
balanced by local inhibition can enable reliable signal propagation in data-constrained
network models of macaque cortex. '
article_processing_charge: No
article_type: original
author:
- first_name: Jake P.
full_name: Stroud, Jake P.
last_name: Stroud
- 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: 'Stroud JP, Vogels TP. Cortical signal propagation: Balance, amplify, transmit.
Neuron. 2018;98(1):8-9. doi:10.1016/j.neuron.2018.03.028'
apa: 'Stroud, J. P., & Vogels, T. P. (2018). Cortical signal propagation: Balance,
amplify, transmit. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2018.03.028'
chicago: 'Stroud, Jake P., and Tim P Vogels. “Cortical Signal Propagation: Balance,
Amplify, Transmit.” Neuron. Elsevier, 2018. https://doi.org/10.1016/j.neuron.2018.03.028.'
ieee: 'J. P. Stroud and T. P. Vogels, “Cortical signal propagation: Balance, amplify,
transmit,” Neuron, vol. 98, no. 1. Elsevier, pp. 8–9, 2018.'
ista: 'Stroud JP, Vogels TP. 2018. Cortical signal propagation: Balance, amplify,
transmit. Neuron. 98(1), 8–9.'
mla: 'Stroud, Jake P., and Tim P. Vogels. “Cortical Signal Propagation: Balance,
Amplify, Transmit.” Neuron, vol. 98, no. 1, Elsevier, 2018, pp. 8–9, doi:10.1016/j.neuron.2018.03.028.'
short: J.P. Stroud, T.P. Vogels, Neuron 98 (2018) 8–9.
date_created: 2020-06-25T12:53:39Z
date_published: 2018-04-04T00:00:00Z
date_updated: 2021-01-12T08:16:31Z
day: '04'
doi: 10.1016/j.neuron.2018.03.028
extern: '1'
external_id:
pmid:
- '29621492'
intvolume: ' 98'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.neuron.2018.03.028
month: '04'
oa: 1
oa_version: Published Version
page: 8-9
pmid: 1
publication: Neuron
publication_identifier:
issn:
- 0896-6273
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: 'Cortical signal propagation: Balance, amplify, transmit'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 98
year: '2018'
...
---
_id: '8073'
abstract:
- lang: eng
text: Motor cortex (M1) exhibits a rich repertoire of neuronal activities to support
the generation of complex movements. Although recent neuronal-network models capture
many qualitative aspects of M1 dynamics, they can generate only a few distinct
movements. Additionally, it is unclear how M1 efficiently controls movements over
a wide range of shapes and speeds. We demonstrate that modulation of neuronal
input–output gains in recurrent neuronal-network models with a fixed architecture
can dramatically reorganize neuronal activity and thus downstream muscle outputs.
Consistent with the observation of diffuse neuromodulatory projections to M1,
a relatively small number of modulatory control units provide sufficient flexibility
to adjust high-dimensional network activity using a simple reward-based learning
rule. Furthermore, it is possible to assemble novel movements from previously
learned primitives, and one can separately change movement speed while preserving
movement shape. Our results provide a new perspective on the role of modulatory
systems in controlling recurrent cortical activity.
article_processing_charge: No
article_type: original
author:
- first_name: Jake P.
full_name: Stroud, Jake P.
last_name: Stroud
- first_name: Mason A.
full_name: Porter, Mason A.
last_name: Porter
- first_name: Guillaume
full_name: Hennequin, Guillaume
last_name: Hennequin
- 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: Stroud JP, Porter MA, Hennequin G, Vogels TP. Motor primitives in space and
time via targeted gain modulation in cortical networks. Nature Neuroscience.
2018;21(12):1774-1783. doi:10.1038/s41593-018-0276-0
apa: Stroud, J. P., Porter, M. A., Hennequin, G., & Vogels, T. P. (2018). Motor
primitives in space and time via targeted gain modulation in cortical networks.
Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-018-0276-0
chicago: Stroud, Jake P., Mason A. Porter, Guillaume Hennequin, and Tim P Vogels.
“Motor Primitives in Space and Time via Targeted Gain Modulation in Cortical Networks.”
Nature Neuroscience. Springer Nature, 2018. https://doi.org/10.1038/s41593-018-0276-0.
ieee: J. P. Stroud, M. A. Porter, G. Hennequin, and T. P. Vogels, “Motor primitives
in space and time via targeted gain modulation in cortical networks,” Nature
Neuroscience, vol. 21, no. 12. Springer Nature, pp. 1774–1783, 2018.
ista: Stroud JP, Porter MA, Hennequin G, Vogels TP. 2018. Motor primitives in space
and time via targeted gain modulation in cortical networks. Nature Neuroscience.
21(12), 1774–1783.
mla: Stroud, Jake P., et al. “Motor Primitives in Space and Time via Targeted Gain
Modulation in Cortical Networks.” Nature Neuroscience, vol. 21, no. 12,
Springer Nature, 2018, pp. 1774–83, doi:10.1038/s41593-018-0276-0.
short: J.P. Stroud, M.A. Porter, G. Hennequin, T.P. Vogels, Nature Neuroscience
21 (2018) 1774–1783.
date_created: 2020-06-30T13:18:02Z
date_published: 2018-12-01T00:00:00Z
date_updated: 2021-01-12T08:16:46Z
day: '01'
doi: 10.1038/s41593-018-0276-0
extern: '1'
external_id:
pmid:
- '30482949'
intvolume: ' 21'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276991/
month: '12'
oa: 1
oa_version: Submitted Version
page: 1774-1783
pmid: 1
publication: Nature Neuroscience
publication_identifier:
issn:
- 1097-6256
- 1546-1726
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1038/s41593-018-0307-x
status: public
title: Motor primitives in space and time via targeted gain modulation in cortical
networks
type: journal_article
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 21
year: '2018'
...
---
_id: '8016'
abstract:
- lang: eng
text: Long-term modifications of neuronal connections are critical for reliable
memory storage in the brain. However, their locus of expression—pre- or postsynaptic—is
highly variable. Here we introduce a theoretical framework in which long-term
plasticity performs an optimization of the postsynaptic response statistics toward
a given mean with minimal variance. Consequently, the state of the synapse at
the time of plasticity induction determines the ratio of pre- and postsynaptic
modifications. Our theory explains the experimentally observed expression loci
of the hippocampal and neocortical synaptic potentiation studies we examined.
Moreover, the theory predicts presynaptic expression of long-term depression,
consistent with experimental observations. At inhibitory synapses, the theory
suggests a statistically efficient excitatory-inhibitory balance in which changes
in inhibitory postsynaptic response statistics specifically target the mean excitation.
Our results provide a unifying theory for understanding the expression mechanisms
and functions of long-term synaptic transmission plasticity.
article_processing_charge: No
article_type: original
author:
- first_name: Rui Ponte
full_name: Costa, Rui Ponte
last_name: Costa
- first_name: Zahid
full_name: Padamsey, Zahid
last_name: Padamsey
- first_name: James A.
full_name: D’Amour, James A.
last_name: D’Amour
- first_name: Nigel J.
full_name: Emptage, Nigel J.
last_name: Emptage
- first_name: Robert C.
full_name: Froemke, Robert C.
last_name: Froemke
- 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: Costa RP, Padamsey Z, D’Amour JA, Emptage NJ, Froemke RC, Vogels TP. Synaptic
transmission optimization predicts expression loci of long-term plasticity. Neuron.
2017;96(1):177-189.e7. doi:10.1016/j.neuron.2017.09.021
apa: Costa, R. P., Padamsey, Z., D’Amour, J. A., Emptage, N. J., Froemke, R. C.,
& Vogels, T. P. (2017). Synaptic transmission optimization predicts expression
loci of long-term plasticity. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2017.09.021
chicago: Costa, Rui Ponte, Zahid Padamsey, James A. D’Amour, Nigel J. Emptage, Robert
C. Froemke, and Tim P Vogels. “Synaptic Transmission Optimization Predicts Expression
Loci of Long-Term Plasticity.” Neuron. Elsevier, 2017. https://doi.org/10.1016/j.neuron.2017.09.021.
ieee: R. P. Costa, Z. Padamsey, J. A. D’Amour, N. J. Emptage, R. C. Froemke, and
T. P. Vogels, “Synaptic transmission optimization predicts expression loci of
long-term plasticity,” Neuron, vol. 96, no. 1. Elsevier, p. 177–189.e7,
2017.
ista: Costa RP, Padamsey Z, D’Amour JA, Emptage NJ, Froemke RC, Vogels TP. 2017.
Synaptic transmission optimization predicts expression loci of long-term plasticity.
Neuron. 96(1), 177–189.e7.
mla: Costa, Rui Ponte, et al. “Synaptic Transmission Optimization Predicts Expression
Loci of Long-Term Plasticity.” Neuron, vol. 96, no. 1, Elsevier, 2017,
p. 177–189.e7, doi:10.1016/j.neuron.2017.09.021.
short: R.P. Costa, Z. Padamsey, J.A. D’Amour, N.J. Emptage, R.C. Froemke, T.P. Vogels,
Neuron 96 (2017) 177–189.e7.
date_created: 2020-06-25T12:54:46Z
date_published: 2017-09-27T00:00:00Z
date_updated: 2021-01-12T08:16:32Z
day: '27'
ddc:
- '570'
doi: 10.1016/j.neuron.2017.09.021
extern: '1'
external_id:
pmid:
- '28957667'
file:
- access_level: open_access
checksum: 49fbca2821066c0965bd5678b32b6b48
content_type: application/pdf
creator: cziletti
date_created: 2020-07-09T09:42:49Z
date_updated: 2020-07-14T12:48:08Z
file_id: '8103'
file_name: 2017_Neuron_Costa.pdf
file_size: 7140149
relation: main_file
file_date_updated: 2020-07-14T12:48:08Z
has_accepted_license: '1'
intvolume: ' 96'
issue: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 177-189.e7
pmid: 1
publication: Neuron
publication_identifier:
issn:
- 0896-6273
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: Synaptic transmission optimization predicts expression loci of long-term plasticity
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: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 96
year: '2017'
...