---
_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: '14422'
abstract:
- lang: eng
text: "Animals exhibit a remarkable ability to learn and remember new behaviors,
skills, and associations throughout their lifetime. These capabilities are made
possible thanks to a variety of\r\nchanges in the brain throughout adulthood,
regrouped under the term \"plasticity\". Some cells\r\nin the brain —neurons—
and specifically changes in the connections between neurons, the\r\nsynapses,
were shown to be crucial for the formation, selection, and consolidation of memories\r\nfrom
past experiences. These ongoing changes of synapses across time are called synaptic\r\nplasticity.
Understanding how a myriad of biochemical processes operating at individual\r\nsynapses
can somehow work in concert to give rise to meaningful changes in behavior is
a\r\nfascinating problem and an active area of research.\r\nHowever, the experimental
search for the precise plasticity mechanisms at play in the brain\r\nis daunting,
as it is difficult to control and observe synapses during learning. Theoretical\r\napproaches
have thus been the default method to probe the plasticity-behavior connection.
Such\r\nstudies attempt to extract unifying principles across synapses and model
all observed synaptic\r\nchanges using plasticity rules: equations that govern
the evolution of synaptic strengths across\r\ntime in neuronal network models.
These rules can use many relevant quantities to determine\r\nthe magnitude of
synaptic changes, such as the precise timings of pre- and postsynaptic\r\naction
potentials, the recent neuronal activity levels, the state of neighboring synapses,
etc.\r\nHowever, analytical studies rely heavily on human intuition and are forced
to make simplifying\r\nassumptions about plasticity rules.\r\nIn this thesis,
we aim to assist and augment human intuition in this search for plasticity rules.\r\nWe
explore whether a numerical approach could automatically discover the plasticity
rules\r\nthat elicit desired behaviors in large networks of interconnected neurons.
This approach is\r\ndubbed meta-learning synaptic plasticity: learning plasticity
rules which themselves will make\r\nneuronal networks learn how to solve a desired
task. We first write all the potential plasticity\r\nmechanisms to consider using
a single expression with adjustable parameters. We then optimize\r\nthese plasticity
parameters using evolutionary strategies or Bayesian inference on tasks known\r\nto
involve synaptic plasticity, such as familiarity detection and network stabilization.\r\nWe
show that these automated approaches are powerful tools, able to complement established\r\nanalytical
methods. By comprehensively screening plasticity rules at all synapse types in\r\nrealistic,
spiking neuronal network models, we discover entire sets of degenerate plausible\r\nplasticity
rules that reliably elicit memory-related behaviors. Our approaches allow for
more\r\nrobust experimental predictions, by abstracting out the idiosyncrasies
of individual plasticity\r\nrules, and provide fresh insights on synaptic plasticity
in spiking network models.\r\n"
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
citation:
ama: 'Confavreux BJ. Synapseek: Meta-learning synaptic plasticity rules. 2023. doi:10.15479/at:ista:14422'
apa: 'Confavreux, B. J. (2023). Synapseek: Meta-learning synaptic plasticity
rules. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14422'
chicago: 'Confavreux, Basile J. “Synapseek: Meta-Learning Synaptic Plasticity Rules.”
Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:14422.'
ieee: 'B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute
of Science and Technology Austria, 2023.'
ista: 'Confavreux BJ. 2023. Synapseek: Meta-learning synaptic plasticity rules.
Institute of Science and Technology Austria.'
mla: 'Confavreux, Basile J. Synapseek: Meta-Learning Synaptic Plasticity Rules.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:14422.'
short: 'B.J. Confavreux, Synapseek: Meta-Learning Synaptic Plasticity Rules, Institute
of Science and Technology Austria, 2023.'
date_created: 2023-10-12T14:13:25Z
date_published: 2023-10-12T00:00:00Z
date_updated: 2023-10-18T09:20:56Z
day: '12'
ddc:
- '610'
degree_awarded: PhD
department:
- _id: GradSch
- _id: TiVo
doi: 10.15479/at:ista:14422
ec_funded: 1
file:
- access_level: closed
checksum: 7f636555eae7803323df287672fd13ed
content_type: application/pdf
creator: cchlebak
date_created: 2023-10-12T14:53:50Z
date_updated: 2023-10-12T14:54:52Z
embargo: 2024-10-12
embargo_to: open_access
file_id: '14424'
file_name: Confavreux_Thesis_2A.pdf
file_size: 30599717
relation: main_file
- access_level: closed
checksum: 725e85946db92290a4583a0de9779e1b
content_type: application/x-zip-compressed
creator: cchlebak
date_created: 2023-10-18T07:38:34Z
date_updated: 2023-10-18T07:56:08Z
file_id: '14440'
file_name: Confavreux Thesis.zip
file_size: 68406739
relation: source_file
file_date_updated: 2023-10-18T07:56:08Z
has_accepted_license: '1'
language:
- iso: eng
month: '10'
oa_version: Published Version
page: '148'
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '9633'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
title: 'Synapseek: Meta-learning synaptic plasticity rules'
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '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: '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: '8253'
abstract:
- lang: eng
text: Brains process information in spiking neural networks. Their intricate connections
shape the diverse functions these networks perform. In comparison, the functional
capabilities of models of spiking networks are still rudimentary. This shortcoming
is mainly due to the lack of insight and practical algorithms to construct the
necessary connectivity. Any such algorithm typically attempts to build networks
by iteratively reducing the error compared to a desired output. But assigning
credit to hidden units in multi-layered spiking networks has remained challenging
due to the non-differentiable nonlinearity of spikes. To avoid this issue, one
can employ surrogate gradients to discover the required connectivity in spiking
network models. However, the choice of a surrogate is not unique, raising the
question of how its implementation influences the effectiveness of the method.
Here, we use numerical simulations to systematically study how essential design
parameters of surrogate gradients impact learning performance on a range of classification
problems. We show that surrogate gradient learning is robust to different shapes
of underlying surrogate derivatives, but the choice of the derivative’s scale
can substantially affect learning performance. When we combine surrogate gradients
with a suitable activity regularization technique, robust information processing
can be achieved in spiking networks even at the sparse activity limit. Our study
provides a systematic account of the remarkable robustness of surrogate gradient
learning and serves as a practical guide to model functional spiking neural networks.
acknowledgement: F.Z. was supported by the Wellcome Trust (110124/Z/15/Z) and the
Novartis Research Foundation. T.P.V. was supported by a Wellcome Trust Sir Henry
Dale Research fellowship (WT100000), a Wellcome Trust Senior Research Fellowship
(214316/Z/18/Z), and an ERC Consolidator Grant SYNAPSEEK.
article_processing_charge: No
article_type: original
author:
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
orcid: 0000-0003-1883-644X
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Zenke F, Vogels TP. The remarkable robustness of surrogate gradient learning
for instilling complex function in spiking neural networks. Neural Computation.
2021;33(4):899-925. doi:10.1162/neco_a_01367
apa: Zenke, F., & Vogels, T. P. (2021). The remarkable robustness of surrogate
gradient learning for instilling complex function in spiking neural networks.
Neural Computation. MIT Press. https://doi.org/10.1162/neco_a_01367
chicago: Zenke, Friedemann, and Tim P Vogels. “The Remarkable Robustness of Surrogate
Gradient Learning for Instilling Complex Function in Spiking Neural Networks.”
Neural Computation. MIT Press, 2021. https://doi.org/10.1162/neco_a_01367.
ieee: F. Zenke and T. P. Vogels, “The remarkable robustness of surrogate gradient
learning for instilling complex function in spiking neural networks,” Neural
Computation, vol. 33, no. 4. MIT Press, pp. 899–925, 2021.
ista: Zenke F, Vogels TP. 2021. The remarkable robustness of surrogate gradient
learning for instilling complex function in spiking neural networks. Neural Computation.
33(4), 899–925.
mla: Zenke, Friedemann, and Tim P. Vogels. “The Remarkable Robustness of Surrogate
Gradient Learning for Instilling Complex Function in Spiking Neural Networks.”
Neural Computation, vol. 33, no. 4, MIT Press, 2021, pp. 899–925, doi:10.1162/neco_a_01367.
short: F. Zenke, T.P. Vogels, Neural Computation 33 (2021) 899–925.
date_created: 2020-08-12T12:08:24Z
date_published: 2021-03-01T00:00:00Z
date_updated: 2023-08-04T10:53:14Z
day: '01'
ddc:
- '000'
- '570'
department:
- _id: TiVo
doi: 10.1162/neco_a_01367
ec_funded: 1
external_id:
isi:
- '000663433900003'
pmid:
- '33513328'
file:
- access_level: open_access
checksum: eac5a51c24c8989ae7cf9ae32ec3bc95
content_type: application/pdf
creator: dernst
date_created: 2022-04-08T06:05:39Z
date_updated: 2022-04-08T06:05:39Z
file_id: '11131'
file_name: 2021_NeuralComputation_Zenke.pdf
file_size: 1611614
relation: main_file
success: 1
file_date_updated: 2022-04-08T06:05:39Z
has_accepted_license: '1'
intvolume: ' 33'
isi: 1
issue: '4'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 899-925
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: Neural Computation
publication_identifier:
eissn:
- 1530-888X
issn:
- 0899-7667
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: The remarkable robustness of surrogate gradient learning for instilling complex
function in spiking neural networks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 33
year: '2021'
...
---
_id: '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'
...