---
_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:<a
    href="https://doi.org/10.15479/at:ista:14422">10.15479/at:ista:14422</a>'
  apa: 'Confavreux, B. J. (2023). <i>Synapseek: Meta-learning synaptic plasticity
    rules</i>. Institute of Science and Technology Austria. <a href="https://doi.org/10.15479/at:ista:14422">https://doi.org/10.15479/at:ista:14422</a>'
  chicago: 'Confavreux, Basile J. “Synapseek: Meta-Learning Synaptic Plasticity Rules.”
    Institute of Science and Technology Austria, 2023. <a href="https://doi.org/10.15479/at:ista:14422">https://doi.org/10.15479/at:ista:14422</a>.'
  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. <i>Synapseek: Meta-Learning Synaptic Plasticity Rules</i>.
    Institute of Science and Technology Austria, 2023, doi:<a href="https://doi.org/10.15479/at:ista:14422">10.15479/at:ista:14422</a>.'
  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
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  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'
...