---
res:
  bibo_abstract:
  - Synaptic plasticity is a key player in the brain’s life-long learning abilities.
    However, due to experimental limitations, the mechanistic link between synaptic
    plasticity rules and the network-level computations they enable remain opaque.
    Here we use evolutionary strategies (ES) to meta learn local co-active plasticity
    rules in large recurrent spiking networks with excitatory (E) and inhibitory (I)
    neurons, using parameterizations of increasing complexity. We discover rules that
    robustly stabilize network dynamics for all four synapse types acting in isolation
    (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity
    detection can also be included in the search constraints. However, our meta learning
    strategy begins to fail for co-active rules of increasing complexity, as it is
    challenging to devise loss functions that effectively constrain network dynamics
    to plausible solutions a priori. Moreover, in line with previous work, we can
    find multiple degenerate solutions with identical network behaviour. As a local
    optimization strategy, ES provides one solution at a time and makes exploration
    of this degeneracy cumbersome. Regardless, we can glean the interdependecies of
    various plasticity parameters by considering the covariance matrix learned alongside
    the optimal rule with ES. Our work provides a proof of principle for the success
    of machine-learning-guided discovery of plasticity rules in large spiking networks,
    and points at the necessity of more elaborate search strategies going forward.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Basile J
      foaf_name: Confavreux, Basile J
      foaf_surname: Confavreux
      foaf_workInfoHomepage: http://www.librecat.org/personId=C7610134-B532-11EA-BD9F-F5753DDC885E
  - foaf_Person:
      foaf_givenName: Everton J.
      foaf_name: Agnes, Everton J.
      foaf_surname: Agnes
  - foaf_Person:
      foaf_givenName: Friedemann
      foaf_name: Zenke, Friedemann
      foaf_surname: Zenke
  - foaf_Person:
      foaf_givenName: Henning
      foaf_name: Sprekeler, Henning
      foaf_surname: Sprekeler
  - foaf_Person:
      foaf_givenName: Tim P
      foaf_name: Vogels, Tim P
      foaf_surname: Vogels
      foaf_workInfoHomepage: http://www.librecat.org/personId=CB6FF8D2-008F-11EA-8E08-2637E6697425
    orcid: 0000-0003-3295-6181
  bibo_doi: 10.1371/journal.pcbi.1012910
  bibo_issue: '4'
  bibo_volume: 21
  dct_date: 2025^xs_gYear
  dct_identifier:
  - UT:001474257000002
  dct_isPartOf:
  - http://id.crossref.org/issn/1553-734X
  - http://id.crossref.org/issn/1553-7358
  dct_language: eng
  dct_publisher: Public Library of Science@
  dct_title: Balancing complexity, performance and plausibility to meta learn plasticity
    rules in recurrent spiking networks@
  fabio_hasPubmedId: '40273284 '
...