@phdthesis{14422,
  abstract     = {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
changes in the brain throughout adulthood, regrouped under the term "plasticity". Some cells
in the brain —neurons— and specifically changes in the connections between neurons, the
synapses, were shown to be crucial for the formation, selection, and consolidation of memories
from past experiences. These ongoing changes of synapses across time are called synaptic
plasticity. Understanding how a myriad of biochemical processes operating at individual
synapses can somehow work in concert to give rise to meaningful changes in behavior is a
fascinating problem and an active area of research.
However, the experimental search for the precise plasticity mechanisms at play in the brain
is daunting, as it is difficult to control and observe synapses during learning. Theoretical
approaches have thus been the default method to probe the plasticity-behavior connection. Such
studies attempt to extract unifying principles across synapses and model all observed synaptic
changes using plasticity rules: equations that govern the evolution of synaptic strengths across
time in neuronal network models. These rules can use many relevant quantities to determine
the magnitude of synaptic changes, such as the precise timings of pre- and postsynaptic
action potentials, the recent neuronal activity levels, the state of neighboring synapses, etc.
However, analytical studies rely heavily on human intuition and are forced to make simplifying
assumptions about plasticity rules.
In this thesis, we aim to assist and augment human intuition in this search for plasticity rules.
We explore whether a numerical approach could automatically discover the plasticity rules
that elicit desired behaviors in large networks of interconnected neurons. This approach is
dubbed meta-learning synaptic plasticity: learning plasticity rules which themselves will make
neuronal networks learn how to solve a desired task. We first write all the potential plasticity
mechanisms to consider using a single expression with adjustable parameters. We then optimize
these plasticity parameters using evolutionary strategies or Bayesian inference on tasks known
to involve synaptic plasticity, such as familiarity detection and network stabilization.
We show that these automated approaches are powerful tools, able to complement established
analytical methods. By comprehensively screening plasticity rules at all synapse types in
realistic, spiking neuronal network models, we discover entire sets of degenerate plausible
plasticity rules that reliably elicit memory-related behaviors. Our approaches allow for more
robust experimental predictions, by abstracting out the idiosyncrasies of individual plasticity
rules, and provide fresh insights on synaptic plasticity in spiking network models.
},
  author       = {Confavreux, Basile J},
  issn         = {2663 - 337X},
  pages        = {148},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Synapseek: Meta-learning synaptic plasticity rules}},
  doi          = {10.15479/at:ista:14422},
  year         = {2023},
}

@article{10753,
  abstract     = {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.},
  author       = {Confavreux, Basile J and Vogels, Tim P},
  issn         = {1097-4199},
  journal      = {Neuron},
  number       = {3},
  pages        = {361--362},
  publisher    = {Elsevier},
  title        = {{A familiar thought: Machines that replace us?}},
  doi          = {10.1016/j.neuron.2022.01.014},
  volume       = {110},
  year         = {2022},
}

@inproceedings{9633,
  abstract     = {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.},
  author       = {Confavreux, Basile J and Zenke, Friedemann and Agnes, Everton J. and Lillicrap, Timothy and Vogels, Tim P},
  booktitle    = {Advances in Neural Information Processing Systems},
  issn         = {1049-5258},
  location     = {Vancouver, Canada},
  pages        = {16398--16408},
  title        = {{A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network}},
  volume       = {33},
  year         = {2020},
}