@phdthesis{20735,
  abstract     = {Left–right alternation is a defining feature of spinal locomotor circuits, yet the level of neuronal
detail required to generate and maintain this pattern remains unclear. This thesis investigates how
models spanning multiple levels of abstraction—from biophysically detailed Hodgkin–Huxley (HH)
neurons to adaptive integrate–and–fire (I&F) formulations and synfire-chain modules—can account
for the generation of fictive swimming in the spinal cord of the Xenopus laevis tadpole. The guiding
hypothesis is that a small set of neuronal mechanisms is sufficient to reproduce the essential features
of rhythmic alternation, and that moving between modeling scales helps distinguish core principles
from biological detail.
A minimal bilateral HH network comprising only four canonical neuron classes—excitatory
descending interneurons (dINs), inhibitory commissural interneurons (cINs), ipsilateral inhibitory
interneurons (aINs) and motoneurons—served as a biophysical proof of concept. Tuned to reproduce
experimentally observed firing modes, the model demonstrated that rebound-prone dIN excitability,
contralateral inhibition and modest electrical coupling are sufficient to generate stable alternating
activity, even in very small networks. These results motivated the transition to simpler models
capable of efficient analysis and scaling.
Adaptive exponential I&F (AdEx) neurons were calibrated to physiological recordings using
simulation-based inference, yielding tonic and phasic/rebound templates that preserved the key
dynamical signatures of the HH model. Phase-plane analysis clarified the mechanisms underlying
single-spike responses and rebound firing in dINs. At network level, the I&F models robustly
reproduced left–right alternation, while highlighting constraints on synaptic kinetics and adaptation
needed to avoid multi-spike responses.
Finally, a synfire-chain framework provided a complementary, timing-centric perspective, demonstrating how precise spike synchrony, synaptic delays and minimal inhibitory coupling can generate
alternating left–right sequences in a feedforward setting. Together, these approaches converge on a
common conclusion: rebound-prone ipsilateral excitation combined with precisely timed contralateral inhibition constitutes a sufficient substrate for alternating spinal rhythms.
By integrating bottom-up and top-down modeling strategies, this thesis provides a unified, extensible framework for studying spinal pattern generation. The results show that essential locomotor
dynamics can be captured across multiple abstraction levels, offering both mechanistic insight and
practical tools for future data-driven investigations of spinal circuit development, robustness and
modulation.},
  author       = {Wilson, Alexia C},
  issn         = {2791-4585},
  pages        = {110},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Modelling the spinal cord of a tadpole: Exploring different ways to model the spinal cord in the Xenopus frog}},
  doi          = {10.15479/AT-ISTA-20735},
  year         = {2025},
}

@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},
}