@article {69127,
	title = {Model-Based Inference of Synaptic Plasticity Rules},
	journal = {bioRxiv},
	year = {2023},
	month = {2023 Dec 12},
	abstract = {<p>Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja{\textquoteright}s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from\&nbsp;<em>Drosophila</em>\&nbsp;during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.</p>
},
	doi = {10.1101/2023.12.11.571103},
	url = {https://doi.org/10.1101/2023.12.11.571103},
	author = {Yash Mehta and Danil Tyulmankov and Adithya E. Rajagopalan and Glenn C. Turner and James E. Fitzgerald and Jan Funke}
}
