Efficient inference of synaptic plasticity rule with Gaussian process regression

Chen, Shirui; Qin, Yang; Lim, Sukbin

doi:10.1016/j.isci.2023.106182

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…The authors make assumptions about the distribution of firing rates, as well as a first-order functional form of the learning rule. Chen et al (2023) elaborate on this approach, fitting a plasticity rule by either a Gaussian process or Taylor expansion, either directly to the synaptic weight updates or indirectly through neural activity over the course of learning. Both approaches consider only the difference in synaptic weights before and after learning.…”

Section: Related Workmentioning

confidence: 99%

Model-based inference of synaptic plasticity rules

Mehta,

Tyulmankov,

Rajagopalan

et al. 2023

Preprint

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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’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 fromDrosophiladuring 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.

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Section: Related Workmentioning

confidence: 99%

Model-based inference of synaptic plasticity rules

Mehta,

Tyulmankov,

Rajagopalan

et al. 2023

Preprint

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“…Statistical models have been used to model and infer learning rules in simulated data (S. Chen, Yang, & Lim, 2023; Linderman et al, 2014) and in experiments where memristors mimic synapses (Wu, Moon, Zhu, & Lu, 2021), but even though the technology to record spike data is available (Jun et al, 2017), not much work currently exists on how to use such recordings to infer learning rules in awake humans. We present a framework with which we aim to study synaptic plasticity in the awake human and animal brain, by assuming that the connections between pairs of neurons are driven by an STDP learning rule, and through that inferring parameters θ describing this rule.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference of spike-timing dependent plasticity learning rules from single neuron recordings in humans

Hem¹,

Ledergerber

Battistin

et al. 2023

Preprint

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The spike-timing dependent plasticity (STDP) learning rules are popular in both neuroscience and computer algorithms due to their ability to capture the change in neural connections arising from the correlated activity of neurons. Recent advances in recording technology have made large neural recordings common, but to date, there is no established method for inferring unobserved neural connections and learning rules from them. We use a Bayesian framework and assume neural spike recordings follow a binary data model to infer the connections and their evolution over time from data using STDP rules. We test the resulting method on one simulated and one real case study, where the real case study consists of human electrophysiological recordings. The simulated case study allows validation of the model, and the real case study shows that we are able to infer learning rules from awake human data that align with experimental results in rats and other animals. The real case study also uncovered a trend in maximum synaptic modification and age, however, this will need testing on a larger scale to draw conclusions.

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Nonlinear slow-timescale mechanisms in synaptic plasticity

O'Donnell

2023

Current Opinion in Neurobiology

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Efficient inference of synaptic plasticity rule with Gaussian process regression

Cited by 3 publications

References 67 publications

Model-based inference of synaptic plasticity rules

Model-based inference of synaptic plasticity rules

Bayesian inference of spike-timing dependent plasticity learning rules from single neuron recordings in humans

Nonlinear slow-timescale mechanisms in synaptic plasticity

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