Biology subjects

Geadah, V.

Publications and source records attributed to Geadah, V..

3 recordsLinked to original sources

bioRxiv2025

Inferring learning rules during de novo task learning

Identifying the learning rules that govern behavior is a central problem in neuroscience. While reinforcement learning (RL) offers a unifying theoretical framework, most empirical studies of animal learning behavior have focused on non-stationary environments (e.g. changing reward probabilities in a known task), as opposed to acquiring an entirely new task from scratch. Here we introduce a statistical framework to infer reinforcement learning rules directly from single-animal behavior. Applied to mice learning a perceptual decision-making task, our approach reveals that policy-gradient-like rules capture de novo task learning better than classical temporal-difference algorithms. By fitting flexible parametric learning rules, we uncover systematic deviations from standard RL models, including side-specific learning rates and negative reward baselines. Together, these parameters account for side-biased learning, as well as forgetting and consecutive errors due to aversive responses to incorrect trials. Extending the framework with latent, dynamic learning rates further reveals that animals adapt their learning rates over training and across curricula. These results provide a statistical account of how animals learn from scratch and highlight key departures from classical reinforcement learning algorithms.

neuroscience↗

bioRxiv2024

Modeling Complex Animal Behavior with Latent State Inverse Reinforcement Learning

Understanding complex animal behavior is crucial for linking brain computation to observed actions. While recent research has shifted towards modeling behavior as a dynamic process, few approaches exist for modeling long-term, naturalistic behaviors such as navigation. We introduce discrete Dynamical Inverse Reinforcement Learning (dDIRL), a latent state-dependent paradigm for modeling complex animal behavior over extended periods. dDIRL models animal behavior as being driven by internal state-specific rewards, with Markovian transitions between the distinct internal states. Using expectation-maximization, we infer reward functions corresponding to each internal states and the transition probabilities between them, from observed behavior. We applied dDIRL to water-starved mice navigating a labyrinth, analyzing each animal individually. Our results reveal three distinct internal states sufficient to describe behavior, including a consistent water-seeking state occupied for less than half the time. We also identified two clusters of animals with different exploration patterns in the labyrinth. dDIRL offers a nuanced understanding of how internal states and their associated rewards shape observed behavior in complex environments, paving the way for deeper insights into the neural basis of naturalistic behavior.

neuroscience↗

bioRxiv2022

Goal-driven optimization of single-neuron properties in artificial networks reveals regularization role of neural diversity and adaptation

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We find that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we find that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time.

neuroscience↗