Biology subjects

Saxe, A. M.

Publications and source records attributed to Saxe, A. M..

3 recordsLinked to original sources

bioRxiv2025

Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety and degeneracy at the heart of learning.

Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of strikingly diverse quadruplets of excitatory(E)-to-E, E-to-inhibitory(I), IE, and II plasticity rules, cooperating to stabilize recurrent spiking networks. Despite the fact that quadruplets were selected for homeostasis, more than 90% of them performed well in simple and more difficult memory tasks such as novelty detection, contextual novelty and sequence replay. Co-activity was crucial, i.e., most rules failed in isolation. Our purely local, unsupervised plasticity rules could also help solve computer games such as pong. Our work showcases automated discovery augmenting human intuition to find en masse solutions for high dimensional problems.

neuroscience↗

bioRxiv2024

NeuralPlayground: A Standardised Environment for Evaluating Models of Hippocampus and Entorhinal Cortex

Neural processes in the hippocampus and entorhinal cortex are thought to be crucial for spatial cognition. A growing variety of theoretical models have been proposed to capture the rich neural and behavioral phenomena associated with these circuits. However, systematic comparison of these theories, both against each other and against empirical data, remains challenging. To address this gap, we present NeuralPlayground, an open-source standardised software framework for comparisons between theory and experiment in the domain of spatial cognition. This Python software package offers a reproducible way to compare models against a centralised library of published experimental results, including neural recordings and animal behavior. The framework implements three Agents embodying different computational models; three Experiments comprising publicly available neural and behavioral datasets; a customisable 2-dimensional Arena (continuous and discrete) able to generate common and novel spatial layouts; and a Comparison tool that facilitates systematic comparisons between models and data. Each module can also be used separately, allowing standardised and flexible access to influential models and data sets. We hope NeuralPlayground, available on GitHub3, provides a starting point for a shared, standardized, open, and reproducible computational understanding of the role of the hippocampus and entorhinal cortex in spatial cognition.

neuroscience↗

bioRxiv2020

Rats strategically manage learning during perceptual decision making

Balancing the speed and accuracy of decisions is crucial for survival, but how organisms manage this trade-off during learning is largely unknown. Here, we track this trade-off during perceptual learning in rats and simulated agents. At the start of learning, rats chose long reaction times that did not optimize instantaneous reward rate, but by the end of learning chose near-optimal reaction times. To understand this behavior, we analyzed learning dynamics in a recurrent neural network model of the task. The model reveals a fundamental trade-off between instantaneous reward rate and perceptual learning speed, putting the goals of learning quickly and accruing immediate reward in tension. We find that the rats strategy of long initial responses can dramatically expedite learning, yielding higher total reward over task engagement. Our results demonstrate that prioritizing learning can be advantageous from a total reward perspective, and suggest that rats engage in cognitive control of learning.

neuroscience↗