Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

In theoretical neuroscience, recent work leverages deep learning tools to explore how some
network attributes critically influence its learning dynamics. Notably, initial weight …

Brain levels of the endocannabinoid 2-arachidonoylglycerol (2-AG) shape motivated
behavior and nucleus accumbens (NAc) dopamine release. However, it is not clear whether …

Abstract Spiking Neural Networks (SNNs) are biologically-inspired models that are capable
of processing information in streams of action potentials. However, simulating and training …

Over the last few years, large neural generative models, capable of synthesizing
semantically rich passages of text or producing complex images, have recently emerged as …

To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of
gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond task …

Spiking neural networks (SNNs) are energy-efficient and hold great potential for large-scale
inference. Since training SNNs from scratch is costly and has limited performance …

Recurrent neural networks exhibit chaotic dynamics when the variance in their connection
strengths exceed a critical value. Recent work indicates connection variance also modulates …

The goal of theoretical neuroscience is to develop models that help us better understand
biological intelligence. Such models range broadly in complexity and biological detail. For …

Theoretical neuroscience has come to face a unique set of opportunities and challenges. By
virtue of being at the nexus of experimental neurobiology and machine learning, theoretical …