Topic: biological neural networks

Artificial recurrent neural networks (RNNs) have become a customary research tool in theoretical neuroscience, being used as models of biological neural networks performing cognitive tasks. While RNNs are not generally intended as faithful, biologically realistic models of neuronal activity, there is a broad expectation that RNN dynamics may provide good models at the level of neural population dynamics. According to several authors, in both cases population activity should organize into low-dimensional manifolds encoding the relevant task variables (1). However, strong tests of this hypothesi

In modern deep neural networks, the intricate learning dynamics of individual neurons often remain obscured due to global optimization methods. This contrasts with biological neural networks, which achieve robustness and efficiency through self-organized, local learning processes using limited global information. To bridge this gap, a local learning framework inspired by the self-organizing principles of biological neurons has been proposed [1]. This framework allows for the investigation of how local information-processing goals can drive the emergence of complex network-level functions. The