Emergent Computations in Trained Artificial Neural Networks and Real Brains

TL;DR

This paper explores how trained recurrent neural networks and real brains develop similar computational strategies for tasks like decision-making and working memory, highlighting the role of synaptic plasticity and neural communication.

Contribution

It demonstrates methods to train recurrent neural networks on neuroscience-like tasks and reveals the emergence of similar computations in artificial and biological neural systems.

Findings

Artificial networks can be trained to perform neuroscience tasks.

Emergent computations in artificial networks resemble those in real brains.

Synaptic plasticity underpins learning and task adaptation.

Abstract

Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories, and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.