Spike coding: recents insights on neural networks
By: Christian Machens
From: Fundação Champalimaud, Lisbon, Portugal
At: Building C6, 6.2.49
[2023-03-16]
Models of neural networks can be largely divided into two camps. On one end are functional models, such as rate networks, that can perform a multitude of functions and have led to many recent breakthroughs in ML/AI, but ignore well-established biological facts. On the other end are mechanistic models such as balanced spiking networks that resemble neural activity, but are limited to simple computations. Here, I will introduce a new framework for spiking networks which retains key properties of both mechanistic and functional models. The key insight is to recast the problem of spiking dynamics in a lower-dimensional space of network activity modes rather than in the original neural space. I will illustrate these insights with simple, geometric toy models, and show how they allow us to construct networks that are both computationally powerful, while reproducing key biological facts. I will argue that these results force us to reconsider the very basics of how we think about neural networks.
