In this special edition we focus on the function of lateral connections (connections between neurons within a level). Long-range lateral connections are ubiquitous in the neocortex and cannot be explained by pure feedforward models. We have invited researchers from the Allen Institute for Brain Science to discuss their recently published paper on modeling these connections.
Our speakers will be Stefan Mihalas, Ramakrishnan Iyer, and Brian Hu. Their paper is titled “Contextual Integration in Cortical and Convolutional Neural Networks” and published as an open access paper here: https://www.frontiersin.org/articles/10.3389/fncom.2020.00031/full . Their paper presents a network model of cortical computation in which the lateral connections from surrounding neurons enable each neuron to integrate contextual information from features in the surround. They show that adding these connections to deep convolutional networks in an unsupervised manner makes them more robust to noise in the input image and leads to better classification accuracy under noise.