NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)

TL;DR

This workshop examines the intersection of cognitive neuroscience and AI, focusing on how biological insights can inform machine learning models and how these models can help understand brain function.

Contribution

It highlights the importance of integrating brain-inspired representations and methods into AI research to enhance understanding and performance.

Findings

Deep learning models are increasingly inspired by biological principles.

Complex stimuli require rich vector representations for better modeling.

The relationship between brain function and machine learning remains an open question.

Abstract

This workshop explores the interface between cognitive neuroscience and recent advances in AI fields that aim to reproduce human performance such as natural language processing and computer vision, and specifically deep learning approaches to such problems. When studying the cognitive capabilities of the brain, scientists follow a system identification approach in which they present different stimuli to the subjects and try to model the response that different brain areas have of that stimulus. The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus. Experimental stimuli are becoming increasingly complex with more and more people being interested in studying real life phenomena such as the perception of natural images or natural sentences. There is therefore a need for a rich and adequate vector representation of the properties of the stimulus, that we can obtain using advances in machine learning. In parallel, new ML approaches, many of which in deep learning, are inspired to a certain extent by human behavior or biological principles. Neural networks for example were originally inspired by biological neurons. More recently, processes such as attention are being used which have are inspired by human behavior. However, the large bulk of these methods are independent of findings about brain function, and it is unclear whether it is at all beneficial for machine learning to try to emulate brain function in order to achieve the same tasks that the brain achieves.