“Computation with Cell Assemblies”
Hosted by the Department of Electrical & Systems Engineering
Abstract: It’s exciting times at the intersection of machine learning and computational neuroscience. Deep neural networks grew out of ‘connectionist’ models in neuroscience and psychology in the 1980s and have found tremendous success in machine learning despite being based on models that are over three decades old. Since that time, an enormous amount has been learned about the circuit-level organization of the brain, yet it remains unclear how to use that knowledge to advance the capabilities of machine learning and AI.
A particularly important research topic is to understand how the generation and manipulation of discrete packets of network activity, known as ‘cell assemblies’, underlie neural computations. In this talk, I will describe a model of a neural circuit in biological olfaction in which information is represented and manipulated through the dynamics of cell assemblies. I will show how the relative timing patterns of spikes within cell assemblies are exploited by synaptic learning rules in the model, and how processes of neuromodulation and contextual priming enhance computational capabilities. I will also discuss the model’s performance compared to alternative techniques and describe its implementation in field-deployable neuromorphic chips.
For inquiries contact Nicole Voumard.