Speaker: Aaron R. Voelker, PhD Candidate
One of the central challenges in neuroscience involves understanding how dynamic stimuli can be processed by neural mechanisms to drive behavior. By synthesizing models from neuroscience with tools from signal processing and control theory, we derive the feedback weights required to represent a low-frequency input signal by delaying it along a low-dimensional manifold, using a recurrently connected network of biologically plausible spiking neurons. This is shown to nonlinearly encode the continuous-time history of an input signal by optimally compressing it into a low-dimensional subspace.
We further extend this theory to harness higher-order dynamical models of the synapse, to improve the accuracy of our simulations on mixed-analog-digital neuromorphic hardware. These methods provide a novel explanation of time cell responses during a delay task, which have been observed throughout the hippocampus, striatum and cortex, thus providing a novel understanding of how temporal information is represented in biological systems.
* Manuscript in review: [2]https://github.com/arvoelke/delay2017.