Striatal Beat Frequency RL Automata as a Model of Time Cell Attenuation

Authors: Michael Tarlton

Presentation type: Poster at SNUFA 2024 online workshop (5-6 Nov 2024)

Abstract

Deep Learning is limited by the current method of credit-assignment, Back-Propagation (BP), which is impossible to utilize in deep non-linear architectures as found in the brain. BP is also difficult to implement in Spiking Neural Networks (SNNs), which offer a tremendous number of advantages over current network models. If we are going to take full advantage advantage of SNNs we must apply better methods of credit-assignment such as found in the brain.

As spiking communication is inherently time-based, we may be able to adapt known models of temporal dynamics and learning in the Brain for new methods of credit-assignment for SNNs. Time may add a dimension for triangulating credit-assignments to neurons in deep and otherwise unnavigable systems.

We study the smallest receptive field of timing on the Brain, the ability to distinguish and internally represent a single interval of time at the minimal scale: in the sub 100ms range. The difficulty lies, in constructing a neural model which can r learn and represent an interval of time in the sub-to-suprasecond range, from neurons with firing rates in the range of tens-of-milliseconds. I.e. How can time dynamics of one scale emerge into time dynamics at higher scales?

The Striatal Beat Frequency (SBF) is a neuroanatomical model of interval timing in the brain which encodes representation a target time-interval on the distribution of weights in circuit of spike-firing neurons. The design of the SBF allow for flexibility and scalability in online learning environments with neuromodulatory input.

We adapt the SBF model in a naïve neural automata which learns in an RL interval timing task and show its performance in a “single neuron” network for a variety of conditions and tasks.

Are you the author and do you want to add some related links to this page? If so, check out our guide to adding related links and videos.