Sequence reproduction, single trial learning, and mimicry based on a mammalian-like distributed code for time

TL;DR

This paper presents a neural model inspired by mammalian prefrontal cortex activity that enables sequence reproduction, single-trial learning, and flexible mimicry of temporal patterns using a distributed coding scheme and STDP.

Contribution

It introduces a novel model leveraging slowly-varying neural activities and STDP for rapid sequence learning and flexible playback, inspired by mammalian neural dynamics.

Findings

Sequence identification via population activity patterns.

Single-trial learning of sequences with STDP.

Sequence playback modulation (speed and reversal).

Abstract

Animals learn tasks requiring a sequence of actions over time. Waiting a given time before taking an action is a simple example. Mimicry is a complex example, e.g. in humans, humming a brief tune you have just heard. Re-experiencing a sensory pattern mentally must involve reproducing a sequence of neural activities over time. In mammals, neurons in prefrontal cortex have time-dependent firing rates that vary smoothly and slowly in a stereotyped fashion. We show through modeling that a Many are Equal computation can use such slowly-varying activities to identify each timepoint in a sequence by the population pattern of activity at the timepoint. The MAE operation implemented here is facilitated by a common inhibitory conductivity due to a theta rhythm. Sequences of analog values of discrete events, exemplified by a brief tune having notes of different durations and intensities, can be learned in a single trial through STDP. An action sequence can be played back sped up, slowed down, or reversed by modulating the system that generates the slowly changing stereotyped activities. Synaptic adaptation and cellular post-hyperpolarization rebound contribute to robustness. An ability to mimic a sequence only seconds after observing it requires the STDP to be effective within seconds.