RI: Small: Dynamic Attractor Computing: A Novel Computational Approach Applied Towards Temporal Pattern and Speech Recognition

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

Harnessing the brain's computational strategies has been a long sought objective of computational neuroscience and machine learning. One reason this goal has remained elusive is that most neurocomputational frameworks have not effectively captured a fundamental computational feature of the brain: the ability to seamlessly encode, represent, and processes temporal information. The current project seeks to address this shortcoming by using the neural dynamics inherent to recurrent neural networks to generate temporal patterns and process temporal information. The ability to generate the fine motor patterns necessary to play the piano or parse the complex temporal structure of speech, are but two examples of the human brain's sophisticated ability to generate and process complex temporal patterns. Notably, both these examples also illustrate an additional feature of the brain's computational abilities: "temporal warping." We can play the same musical piece at different speeds, or understand speech spoken at slow or fast rates. The mechanisms underlying the brain's ability to process temporal information in a flexible and temporally invariant fashion are a key focus of the current proposal. Recent theoretical and experimental studies have favored the view that the brain does not have sampling rates, time bins or explicit delay lines; but rather encodes time and the temporal features of stimuli through the internal dynamics of recurrent neural networks. The computational potential of these recurrent neural network models, however, has been limited for two reasons: 1) while it is well established that the recurrent connections of neural circuits are plastic, it has proven challenging to incorporate plasticity into simulated recurrent neural network models; 2) the dynamic regimes with the most computational potential are precisely those that also exhibit chaos--voiding much of their computational potential because the dynamics is not reproducible across trials. Building on a novel framework, this project tunes the weights of the recurrent connections in a manner that "tames" the chaotic dynamics of recurrent networks. The approach creates locally stable trajectories (dynamic attractors) which provide a novel and potentially powerful computational approach that can elegantly encode temporal information, and retain internal memories of recent events. Of particular relevance to the current project is to demonstrate that these networks can produce families of similar neural trajectories that flow at different speeds, thus allowing the network to generate the same complex motor pattern at different speeds. This project will also determine if the principles of dynamic attractors and time warping can be applied in the domain of sensory processing, using speech recognition as a test bed for the brain's ability to discriminate complex spatiotemporal stimuli.