The temporal dimension is of fundamental importance to understanding the brain because one of the brain's primary function is temporal in nature: the brain uses information about the past (memories) to predict the future. As a result of the inherently temporal nature of brain function the brain has evolved mechanisms to tell time, encode time, and perform time-dependent computations. These computations endow animals with the ability to quickly learn to anticipate external events (for example when a red light should change), and to recognize and generate complex temporal patterns (such as those that underlie speech or Morse code). A feature of the brain's computational abilities is referred to as "temporal scaling," for example, the ability to talk, play music, or tap a Morse code message at different speeds. The neural mechanisms underlying timing and temporal scaling remain poorly understood. Furthermore, although dramatic advances have taken place in the field of machine learning, current machine learning approaches do not capture how the brain performs temporal computations or achieves temporal scaling. Emerging experimental data suggest that the brain may encode time and implement temporal scaling through a number of different dynamic regimes including ramping (increasing firing rates with time) or neural sequences (transient sequential activation of neurons). This project seeks to understand how time-dependent computations are performed in recurrent neural networks, and proposes that neural sequences provide an optimal solution to the problem of temporal scaling. This project will contribute to advances in the ability of artificial systems to capture the computational power of the brain. Associated education and outreach efforts are closely related to the research.

Two main approaches will be used. First, machine-learning based supervised recurrent neural networks will be trained on a number of different timing tasks--including a Morse code task that requires producing a complex temporal pattern at different speeds--in order to determine if neural sequences represent a general solution to the problems of encoding time and temporal scaling. Second, neuronal and synaptic properties that are mostly absent from current machine learning approaches will be used to develop a model of how neural sequences emerge and undergo temporal scaling in a biologically plausible fashion. Specifically, cortical synapses exhibit short-term synaptic plasticity, in which the strength of synapses change in a use-dependent manner over the course of hundreds of milliseconds, these dynamics can in turn be modulated--accelerating or slowing short-term synaptic plasticity. It is hypothesized that this modulation of short-term synaptic plasticity is one way the brain implements temporal scaling. Overall, this project will lead to novel biological principles being applied towards machine learning, and further advance the ability to emulate the brain’s computational strategies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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University of California Los Angeles
Los Angeles
United States
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