Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems

Abstract

Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. The brain is able to learn the dynamic nature of the physical world via experience; analogously, artificial neural systems such as reservoir computing networks (RCNs) can learn the long-term behavior of complex dynamical systems from data. Recent work has shown that the mechanism of such learning in RCNs is invertible generalized synchronization (IGS). Yet, whether IGS is also the mechanism of learning in biological systems remains unclear. To shed light on this question, we draw inspiration from features of the human brain to propose a general and biologically feasible learning framework that utilizes IGS. To evaluate the framework’s relevance, we construct several distinct neural network models as instantiations of the proposed framework. Regardless of their particularities, these neural network models can consistently learn to imitate other dynamical processes with a biologically feasible adaptation rule that modulates the strength of synapses. Further, we observe and theoretically explain the spontaneous emergence of four distinct phenomena reminiscent of cognitive functions: (i) learning multiple dynamics; (ii) switching among the imitations of multiple dynamical systems, either spontaneously or driven by external cues; (iii) filling-in missing variables from incomplete observations; and (iv) deciphering superimposed input from different dynamical systems. Collectively, our findings support the notion that biological neural networks can learn the dynamic nature of their environment through the mechanism of IGS.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2020
Source ID
10.1063/5.0004344

Entities

People

  • Danielle Bassett
  • Zhixin Lu

Organizations

  • Alfred P. Sloan Foundation
  • Army Research Office
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development
  • Institute for Scientific Interchange
  • John D. and Catherine T. MacArthur Foundation
  • National Institute of Mental Health
  • National Institute of Neurological Disorders and Stroke
  • National Science Foundation
  • Office of Naval Research
  • Paul G. Allen Family Foundation
  • United States Army Research Laboratory
  • University of Pennsylvania

Tags

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks