A Neural Programming Language for the Reservoir Computer

TL;DR

This paper introduces a low-level programming language for neural networks based on reservoir computing, enabling complex computations, memory storage, and virtual machine functionalities within neural systems.

Contribution

It provides the first concrete, low-level neural programming language using reservoir computing, bridging the gap between neural and traditional computing paradigms.

Findings

Program complex equations in reservoir computers.

Store chaotic systems as memory (dRAM).

Implement software virtualization and logical circuits.

Abstract

From logical reasoning to mental simulation, biological and artificial neural systems possess an incredible capacity for computation. Such neural computers offer a fundamentally novel computing paradigm by representing data continuously and processing information in a natively parallel and distributed manner. To harness this computation, prior work has developed extensive training techniques to understand existing neural networks. However, the lack of a concrete and low-level programming language for neural networks precludes us from taking full advantage of a neural computing framework. Here, we provide such a programming language using reservoir computing -- a simple recurrent neural network -- and close the gap between how we conceptualize and implement neural computers and silicon computers. By decomposing the reservoir's internal representation and dynamics into a symbolic basis of its inputs, we define a low-level neural machine code that we use to program the reservoir to solve complex equations and store chaotic dynamical systems as random access memory (dRAM). Using this representation, we provide a fully distributed neural implementation of software virtualization and logical circuits, and even program a playable game of pong inside of a reservoir computer. Taken together, we define a concrete, practical, and fully generalizable implementation of neural computation.