Neuromorphic Computing is Turing-Complete

TL;DR

This paper proves that neuromorphic computing, which emulates the human brain and is energy-efficient, is capable of general-purpose computation by establishing its Turing-completeness through a formal model.

Contribution

The authors demonstrate that neuromorphic systems with minimal parameters can compute all μ-recursive functions, proving their Turing-completeness and potential for general-purpose computing.

Findings

Neuromorphic computing is Turing-complete.

A minimal model with two neuron and two synaptic parameters suffices.

Neuromorphic systems can perform all μ-recursive functions and operators.

Abstract

Neuromorphic computing is a non-von Neumann computing paradigm that performs computation by emulating the human brain. Neuromorphic systems are extremely energy-efficient and known to consume thousands of times less power than CPUs and GPUs. They have the potential to drive critical use cases such as autonomous vehicles, edge computing and internet of things in the future. For this reason, they are sought to be an indispensable part of the future computing landscape. Neuromorphic systems are mainly used for spike-based machine learning applications, although there are some non-machine learning applications in graph theory, differential equations, and spike-based simulations. These applications suggest that neuromorphic computing might be capable of general-purpose computing. However, general-purpose computability of neuromorphic computing has not been established yet. In this work, we prove that neuromorphic computing is Turing-complete and therefore capable of general-purpose computing. Specifically, we present a model of neuromorphic computing, with just two neuron parameters (threshold and leak), and two synaptic parameters (weight and delay). We devise neuromorphic circuits for computing all the {\mu}-recursive functions (i.e., constant, successor and projection functions) and all the {\mu}-recursive operators (i.e., composition, primitive recursion and minimization operators). Given that the {\mu}-recursive functions and operators are precisely the ones that can be computed using a Turing machine, this work establishes the Turing-completeness of neuromorphic computing.