Chalcogenide optomemristors for multi-factor neuromorphic computation

TL;DR

This paper introduces chalcogenide optomemristors that leverage tunable electronic and optical properties for advanced neuromorphic computation, demonstrating their ability to emulate complex neural functions and solve challenging problems.

Contribution

It presents a novel use of nano-scaled chalcogenide films as optomemristors capable of multi-factor neural computations, including non-linear operations and reinforcement learning applications.

Findings

Emulation of neo-Hebbian plasticity and shunting inhibition.

Successful maze solving via reinforcement learning.

Single-neuron XOR problem solution.

Abstract

Neural processing on devices and circuits is fast becoming a popular approach to emulate biological neural networks. Elaborate CMOS and memristive technologies have been employed to achieve this, including chalcogenide-based in-memory computing concepts. Here we show that nano-scaled films of chalcogenide semiconductors can serve as building-blocks for novel types of neural computations where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate the computationally powerful non-linear operations of three-factor neo-Hebbian plasticity and the shunting inhibition. We apply this property to solve a maze game through reinforcement learning, as well as a single-neuron solution to the XOR, which is a linearly inseparable problem with point-neurons. Our results point to a new breed of memristors with broad implications for neuromorphic computing.