Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits

TL;DR

This paper presents a versatile CMOS analog LIF neuron design with a voltage regulator and current attenuator, enabling efficient interfacing with memristive synapses and supporting local learning rules in neuromorphic circuits.

Contribution

The paper introduces a novel CMOS LIF neuron with dual leakage and adaptive connection schemes for improved integration with memristive synapses and local learning capabilities.

Findings

Effective impedance matching between neuron and memristive synapses.

Implementation of local learning rules such as voltage-dependent plasticity.

Versatile interface for diverse synaptic devices and temporal dynamics.

Abstract

Heterogeneous systems with analog CMOS circuits integrated with nanoscale memristive devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS Neuron with low footprint can emulate slow temporal dynamics by operating with extremely low current levels. Nevertheless, the current read from the memristive synapses can be higher by several orders of magnitude, and performing impedance matching between neurons and synapses is mandatory. In this paper, we implement an analog leaky integrate and fire (LIF) neuron with a voltage regulator and current attenuator for interfacing CMOS neurons with memristive synapses. In addition, the neuron design proposes a dual leakage that could enable the implementation of local learning rules such as voltage-dependent synaptic plasticity. We also propose a connection scheme to implement adaptive LIF neurons based on two-neuron interaction. The proposed circuits can be used to interface with a variety of synaptic devices and process signals of diverse temporal dynamics.