Persistent spiking activity in neuromorphic circuits incorporating post-inhibitory rebound excitation

Objective. This study introduces a novel approach for integrating the post-inhibitory rebound excitation (PIRE) phenomenon into a neuronal circuit. Excitatory and inhibitory synapses are designed to establish a connection between two hardware neurons, effectively forming a network. The model demonstrates the occurrence of PIRE under strong inhibitory input. Emphasizing the significance of incorporating PIRE in neuromorphic circuits, the study showcases generation of persistent activity within cyclic and recurrent spiking neuronal networks.Approach. The neuronal and synaptic circuits are designed and simulated in Cadence Virtuoso using TSMC 180 nm technology. The operating mechanism of the PIRE phenomenon integrated into a hardware neuron is discussed. The proposed circuit encompasses several parameters for effectively controlling multiple electrophysiological features of a neuron.Main results. The neuronal circuit has been tuned to match the response of a biological neuron. The efficiency of this circuit is evaluated by computing the average power dissipation and energy consumption per spike through simulation. The sustained firing of neural spikes is observed till 1.7 s using the two neuronal networks.Significance. Persistent activity has significant implications for various cognitive functions such as working memory, decision-making, and attention. Therefore, hardware implementation of these functions will require our PIRE-integrated model. Energy-efficient neuromorphic systems are useful in many artificial intelligence applications, including human-machine interaction, IoT devices, autonomous systems, and brain-computer interfaces.

The Framework and Memristive Circuit Design for Multisensory Mutual Associative Memory Networks

In this work, we propose a multisensory mutual associative memory networks framework and memristive circuit to mimic the ability of the biological brain to make associations of information received simultaneously. The circuit inspired by neural mechanisms of associative memory cells mainly consists of three modules: 1) the storage neurons module, which encodes external multimodal information into the firing rate of spikes; 2) the synapse module, which uses the nonvolatility memristor to achieve weight adjustment and associative learning; and 3) the retrieval neuron module, which feeds the retrieval signal output from each sensory pathway to other sensory pathways, so that achieve mutual association and retrieval between multiple modalities. Different from other one-to-one or many-to-one unidirectional associative memory work, this circuit achieves bidirectional association from multiple modalities to multiple modalities. In addition, we simulate the acquisition, extinction, recovery, transmission, and consolidation properties of associative memory. The circuit is applied to cross-modal association of image and audio recognition results, and episodic memory is simulated, where multiple images in a scene are intramodal associated. With power and area analysis, the circuit is validated as hardware-friendly. Further research to extend this work into large-scale associative memory networks, combined with visual-auditory-tactile-gustatory sensory sensors, is promising for application in intelligent robotic platforms to facilitate the development of neuromorphic systems and brain-like intelligence.

A Memristor-Based High-Resolution A/D Converter

Based on the voltage threshold adaptive memristor (VTEAM) model, this paper proposes a circuit design of a memristor-based A/D converter, which can achieve high-resolution conversion by simple configuration. For this A/D converter, there are the input voltage stage and the reference voltage stage in one conversion. According to the memristance change in the two stages, the input analog voltage is converted as the corresponding digital value. In the input voltage stage, the memristance increases from the initial memristance. Meanwhile, the counter rises its value from zero to the maximum. Next, the memristance returns to the initial memristance in the reference voltage stage. At the same time, the counting value starts to increase from zero again. Then, the input analog voltage is mapped to the eventual counting value of the reference voltage stage. The simulations of the memristor-based A/D converter demonstrate that it has good conversion performance. The proposed memristor-based A/D converter not only has more brilliant performance than the CMOS A/D converter, but also has the advantages over existing memristor-based A/D converters of anti-interference ability and high resolution.