Work

Fractional Spike-Frequency Adaptation for Multi-Timescale Negative Feedback in an LGMD2 Inspired Neuromorphic Model

Year: 2026

Type: article

Abstract: Spike-frequency adaptation (SFA) regulates neuronal excitability according to recent spiking activity, yet conventional neuromorphic models typically describe adaptation using single-timescale exponential recovery. To the best of our knowledge, most existing fractional spiking-neuron formulations introduce memory primarily through membrane or input-related dynamics rather than through the recovery of a spike-triggered adaptation current. Here, we propose fractional spike-frequency adaptation (F-SFA), in which an integer-order LIF membrane is coupled with a spike-triggered adaptation current undergoing fractional-order recovery. A compact multi-exponential embedding enables recursive implementation without explicit history convolution. Single-neuron experiments show firing rate adaptation, progressive inter-spike-interval (ISI) elongation, and post-stimulus recovery. F-SFA is further incorporated into an event-driven Lobula Giant Movement Detector 2 (LGMD2)-inspired model without feed-forward inhibition (FFI). The resulting model retains a pronounced response to dark looming while providing additional history-dependent attenuation of sustained translation and post-looming activity. These results support the feasibility of F-SFA as a compact mechanism for introducing multi-timescale spike-history-dependent adaptation into event-driven neuromorphic models.

Source: Fractal and Fractional

DOI: 10.3390/fractalfract10100713

Authors: Yabin Deng, Pengzhou Wang, Bijing Liu, Yijin Wu, Junqi Huang, Zenan Huang

Institutions: Xiamen University of Technology, Xiamen University, Huaqiao University

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: Xiamen University of Technology