Fractional Spike-Frequency Adaptation for Multi-Timescale Negative Feedback in an LGMD2 Inspired Neuromorphic Model
- 1School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
- 2School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China
- 3College of Information Science & Engineering, Huaqiao University, Xiamen 361021, China
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.
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BibTeX
@article{deng2026fractional,
title = {{Fractional Spike-Frequency Adaptation for Multi-Timescale Negative Feedback in an LGMD2 Inspired Neuromorphic Model}},
author = {Yabin Deng and Pengzhou Wang and Bijing Liu and Yijin Wu and Junqi Huang and Zenan Huang},
journal = {Fractal and Fractional},
year = {2026},
month = oct,
doi = {10.3390/fractalfract10100713},
url = {https://www.mdpi.com/2504-3110/10/10/713},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Fractional Spike-Frequency Adaptation for Multi-Timescale Negative Feedback in an LGMD2 Inspired Neuromorphic Model
AU - Yabin Deng
AU - Pengzhou Wang
AU - Bijing Liu
AU - Yijin Wu
AU - Junqi Huang
AU - Zenan Huang
PY - 2026
DA - 2026/10/10
JO - Fractal and Fractional
DO - 10.3390/fractalfract10100713
UR - https://www.mdpi.com/2504-3110/10/10/713
AB - 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.
SN - 2504-3110
ER -