Spike frequency adaptation: bridging neural models and neuromorphic applications

Spike frequency adaptation: bridging neural models and neuromorphic applications

(2024)3:22 | Chittotosh Ganguly, Sai Sukruth Bezugam, Elisabeth Abs, Melika Payvand, Sounak Dey, Manan Suri
The paper "Spike Frequency Adaptation: Bridging Neural Models and Neuromorphic Applications" by Chittotosh Ganguly et al. explores the significance of spike frequency adaptation (SFA) in spiking neural networks (SNNs) and its potential in artificial intelligence and hardware integration. SFA, which adjusts the frequency of spikes based on recent neuronal activity, enhances computational performance and energy efficiency in SNNs. The authors review various adaptive neuron models, including the Hodgkin-Huxley model, leaky-integrate and fire (LIF) model, Izhikevich model, and spike response model, with a focus on the LIF and adaptive LIF (ALIF) models. They highlight the advantages of SFA, such as reduced metabolic costs, improved separation of high-frequency signals, and enhanced memory capabilities. The paper discusses the implementation of SFA in hardware, including commercial off-the-shelf platforms and emerging technologies like resistive memory devices. It also reviews state-of-the-art applications of SFA in tasks like video recognition, image classification, and cognitive computing. The authors identify challenges in encoding techniques, learning algorithms, network architecture, hyperparameter tuning, and hardware compatibility, and propose future research directions, including the integration of SFA with emerging NVM technologies and the development of real-time adaptation in dynamic environments.The paper "Spike Frequency Adaptation: Bridging Neural Models and Neuromorphic Applications" by Chittotosh Ganguly et al. explores the significance of spike frequency adaptation (SFA) in spiking neural networks (SNNs) and its potential in artificial intelligence and hardware integration. SFA, which adjusts the frequency of spikes based on recent neuronal activity, enhances computational performance and energy efficiency in SNNs. The authors review various adaptive neuron models, including the Hodgkin-Huxley model, leaky-integrate and fire (LIF) model, Izhikevich model, and spike response model, with a focus on the LIF and adaptive LIF (ALIF) models. They highlight the advantages of SFA, such as reduced metabolic costs, improved separation of high-frequency signals, and enhanced memory capabilities. The paper discusses the implementation of SFA in hardware, including commercial off-the-shelf platforms and emerging technologies like resistive memory devices. It also reviews state-of-the-art applications of SFA in tasks like video recognition, image classification, and cognitive computing. The authors identify challenges in encoding techniques, learning algorithms, network architecture, hyperparameter tuning, and hardware compatibility, and propose future research directions, including the integration of SFA with emerging NVM technologies and the development of real-time adaptation in dynamic environments.
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[slides and audio] Spike frequency adaptation%3A bridging neural models and neuromorphic applications