A Novel Low-power Neuromorphic Circuit based on Izhikevich Model

Sapounaki, Maria; Kakarountas, Athanasios

doi:10.1109/mocast52088.2021.9493396

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…A point neuron model such as the one used in this study requires a minimal set of parameters and only needs to communicate with other neurons in the network upon firing an action potential, therefore fulfilling the criteria of communication sparseness and memory locality that is a prerequisite for efficient operation on fully-distributed neuromorphic computing approaches. Izhikevich models have been successfully employed in various applications on neuromorphic hardware [12,16,17].…”

Section: Discussionmentioning

confidence: 99%

“…Izhikevich [6] maintained an effective trade-off between the biological plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons, making it a popular choice for implementing neuronal computations onto neuromorphic hardware [12,16,17] and robotic platforms [13,18]. Izhikevich presented a two-dimensional system of ordinary differential equations (Eq.…”

Section: Introductionmentioning

confidence: 99%

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Decoding the amplitude and slope of continuous signals into spikes with a spiking point neuron model

Miko,

Scheunemann,

Steuber

et al. 2024

Preprint

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In this study, we harness the signal processing potential of neurons, utilizing the Izhikevich point neuron model to efficiently decode the slope or amplitude of fluctuating continuous input signals. Using biophysically detailed compartmental neurons often requires significant computational resources. We present a novel approach to create behaviours and simulate these interactions in a lower-dimensional space, thereby reducing computational requirements. We began by conducting an extensive search of the Izhikevich parameter space, leading to the first significant outcome of our study: i) the identification of optimal parameter sets for generating slope or amplitude detectors, thereby achieving signal processing goals using neurons. Next, we compared the performance of the slope detector we discovered with a biophysically detailed two-compartmental pyramidal neuron model. Our findings revealed several key observations: ii) bursts primarily occurred on the rising edges of similar input signals, iii) our slope detector exhibited bidirectional slope detection capabilities, iv) variations in burst duration encoded the magnitude of input slopes in a graded manner. Overall, our study demonstrates the efficient and accurate simulation of dendrosomatic behaviours. Real-time applications in robotics or neuromorphic hardware can utilize our approach. While biophysically detailed compartmental neurons are compatible with such hardware, Izhikevich point neurons are more efficient. This work has the potential to facilitate the simulation of such interactions on a larger scale, encompassing a greater number of neurons and neuronal connections for the same computational power.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoding the amplitude and slope of continuous signals into spikes with a spiking point neuron model

Miko,

Scheunemann,

Steuber

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The main categorizer is the Izhikevich Spiking Neural Networks [38], which base their operation on the theory of dynamic systems. The Izhikevich model is a biological model that offers low computational complexity [39,40] like the Integrate-and-Fire models, as a neuron is treated as a homogeneous set of receiving and transmitting peaks, defined by the membrane's internal potentials [41,42].…”

Section: The Proposed Artificial Immune Systemmentioning

confidence: 99%

An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection

et al. 2022

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The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks’ efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem’s significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effectiveness of the proposed innovative system is demonstrated experimentally in multiple complex scenarios that optimally simulate the modern energy environment. The most significant findings of this work are that the transfer learning architecture’s shared learning rate significantly adds to the speed of generalization and convergence approach. In addition, the ensemble combination improves the accuracy of the model because the overall behavior of the numerous models is less noisy than a comparable individual single model. Finally, the Izhikevich Spiking Neural Network used here, due to its dynamic configuration, can reproduce different spikes and triggering behaviors of neurons, which models precisely the problem of digital security of energy infrastructures, as proved experimentally.

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A Novel Low-power Neuromorphic Circuit based on Izhikevich Model

Cited by 2 publications

References 8 publications

Decoding the amplitude and slope of continuous signals into spikes with a spiking point neuron model

Decoding the amplitude and slope of continuous signals into spikes with a spiking point neuron model

An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection

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