Izhikevich Spiking Neuron Model Research Articles

Implementation of Izhikevich neuron based on stochastic computing using a novel inspired Omega‐Flip stochastic number generator

AbstractThis paper presents an optimized stochastic multiplier to implement Izhikevich spiking neuron model. A novel stochastic number generator is used as the main block for stochastic computing to design a stochastic multiplier. The designed multiplier was used to describe and implement Izhikevich neuron model equation which is capable to produce variant responses of spiking neurons. To the best of the author's knowledge, this work is the pioneer using a designed multiplier for computing all of the multiplication expressions in spiking neuron equation. The effect of increasing number of bit streams on the accuracy of stochastic computing is studied. Furthermore, some numerical intervals that are more prone to the error are found and illustrated upon stochastic computing usage, and a method is proposed to improve the error prone numerical intervals. In addition, the response accuracy is optimized by changing time step parameter in solving ordinary differential Izhikevich neuron model equation using Euler's method. Hence, a 13‐bit stochastic multiplier is designed. It is found that variations in parameters a and b have a profound effect on the accuracy of recovery variable u and, consequently, on the responses. Out of 20 responses of Izhikevich neuron model, 11 responses are generated using the proposed stochastic computing method and compared with piecewise linear method and some previous architectures of Izhikevich neuron model implementations.

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory.

The Izhikevich (IZH) spiking neuron model can display spiking and bursting behaviors of neurons. Based on the switching property and bio-plausibility of the memristor, the memristive Izhikevich (MIZH) spiking neuron model is built. Firstly, the MIZH spiking model is introduced and used to generate 23 spiking patterns. We compare the 23 spiking patterns produced by the IZH and MIZH spiking models. Secondly, the MIZH spiking model actively reproduces various neuronal behaviors, including the excitatory cortical neurons, the inhibitory cortical neurons, and other cortical neurons. Finally, the collective dynamic activities of the MIZH neuronal network are performed, and the MIZH oscillatory network is constructed. Experimental results illustrate that the constructed MIZH spiking neuron model performs high firing frequency and good frequency adaptation. The model can easily simulate various spiking and bursting patterns of distinct neurons in the brain. The MIZH neuronal network realizes the synchronous and asynchronous collective behaviors. The MIZH oscillatory network can memorize and retrieve the information patterns correctly and efficiently with high retrieval accuracy.

Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks

Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.

Self-repairing Control against Actuator Failures Using a Spiking Neuron Model

This paper presents a new Self-repairing Control System (SRCS) for plants with actuator failures. The proposed SRCS uses the well-known Izhikevich spiking neuron model as a fault detector. When the actuator fails, the neuron model is excited and then spikes occur. Thus, counting up spikes makes it possible to find failures. Compared with the existing active fault-tolerant control systems, the SRCS has the following advantages: one can not only set a maximum detection time in advance, but also construct a simple control system whose structure is independent of the mathematical model of the plant. To confirm the effectiveness of the SRCS, this paper shows theoretical performance analysis and numerical simulation.

A Neuromorphic Model to Match the Spiking Activity of Merkel Mechanoreceptors With Biomimetic Tactile Sensors for Bioengineering Applications

A major challenge in upper limb neuroprostheses is to reproduce the tactile feedback as the one provided by the spiking activity of human mechanoreceptors. In this paper, we aim at emulating the firing behavior of Merkel mechanoreceptors, innervated by slowly adapting type I (SA1) receptors, in silico by means of a custom implementation of the Izhikevich spiking neuron model with a porting function of the sensors output. We matched the neuron model output with the sustained firing observed in neurophysiological experiments in response to constant stimuli to the skin. We compared different input transformation functions to find the proper trade-off between agreement with biological spiking activity and model leanness. We identified a porting function that converts the output of physical sensors into the input to artificial spiking models of mechanoreceptors. The porting function was then validated by comparing the firing rate-indentation curve of real mechanoreceptors with the one obtained from an MEMS-based biomimetic tactile sensor platform. From the analysis of the adjusted-residual variance, the two curves result coherent. Therefore, having applied the calibration inverse function to a sensor output, the proposed porting function allows obtaining the proper input to an artificial neuron model, enabling the generation of neuromorphic signals.

Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus

The need to protect the environment and biodiversity and to safeguard public health require the development of timely and reliable methods for the identification of particularly dangerous invasive species, before they become regulators of ecosystems. These species appear to be morphologically similar, despite their strong biological differences, something that complicates their identification process. Additionally, the localization of the broader space of dispersion and the development of invasive species are considered to be of critical importance in the effort to take proper management measures. The aim of this research is to create an advanced computational intelligence system for the automatic recognition, of invasive or another unknown species. The identification is performed based on the analysis of environmental DNA by employing machine learning methods. More specifically, this research effort proposes a hybrid bio-inspired computational intelligence detection approach. It employs extreme learning machines combined with an evolving Izhikevich spiking neuron model for the automated identification of the invasive fish species “Lagocephalus sceleratus” extremely dangerous for human health.

Computational simulation of dentate gyrus granule cell—The role of metaplasticity

After several decades of study, the dynamics of synaptic plasticity in neurons still remains somewhat a mystery. By conducting a series of simulations on a simulated version of an in-vivo experiment on the rat dentate gyrus granule cell, using the Izhikevich spiking neuron model, we compare and contrast several potential synaptic plasticity rules׳ applicability to the same experiment. Our simulations reveal that spike timing dependent plasticity (STDP), a more recent theory of synaptic plasticity, is insufficient to replicate the heterosynaptic LTD shown in the experiment without including aspects of the significantly older Bienenstock–Cooper–Munro (BCM) theory. The STDP rule modified by including the history of postsynaptic spiking seems most likely to be an accurate candidate for reproducing the heterosynaptic plasticity dynamics.

ASIC Implementation for Improved Character Recognition and Classification using SNN Model

The third generation of spiking neural networks raises the level of biological realism by using individual spikes. So instead of using rate coding, these neurons use pulse coding mechanisms where neurons receive and do send out individual pulses, allowing multiplexing of information. This work depicts how Spiking neural network model is used for character recognition and classification. Here, we adapt to the technique of using ASIC for large scale simulations of the Izhikevich model and use RTL Clock gating approach for reducing the dynamic power. Here the focus is on how power consumption and system cost can be reduced for large production run. The full custom biologically plausible spiking neural network model is implemented on ASIC with 90 nm Process. The Izhikevich spiking neuron model is best suited for large scale cortical simulations due to its accuracy, efficiency, power and simulation time. The classification efficiency of SNN based on MATLAB simulations is demonstrated in this work by its ability to classify the 27 characters correctly out of 30 noisy character images presented. The ASIC realizing the English character classification and recognition dissipates power of 2.8 mW and an area of 120312 μm2. This work brings about the application of using networks of these spiking neurons for character recognition and their suitability for custom realization with reduced power consumption.

Emergence of synchronicity in a self-organizing spiking neuron network: an approach via genetic algorithms

Based on the Theory of Neuronal Group Selection (TNGS), we have investigated the emergence of synchronicity in a network composed of spiking neurons via genetic algorithm. The TNGS establishes that a neuronal group is the most basic unit in the cortical area of the brain and, as a rule, it is not formed by a single neuron, but by a cluster of tightly coupled neural cells which fire and oscillate in synchrony at a predefined frequency. Thus, this paper describes a method of tuning the parameters of the Izhikevich spiking neuron model through genetic algorithm in order to enable the self-organization of the neural network. Computational experiments were performed considering a network composed of neurons of the same type and another composed of neurons of different types.

The polychronic economy

The polychronic economy