A Modified Izhikevich Model For Circuit Implementation of Spiking Neural Networks (original) (raw)
Mathematics, 2022
The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high deg...
The Application Perspective of Izhikevich Spiking Neural Model - The Initial Experimental Study
2017
In this paper we explore the Izhikevich spiking neuron model especially the synergy of the dimensionless model parameters and their implications to the spiking of the neuron itself. This spiking, principally the spike rate, is highly important from the application point of view. The understanding of the model is useful for better spiking network design, when the input neuronal stimulus is transferred to the spikes in order to produce faster network response. Whereas we can achieve the better neuronal response of the spiking network through utilization of the correct model parameters which impact to the neurons and the network neuronal dynamics significantly. The model parameters setup were described, demonstrated and spiking neuron model output and behaviour examined. The influence of the input current was also described in a given experimental study.
Biologically Inspired Spiking Neurons: Piecewise Linear Models and Digital Implementation
2012
there has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation.
2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013
Nowadays neuroscience is progressing to higher levels which have made it possible to have a better understanding of the brain behavior. In this scheme, spiking neural network has a great potential and have attracted much research interests. In this direction, one problem in simulations and implementations is speed and simplicity. In other hand, neuron models as building blocks of the neuronal systems have a vital role. One of the recently developed neuron models is called "Adaptive Exponential Integrate and Fire". This model is a two dimensional system that can produce rich firing pattern. This paper proposes simplified models based on the Adaptive Exponential Integrate and Fire model. This modification simplifies the hardware implementation, increases speed and demonstrates similar dynamic behavior. These models can be used for both of analog and digital implementations. This paper can be a step in the neural network simulation and implementation as large as the brain scale.
Nonlinear electronic circuit with neuron like bursting and spiking dynamics
Biosystems, 2009
It is difficult to design electronic nonlinear devices capable of reproducing complex oscillations because of the lack of general constructive rules, and because of stability problems related to the dynamical robustness of the circuits. This is particularly true for current analog electronic circuits that implement mathematical models of bursting and spiking neurons. Here we describe a novel, four-dimensional and dynamically robust nonlinear analog electronic circuit that is intrinsic excitable, and that displays frequency adaptation bursting and spiking oscillations. Despite differences from the classical Hodgkin-Huxley (HH) neuron model, its bifurcation sequences and dynamical properties are preserved, validating the circuit as a neuron model. The circuit's performance is based on a nonlinear interaction of fast-slow circuit blocks that can be clearly dissected, elucidating burst's starting, sustaining and stopping mechanisms, which may also operate in real neurons. Our analog circuit unit is easily linked and may be useful in building networks that perform in real-time.
Efficient modelling of spiking neural networks on a scalable chip multiprocessor
2008
Abstract We propose a system based on the Izhikevich model running on a scalable chip multiprocessor-SpiNNaker-for large-scale spiking neural network simulation. The design takes into account the requirements for processing, storage, and communication which are essential to the efficient modelling of spiking neural networks. To gain a speedup of the processing as well as saving storage space, the Izhikevich model is implemented in 16-bit fixed-point arithmetic.
Hardware Implementation of LIF and HH Spiking Neuronal Models
2019
This paper presents a hardware implementation of both Hodgkin-Huxley (HH) and Leaky Integrate and Fire (LIF) spiking neuronal models. FPGA is used as digital platform due to flexibility and reconfigureability. The proposed neural models are simulated by MatLab and the results are compared with the HDL software’s output in order to evaluate the design. Simple architecture uses two counters and a comparator used as the main part of leaky Integrate and Fire model. For the Hodgkin and Huxley model a Look Up Table based structure is utilized. Although it consumes large amount of area, it results more reasonable propagation delay time hence higher operating frequency. The proposed architectures are evaluated on Stratix III device using Quartus II simulator. Maximum operating frequency of 583 MHz (limited to 500 MHz due to the device port rate) and 76 MHz are achieved for the LIF and HH architectures respectively.
A functional spiking neuron hardware oriented model
Lecture Notes in Computer Science, 2003
In this paper we present a functional model of spiking neuron intended for hardware implementation. The model allows the design of speedand/or area-optimized architectures. Some features of biological spiking neurons are abstracted, while preserving the functionality of the network, in order to define an architecture easily implementable in hardware, mainly in field programmable gate arrays (FPGA). The model permits to optimize the architecture following area or speed criteria according to the application. In the same way, several parameters and features are optional, so as to allow more biologically plausible models by increasing the complexity and hardware requirements of the model. We present the results of three example applications performed to verify the computing capabilities of a simple instance of our model.
An Functional Spiking Neuron Hardware Oriented Model
2003
In this paper we present a functional model of spiking neuron intended for hardware implementation. The model allows the design of speed- and/or area-optimized architectures. Some features of biological spiking neurons are abstracted, while preserving the functionality of the network, in order to define an architecture easily implementable in hardware, mainly in field programmable gate arrays (FPGA). The model permits to optimize the architecture following area or speed criteria according to the application. In the same way, several parameters and features are optional, so as to allow more biologically plausible models by increasing the complexity and hardware requirements of the model. We present the results of three example applications performed to verify the computing capabilities of a simple instance of our model.
An Efficient Implementation of a Realistic Spiking Neuron Model on an FPGA
2010
Hardware implementations of spiking neuron models have been studied over the years mainly in researches focused on bio-inspired systems and computational neuroscience. This introduced considerable challenges for researchers particularly in terms of the requirements to realise a efficient embedded solution which may provide artificial devices adaptability and performance in real-time environment. Thus, programmable hardware was widely used as a model for the adaptable requirements of neural networks. From this perspective, this paper describes an efficient implementation of a realistic spiking neuron model on a Field Programmable Gate Array (FPGA). A network consisting of 10 Izhikevich's neurons was produced, in a low-cost and low-density FPGA. It operates 100 times faster than in real time, and the perspectives of these results in newer models of FPGAs are promising.
Spiking Neural Networks: Modification and Digital Implementation
2020
Real-time large-scale simulation of biological systems is a challenging task due to nonlinear functions describing biochemical reactions in the cells. Being fast, cost and power efficient alongside of capability to work in parallel have made hardware an attractive choice for simulation platform. This thesis proposes a neuromorphic platform for online Spike Timing Dependant Plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) algorithms. The implemented platform comprises two main components. First, the Izhikevich neuron model is modified for implementation using the CORDIC algorithm and simulated to ensure the model accuracy. Afterwards, the model was described as hardware and implemented on Field Programmable Gate Array (FPGA). Second, the STDP learning algorithm is adapted and optimized using the CORDIC method, synthesized for hardware, and implemented to perform on-FPGA online learning on a network of CORDIC Izhikevich neurons to demonstrate comp...
A Review of Biologically Plausible Neuron Models for Spiking Neural Networks
AIAA Infotech@Aerospace 2010, 2010
In this paper, five mathematical models of single neurons are discussed and compared. The physical meanings, derivations, and differential equations of each model are provided. Since for many applications the spiking rates of neurons are of great importance, we compare the spiking rate patterns under different sustained current inputs. Numerical stability and accuracy are also considered. The computational cost and storage requirements needed to numerically solve each of the models are also discussed.
In this article is presented a very simple and effective analog spiking neural network simulator, realized with an event-driven method, taking into account a basic biological neuron parameter: the spike latency. Also, other fundamentals biological parameters are considered, such as subthreshold decay and refractory period. This model allows to synthesize neural groups able to carry out some substantial functions. The proposed simulator is applied to elementary structures, in which some properties and interesting applications are discussed, such as the realization of a Spiking Neural Network Classifier.
Implementation of Spiking Neural Network Izhikevich Model for Hand Written Character Recognition
2015
Neural network concepts and principles appear to have great potential for solving problems arising in pattern recognition. Over the last decade, various spiking neural network models have been proposed, along with a similarly increasing interest in spiking models of computation in computational neuroscience. In this paper, for Pattern Recognition of Handwritten Characters, Spiking Neural Network's Izhikevich Neuron Model is used. Here for large scale simulations of the Izhikevich model we explore the expediency of using FPGAs. It has been observed that due to the accuracy, efficiency, power and simulation time; the Izhikevich spiking neuron model is best suited for large scale simulations.
Spiking and Bursting Firing Patterns of a Compact VLSI Cortical Neuron Circuit
2007 International Joint Conference on Neural Networks, 2007
The paper presents a silicon neuron circuit that mimics the behaviour of known classes of biological neurons. The circuit has been designed in a 0.35µm CMOS technology. The firing patterns of basic cell classes: regular spiking (RS), fast spiking (FS), chattering (CH) and intrinsic bursting (IB) are obtained with a simple adjustment of two biasing voltages. The simulations reveal the potential of the circuit to provide a wide variety of cell behaviours with required accommodation and firing frequency of a given cell type. The neuron consumes only 14 MOSFETs enabling the integration of many neurons in a small silicon area. Hence, the circuit provides a foundation for designing massively parallel analogue neuromorphic networks that closely resemble the circuits of the cortex.
Neurocomputing, 2019
This study investigates the trade-off between computational efficiency and accuracy of Izhikevich neuron models by numerically quantifying their convergence to provide design guidelines in choosing the limit time steps during a discretization procedure. This is important for bionic engineering and neurorobotic applications where the use of embedded computational resources requires the introduction of optimality criteria. Specifically, the regular spiking (RS) and intrinsically bursting (IB) Izhikevich neuron models are evaluated with step inputs of various amplitudes. We analyze the convergence of spike sequences generated under different discretization time steps (10µs to 10ms), with respect to an ideal reference spike sequence approximated with a discretization time step of 1µs. The differences between the ideal reference and the computed spike sequences were quantified by Victor-Purpura (VPd) and van Rossum (VRd) distances. For each distance, we found two limit discretization times (dt 1 and dt 2), as a function of the applied input and thus firing rate, beyond which the convergence is lost for each neuron model.
This paper proposes a silicon neuron circuit which uses a slow-variable controlled leakage term to extend the repertoire of spiking patterns achievable in an integrate and fire model. The simulations reveal the potential of the circuit to provide a wide variety of neuron firing patterns observed in neocortex, including adapting and non-adapting, regular spiking, fast spiking, bursting, chattering, etc. The firing patterns of basic cell classes are obtained with a simple adjustment of four biasing voltages. The circuit operates in the sub-threshold regime, with time constants similar to biological neurons, and hence is suitable for use in systems requiring such operating speeds. Envisaged applications of the proposed circuit are in large-scale analogue VLSI systems for spiking neural network simulations, brain-inspired circuits for robotics and hybrid silicon/biology systems.
A log-domain implementation of the Izhikevich neuron model
2010
We present an electronic neuron that uses first-order log-domain low-pass filters to implement the Mihalas-Niebur model. The neuron consists of a leaky-integrate-and-fire core and building blocks to implement an adaptive threshold and spike induced currents. Simulation results show that this modular neuron can emulate different spiking behaviours observed in biological neurons.
Effects of the parameters on the oscillation frequency of Izhikevich spiking neural networks
Neurocomputing, 2019
Computational neuroscience attempts to understand the nervous system functions by using realistic models in large-scale simulations. This work is inspired by the Theory of Neural Group Selection (TNGS) and by the theory of Central Pattern Generators (CPGs). The TNGS states that neural connection topology generates strongly connected neuronal groups which are the smallest functional unit of the nervous system responsible for the most basic processing activities. The CPGs are neuronal circuits, located in the spinal cord of vertebrate animals, responsible for breathing, rhythm generation, motor behaviors, as well as other oscillatory functions. In this work, the oscillatory dynamics of neuronal groups and CPGs is modeled by using spiking neural networks.