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نتیجه جستجو - Computational neuroscience

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 FPGA Realization of Fractional Order Neuron
تحقق FPGA نورون مرتبه فراکسیون-2020
In this paper fractional Hindmarsh Rose (HR) neuron, which mimics several behaviors of a real biological neuron is implemented on field programmable gate array (FPGA). The re- sults show several differences in the dynamic characteristics of integer and fractional order Hindmarsh Rose neuron models. The integer order model shows only one type of firing characteristics when the parameters of model remains same. The fractional order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied. The firing frequency increases when the order of the frac- tional operator decreases. The fractional order is therefore key in determining the firing characteristics of biological neurons. To implement this neuron model first the digital re- alization of different fractional operator approximations are obtained, then the fractional integrator is used to obtain the low power and low cost hardware realization of fractional HR neuron. The fractional neuron model has been implemented on a low voltage and low power circuit and then compared with its integer counter part. The hardware is used to demonstrate the different dynamical behaviors of fractional HR neuron for different type of approximations obtained for fractional operator in this paper. A coupled network of frac- tional order HR neurons is also implemented. The results also show that synchronization between neurons increases as long as coupling factor keeps on increasing.
Keywords: Computational neuroscience | Fractional Hindmarsh Rose neuron (HR) | Fractional calculus | Fractional-operator | Field-programmable-gate-arrays (FPGA) | Synchronization
مقاله انگلیسی
2 Synchronization of Hindmarsh Rose Neurons
هماهنگ سازی از نورون های Hindmarsh Rose-2020
Modeling and implementation of biological neurons are key to the fundamental understanding of neural network architectures in the brain and its cognitive behavior. Synchronization of neuronal models play a significant role in neural signal processing as it is very difficult to identify the actual interaction between neurons in living brain. Therefore, the synchronization study of these neuronal architectures has received extensive attention from researchers. Higher biological accuracy of these neuronal units demands more computational overhead and requires more hardware resources for implementation. This paper presents a two coupled hardware implementation of Hindmarsh Rose neuron model which is mathematically simpler model and yet mimics several behaviors of a real biological neuron. These neurons are synchronized using an exponential function. The coupled system shows several behaviors depending upon the parameters of HR model and coupling function. An approximation of coupling function is also provided to reduce the hardware cost. Both simulations and a low cost hardware implementations of exponential synaptic coupling function and its approximation are carried out for comparison. Hardware implementation on field programmable gate array (FPGA) of approximated coupling function shows that the coupled network produces different dynamical behaviors with acceptable error. Hardware implementation shows that the approximated coupling function has significantly lower implementation cost. A spiking neural network based on HR neuron is also shown as a practical application of this coupled HR neural networks. The spiking network successfully encodes and decodes a time varying input.
Keywords: Computational neuroscience | Hindmarsh Rose neuron (HR) | Digital | Spiking Neural Networks (SNNs) | Field programmable gate arrays (FPGAs) | Nengo
مقاله انگلیسی
3 Dynamics of spiking map-based neural networks in problems of supervised learning
پویایی شبکه های عصبی مبتنی بر نقشه اسپایک در چالش ها و یادگیری نظارت شده-2020
Recurrent networks of artificial spiking neurons trained to perform target functions are a perspective tool for understanding dynamic principles of information processing in computational neuroscience. Here, we develop a system of this type based on a map-based model of neural activity allowing for producing various biologically relevant regimes. Target signals used to supervisely train the network are sinusoid functions of different frequencies. Impacts of individual neuron dynamics, coupling strength, network size and other key parameters on the learning error are studied. Our findings suggest, among others, that firing rate heterogeneity as well as mixing of spiking and nonspiking regimes of neurons comprising the network can improve its performance for a wider range of target frequencies. At a single neuron activity level, successful training gives rise to well separated domains with qualitatively different dynamics.
مقاله انگلیسی
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