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We present a parallelized and pipelined architecture for a generalized Laguerre-Volterra MIMO system to identify the time-varying neural dynamics underlying spike activities. The proposed architecture consists of a first stage containing a vector convolution and MAC (Multiply and Accumulation) component, a second stage containing a pre-threshold potential updating unit with an error approximation...
Field-programmable gate arrays (FPGAs) can provide an efficient programmable resource for implementing hardware-based spiking neural networks (SNN). In this paper we present a hardware-software design that makes it possible to simulate large-scale (2 million neurons) biologically plausible SNNs on an FPGA-based system. We have chosen three SNN models from the various models available in the literature,...
In this paper, we present an hardware implementation of spiking neural networks based on analog integrated circuits. These ICs compute in real-time a biologically realistic neuron models. Each integrated circuit includes five neurons and analog memory cells to set and store the conductance model parameters, and eventually optimize it to compensate the analog circuit variability. The circuits are embedded...
This paper presents a hardware based approach to simulate action potential of large numbers of somas within a biological neural network. At the proposed method multiple processors can work in parallel to increase processing power as required. The high speed pipelined architecture for each processor provides the computation speed of one soma per clock ratio and with multiple processors higher speeds...
In this paper, we present a multi-board system based on analog neuromimetic ICs. These ICs compute in realtime conductance-based models. These models are implemented in a modular architecture based on our analog IPs. Each IC includes five neurons and analog memory cells to set and store the conductance model parameters, and eventually optimize it to compensate the analog circuit variability. The circuits...
We describe the design and implementation of an FPGA-based architecture for real-time simulation of spiking neural networks that include gap junctions, a type of synapse not often used in neural models due to their high computational cost. Recent research suggests that electrical synapses or gap junctions play a role in synchronizing the activity of larger groups of neurons in the brain, and are potentially...
An FPGA-based systolic architecture for the high speed simulation of spiking neural networks is presented. The design is an implementation of Izhikevich's neuron model and employs optimizations for the typical case where neuron activity is low. Since execution time required is related to the activity level, performance of the design can be improved by an order of magnitude.
An accurate method for feature extraction based on FPGA (Field Programmable Gate Arrays) implementation is proposed in this paper. The specific application is offline Farsi handwritten digit recognition. The classification is based on a simple two layer MLP (Multi Layer Perceptron). This method of feature extraction is appropriate for FPGA implementation as it can be implemented only with add and...
Spiking neural networks (SNNs) are an emerging computing paradigm that attempt to model the biological functions of the human brain. However, as networks approach the biological scale with significantly large numbers of neurons, software simulations face the problem of scalability and increasing computation times. Thus, numerous researchers have targeted hardware implementations in an attempt to more...
Within the field of neural electrophysiology, there exists a divide between experimentalists and computational modellers. This is caused by the different spheres of expertise required to perform each discipline, as well as the differing resource requirements of the two parties. This paper considers several forms of hardware acceleration for implementation within a laboratory alongside time sensitive...
Artificial neural networks are a key tool for researchers attempting to understand and replicate the behaviour and intelligence found in biological neural networks. Software simulations offer great flexibility and the ability to select which aspects of biological networks to model, but are slow when operating on more complex biologically plausible models; while dedicated hardware solutions can be...
The China-Austria OnLine-Diagnosis Medical-Measurement Grid computing system (CAODMMG) consist of a special novel non-invasive medical measurement (NIMM) based on the combination of modern medical measurement, modern information theory and traditional Chinese medical theory, the Grid computing system equipped by the newly practically developed workflow engine (WEEP) and in-computer-embedded complete...
We propose a digital neuron model suitable for evolving and growing heterogeneous spiking neural networks on FPGAs by introducing a novel flexible dendrite architecture and the new PLAQIF (piecewise-linear approximation of quadratic integrate and fire) soma model. A network of 161 neurons and 1610 synapses was simulated, implemented, and verified on a Virtex-5 chip with 4210 times real-time speed...
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