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A recent paper and patent claims to have found a method of finding a threshold logic solution for all linearly separable Boolean functions. Although the method appears to work, one step of the method has not previously been proven. This paper gives a proof that the method does work.
As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these...
In this paper we present an implementation of and a proposed algorithm for an easily expandable hardware Artificial Neural Network (ANN) capable of learning using inexpensive, off-the-shelf microprocessors. While significant work has been done in hardware ANN implementations, this research offers a unique, general use, unspecialized, and inexpensive model with a flexible architecture representation...
Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapse-astrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows...
Deep learning neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, in spite of achieving high accuracy in such classification problems, they involve significant computational resources. Over the past few years, artificial neural network models have...
The hardware implementation of neural network models allows to efficiently exploit their inherent parallelism. Here, we focus on the Liquid State Machine (LSM) methodology to build recurrent Spiking Neural Networks (SNN), particularly suited to process time-dependent signals. We propose a low cost hardware implementation of LSM networks based on the use of stochastic computing (SC) concepts. The functionality...
The Liquid State Machine (LSM) exploits the computation capability of recurrent spiking neural networks by incorporating a randomly generated reservoir, which is often fixed. This standard choice relaxes the challenging need for training the complex recurrent reservoir. The fixed reservoir is used as a generic kernel to map the temporal input signals to the internal network dynamics, and a readout...
Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model artificial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware...
A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to...
Dynamic neural field (DNF) is a popular mesoscopic model for cortical column interactions. It is widely studied analytically and successfully applied to physiological modelling, bioinspired computation and robotics. DNF behavior emerges from distributed and decentralized interactions between computing units which makes it an interesting candidate as a cellular building-block for unconventional computations...
This paper introduces a novel design of phase locked loop (PLL) based oscillatory neural networks (ONNs) to mitigate the frequency clustering phenomenon caused by transmission delays in real systems. Theoretical analysis of the ONN reveals that transmission delays can produce frequency clustering that leads to synchronization and convergence failure. This paper describes the redesign of ONN dynamics...
Spiking neural networks with hardware implementations of Spike Timing Dependent Plasticity (STDP) present a promising solution to energy efficient real-time machine learning. Online real-time learning, however, requires that new training information be considered by an already trained network without reinforcing previous data. Learning new information without severely altering previously learned data...
We present an approach to constructing a neuromorphic device that responds to language input by producing neuron spikes in proportion to the strength of the appropriate positive or negative emotional response. Specifically, we perform a fine-grained sentiment analysis task with implementations on two different systems: one using conventional spiking neural network (SNN) simulators and the other one...
Physically and computationally efficient hardware coupled with fast sparse approximation solvers provide opportunities for real-time visual processing on low-power embedded platforms. This paper presents a system using the low-power Locally Competitive Algorithm (LCA) on the highly programmable, brain-inspired IBM TrueNorth chip. A small-scale spiking LCA network is successfully implemented on the...
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