Deep learning in spiking neural networks

Locally connected spiking neural networks for unsupervised feature learning.

Neuromorphic utilizes VLSI technology to implement a brain-inspired architecture. Recently, a brain-inspired associative memory with large capacity and robust retrieval, known as columnar-organized memory (COM), has been introduced. COM is a combination of spiking winner-take-all (WTA) units, which is inspired by the cortex structure. In this paper, a hardware architecture of COM is proposed that is presented at three levels of design. At the level I, a low-power circuit of a leaky integrate and fire neuron is introduced that is compatible with the architecture of COM. At the level II, the assembly of the proposed neuron and a single memristor crossbar array are used to implement a WTA module. At the level III, a COM hardware architecture is developed using the combination of the WTA modules and memristor crossbar arrays. The ex situ method is utilized to train the COM hardware. The simulations are performed at all design levels. First, the power consumption of the neuron circuit is evaluated. It consumes 4.3 pJ/spike, and its static power is 182 pW. Second, the operation of the WTA module is deliberated. Finally, the operation of the COM hardware for message storage and retrieval is evaluated in the presence of the hardware imperfections.

A Hardware Architecture for Columnar-Organized Memory Based on CMOS Neuron and Memristor Crossbar Arrays

In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by \textit{locally connected layers} in SNNs using spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks compete via competitive inhibitory interactions to learn features from different locations of the input space. These \textit{Locally-Connected SNNs} (LC-SNNs) manifest key topological features of the spatial interaction of biological neurons. We explore biologically inspired n-gram classification approach allowing parallel processing over various patches of the the image space. We report the classification accuracy of simple two-layer LC-SNNs on two image datasets, which match the state-of-art performance and are the first results to date. LC-SNNs have the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Robustness tests demonstrate that LC-SNNs exhibit graceful degradation of performance despite the random deletion of large amounts of synapses and neurons.

pdf/locally-connected-spiking-neural-networks-for-unsupervised-21dzwaiw2w.pdf

Locally Connected Spiking Neural Networks for Unsupervised Feature Learning

The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

/pdf/toward-reflective-spiking-neural-networks-exploiting-3e6bphr1.pdf

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

This paper reports the results of experiments to develop a minimal neural network for pattern classification. The network uses biologically plausible neural and learning mechanisms and is applied to a subset of the MNIST dataset of handwritten digits. The research goal is to assess the classification power of a very simple biologically motivated mechanism. The network architecture is primarily a feedforward spiking neural network (SNN) composed of Izhikevich regular spiking (RS) neurons and conductance-based synapses. The weights are trained with the spike timing-dependent plasticity (STDP) learning rule. The proposed SNN architecture contains three neuron layers which are connected by both static and adaptive synapses. Visual input signals are processed by the first layer to generate input spike trains. The second and third layers contribute to spike train segmentation and STDP learning, respectively. The network is evaluated by classification accuracy on the handwritten digit images from the MNIST dataset. The simulation results show that although the proposed SNN is trained quickly without error-feedbacks in a few number of iterations, it results in desirable performance (97.6%) in the binary classification (0 and 1). In addition, the proposed SNN gives acceptable recognition accuracy in 10-digit (0-9) classification in comparison with statistical methods such as support vector machine (SVM) and multi-perceptron neural network.

/pdf/a-minimal-spiking-neural-network-to-rapidly-train-and-53aybwjb1f.pdf

A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks

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A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks