Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data

@article{Hazan2019LatticeMS,
  title={Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data},
  author={Hananel Hazan and Daniel J. Saunders and Darpan T. Sanghavi and Hava T. Siegelmann and Robert Thijs Kozma},
  journal={Annals of Mathematics and Artificial Intelligence},
  year={2019},
  volume={88},
  pages={1237 - 1260}
}
Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a self-organized grid of filters via cooperative and competitive excitatory-inhibitory interactions. Several inhibition strategies are developed and tested, such as (i) incrementally increasing inhibition level over the course of network… 
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15 Citations

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The degree of self-repair that can be enabled in such networks with varying degree of faults ranging from 50 to 90% is characterized and the proposal is evaluated on the MNIST and Fashion-MNIST datasets.

Spiking Neural Networks and Their Applications: A Review

A comprehensive review of theories of biological neurons, which have been studied in neuroscience, is given and existing spiking neural network applications in computer vision and robotics domains are covered.

lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents

The experimental results show that the lpSpikeCon methodology can reduce weight memory of the SNN model by 8x and achieve no accuracy loss in the inference phase, as compared to the baseline model with 32-bit weights across different network sizes.

FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks.

FSpiNN is an optimization framework for obtaining memory- and energy-efficient SNNs for training and inference processing, with unsupervised learning capability while maintaining accuracy, by reducing the computational requirements of neuronal and STDP operations, improving the accuracy of STDP-based learning, compressing the SNN through a fixed-point quantization, and incorporating the memory and energy requirements in the optimization process.

Q-SpiNN: A Framework for Quantizing Spiking Neural Networks

The Q-SpiNN is proposed, a novel quantization framework for memory-efficient SNNs that employs quantization for different SNN parameters based on their significance to the accuracy, and develops an algorithm that quantifies the benefit of the memory-accuracy trade-off obtained by the candidates, and selects the Pareto-optimal one.

TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity

The TripleBrain hardware core is proposed, that tightly combines three common brain-inspired factors: the spike-based processing and plasticity, the self-organizing map (SOM) mechanism and the reinforcement learning scheme, to improve object recognition accuracy and processing throughput, while keeping low resource costs.

Memory via Temporal Delays in weightless Spiking Neural Network

A prototype for weightless spiking neural networks that can perform a simple classification task and is trained using a Hebbian Spike Timing Dependent Plasticity (STDP), which modulates the delays of the connection.

tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks

The tinySNN effectively compresses the given SNN model to achieve high accuracy in a memory- and energy-efficient manner, hence enabling the employment of SNNs for the resource-and energy-constrained embedded applications.

A Spiking Neural Network Based Auto-encoder for Anomaly Detection in Streaming Data

It is shown that SNNs are well suited for detecting anomalous character sequences, that they can learn rapidly, and that there are many optimizations to the SNN architecture and training that can improve AD performance.

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