Bioinspired low-energy identification of stochastic dynamical systems based on hybrid spiking neural networks

The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks

Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity. However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neuron...

Design and Verification of Adaptive LIF Neurons: From Single-Neuron Dynamics to Multi-Neuron Spiking Networks

This work contributes a 2nd-order adaptive LIF neuron with two-stage synaptic filtering for richer temporal dynamics; a fully connected six-neuron spiking network with configurable weights demonstrating weight-based inter-neuron communication; and a direct verification methodology enabling per-cycle observation of all...

Efficient Spiking Neural Network Prototype on FPGA for Classifying Gases

This work presents a discrete-time SNN design using machine learning tools within the PyTorch framework that can efficiently classify industrial gases and demonstrates its performance in terms of energy efficiency in a classification task scheme.

Stabilizing Linear Time-Invariant Systems with Recurrent Spiking Neural Networks

Spiking Neural Networks for Efficient Classification of Peripheral Nerve Stimuli

Accurate electroneurographic (ENG) signal classification is a key function in biological and neural communication systems, where peripheral nerves act as noisy, bandwidth-limited information channels interfacing with implantable bioelectronic devices. In neural decoding and stimulation (ND&S) systems, ENG signals are t...

Investigation of efficiency of Liquid State Machine spiking neural networks in chaotic time series forecasting using software simulation methods

The subject of the study is the application of spiking neural networks of the Liquid State Machine (LSM) type to the problem of chaotic time series forecasting. The objective is to establish a theoretical and software foundation for modeling the LSM utilizing the BindsNET library, alongside performing a reproducible co...