Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing
B Chakraborty, S Mukhopadhyay - arXiv preprint arXiv:2407.06452, 2024 - arxiv.org
Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing,
promising energy-efficient and biologically plausible models for complex tasks. This paper …
promising energy-efficient and biologically plausible models for complex tasks. This paper …
Dive into the power of neuronal heterogeneity
The biological neural network is a vast and diverse structure with high neural heterogeneity.
Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weights of …
Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weights of …
Adaptive spiking neuron with population coding for a residual spiking neural network
Y Dan, C Sun, H Li, L Meng - Applied Intelligence, 2025 - Springer
Spiking neural networks (SNNs) have attracted significant research attention due to their
inherent sparsity and event-driven processing capabilities. Recent studies indicate that the …
inherent sparsity and event-driven processing capabilities. Recent studies indicate that the …
Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning
Spiking neural networks (SNNs) are investigated as biologically inspired models of neural
computation, distinguished by their computational capability and energy efficiency due to …
computation, distinguished by their computational capability and energy efficiency due to …
Brain-inspired spiking neural network for online unsupervised time series prediction
B Chakraborty, S Mukhopadhyay - 2023 International Joint …, 2023 - ieeexplore.ieee.org
Energy and data-efficient online time series prediction for predicting evolving dynamical
systems are critical in several fields, especially edge AI applications that need to update …
systems are critical in several fields, especially edge AI applications that need to update …
Brain-Inspired Spatiotemporal Processing Algorithms for Efficient Event-Based Perception
Neuromorphic event-based cameras can unlock the true potential of bio-plausible sensing
systems that mimic our human perception. However, efficient spatiotemporal processing …
systems that mimic our human perception. However, efficient spatiotemporal processing …
Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks
B Chakraborty, S Mukhopadhyay - arXiv preprint arXiv:2403.12462, 2024 - arxiv.org
Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and
artificial intelligence, providing brain-inspired computation. Recent advances in literature …
artificial intelligence, providing brain-inspired computation. Recent advances in literature …
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network
depth increases, leading to homogenized node representations and a loss of …
depth increases, leading to homogenized node representations and a loss of …