Heterogeneous neuronal and synaptic dynamics for spike-efficient unsupervised learning: Theory and design principles
B Chakraborty, S Mukhopadhyay - arXiv preprint arXiv:2302.11618, 2023 - arxiv.org
This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the
spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction …
spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction …
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 …
A brain-inspired model for multi-step forecasting of malignant arrhythmias
YK Kim, I Choi, SJ Lee, HB Shin, GC Kim… - Expert Systems with …, 2025 - Elsevier
Malignant arrhythmias (MA), stemming from abnormalities in the neuronal signaling of the
cardiac muscle, necessitate sophisticated predictive models for effective clinical …
cardiac muscle, necessitate sophisticated predictive models for effective clinical …
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 …