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20152022
most citedDeep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning

163 citations · 218 across the 17 of their papers we have counts for

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6 papers · 1 filter

cs.NE20223 cited

Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions

Arindam Basu, Charlotte Frenkel, Lei Deng +1

In this paper, we reviewed Spiking neural network (SNN) integrated circuit designs and analyzed the trends among mixed-signal cores, fully digital cores and large-scale, multi-core…

cs.NE20213 cited

Prospects for Analog Circuits in Deep Networks

Shih-Chii Liu, John Paul Strachan, Arindam Basu

Operations typically used in machine learning al-gorithms (e.g. adds and soft max) can be implemented bycompact analog circuits. Analog Application-Specific Integrated Circuit (ASI…

cs.NE2019

Spiking Neural Network based Region Proposal Networks for Neuromorphic Vision Sensors

Jyotibdha Acharya, Vandana Padala, Arindam Basu

This paper presents a three layer spiking neural network based region proposal network operating on data generated by neuromorphic vision sensors. The proposed architecture consist…

cs.NE2018

Power efficient Spiking Neural Network Classifier based on memristive crossbar network for spike sorting application

Anand Kumar Mukhopadhyay, Indrajit Chakrabarti, Arindam Basu +1

In this paper authors have presented a power efficient scheme for implementing a spike sorting module. Spike sorting is an important application in the field of neural signal acqui…

cs.NE2016

An Online Structural Plasticity Rule for Generating Better Reservoirs

Subhrajit Roy, Arindam Basu

In this article, a novel neuro-inspired low-resolution online unsupervised learning rule is proposed to train the reservoir or liquid of Liquid State Machine. The liquid is a spars…

cs.NE2015

Learning Spike time codes through Morphological Learning with Binary Synapses

Subhrajit Roy, Phyo Phyo San, Shaista Hussain +2

In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses that is able to learn temporal features of spike input patterns is considered. Since binary synapses are…