79 citations · 106 across the 6 of their papers we have counts for
6 papers
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
Priyadarshini Panda, Narayan Srinivasa
A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to rec…
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition
Nitin Rathi, Priyadarshini Panda, Kaushik Roy
Spiking Neural Networks (SNNs) with a large number of weights and varied weight distribution can be difficult to implement in emerging in-memory computing hardware due to the limit…
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
Syed Shakib Sarwar, Priyadarshini Panda, Kaushik Roy
Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a wide range of classification and recognition problems. However, training these large networ…
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy
Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficie…
Perovskite Quantum Organismoids
Fan Zuo, Priyadarshini Panda, Michele Kotiuga +14
A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making1-3. This behavior, known as h…
RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks
Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda +1
Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computin…