activity
20172022
most citedVisual response properties of MSTd emerge from a sparse population code

42 citations · 64 across the 10 of their papers we have counts for

collaborators

18 papers

cs.LG2022

Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability

Jinwei Xing, Takashi Nagata, Xinyun Zou +2

Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency m…

q-bio.NC2021

Edelman's Steps Toward a Conscious Artifact

Jeffrey L. Krichmar

In 2006, during a meeting of a working group of scientists in La Jolla, California at The Neurosciences Institute (NSI), Gerald Edelman described a roadmap towards the creation of…

cs.NE2021

Dynamic Reliability Management in Neuromorphic Computing

Shihao Song, Jui Hanamshet, Adarsha Balaji +5

Neuromorphic computing systems uses non-volatile memory (NVM) to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate NVMs cause…

cs.NE2021

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks

Adarsha Balaji, Shihao Song, Twisha Titirsha +6

Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequent…

cs.NE2021

Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware

Twisha Titirsha, Shihao Song, Anup Das +4

Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient…

cs.NE2021

Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment

Xinyun Zou, Eric O. Scott, Alexander B. Johnson +4

Animals ranging from rats to humans can demonstrate cognitive map capabilities. We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary…