89 citations · 180 across the 28 of their papers we have counts for
29 papers
Inferring continuous cognitive traits from limited neuroimaging data using pairwise convolutional neural networks
Marjan Rashidi, Robert Bain, Craig Stark +3
Individual differences in cognitive abilities vary widely, yet the structural brain features supporting these differences remain poorly understood. Traditional brain-behavior analy…
Full Feature Spiking Neural Network Simulation on Micro-Controllers for Neuromorphic Applications at the Edge
L. Niedermeier, J. L. Krichmar
Microcontroller units (MCU), which have an order of magnitude lower Size, Weight and Power (SWaP) than standard computers, makes them suitable for applications at the edge. Neuromo…
A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications
Lars Niedermeier, Vyom Shah, Jeffrey L. Krichmar
Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables ne…
STRMs: Spatial Temporal Reasoning Models for Vision-Based Localization Rivaling GPS Precision
Hin Wai Lui, Jeffrey L. Krichmar
This paper explores vision-based localization through a biologically-inspired approach that mirrors how humans and animals link views or perspectives when navigating their world. W…
A Rapid Adapting and Continual Learning Spiking Neural Network Path Planning Algorithm for Mobile Robots
Harrison Espino, Robert Bain, Jeffrey L. Krichmar
Mapping traversal costs in an environment and planning paths based on this map are important for autonomous navigation. We present a neurobotic navigation system that utilizes a Sp…
An Integrated Toolbox for Creating Neuromorphic Edge Applications
Lars Niedermeier, Jeffrey L. Krichmar
Spiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biological realism than the activation functions typically used in deep neural networks, tra…
A Sparse Quantized Hopfield Network for Online-Continual Memory
Nick Alonso, Jeff Krichmar
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-indep…
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…
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…
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…
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…
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…
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…
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
Jinwei Xing, Takashi Nagata, Kexin Chen +3
Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real…
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
Adarsha Balaji, Shihao Song, Anup Das +5
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is because the…
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
Adarsha Balaji, Prathyusha Adiraju, Hirak J. Kashyap +4
We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two k…
Thermodynamic Computing
Tom Conte, Erik DeBenedictis, Natesh Ganesh +36
The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now u…
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Adarsha Balaji, Shihao Song, Anup Das +4
Neuromorphic hardware with non-volatile memory (NVM) can implement machine learning workload in an energy-efficient manner. Unfortunately, certain NVMs such as phase change memory…
Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation
Jinwei Xing, Xinyun Zou, Jeffrey L. Krichmar
Robots and self-driving vehicles face a number of challenges when navigating through real environments. Successful navigation in dynamic environments requires prioritizing subtasks…
Mapping Spiking Neural Networks to Neuromorphic Hardware
Adarsha Balaji, Anup Das, Yuefeng Wu +7
Neuromorphic hardware platforms implement biological neurons and synapses to execute spiking neural networks (SNNs) in an energy-efficient manner. We present SpiNeMap, a design met…
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture
Pawel Ladosz, Eseoghene Ben-Iwhiwhu, Jeffery Dick +6
This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA). The hy…
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Hirak J Kashyap, Charless Fowlkes, Jeffrey L Krichmar
Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO). Another issue with monocular VO is the scale ambiguity, i.e. these…
Attention-Based Structural-Plasticity
Soheil Kolouri, Nicholas Ketz, Xinyun Zou +2
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learni…
Neuromodulated Goal-Driven Perception in Uncertain Domains
Xinyun Zou, Soheil Kolouri, Praveen K. Pilly +1
In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive excitation backprop (c-EB) was used in a goal-drive…