Search Results for author:
Found 14 papers, 4 papers with code
An Integrated Toolbox for Creating Neuromorphic Edge Applications
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, transformer models and generative AI.
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability
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.
Edelman's Steps Toward a Conscious Artifact
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 a Conscious Artifact.
Dynamic Reliability Management in Neuromorphic Computing
We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems, by designing an intelligent run-time manager (NCRTM), which dynamically destresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target.
Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment
Animals ranging from rats to humans can demonstrate cognitive map capabilities.
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source domain based on LUSR in the second stage.
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations.
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture
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).
Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation
In the present paper, we take inspiration from the serotonergic system and apply it to the task of robot navigation.
Mapping Spiking Neural Networks to Neuromorphic Hardware
SpiNePlacer then finds the best placement of local and global synapses on the hardware using a meta-heuristic-based approach to minimize energy consumption and spike latency.
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO).
Neuromodulated Goal-Driven Perception in Uncertain Domains
In uncertain domains, the goals are often unknown and need to be predicted by the organism or system.
Making BREAD: Biomimetic strategies for Artificial Intelligence Now and in the Future
First, that scientific societies and governments coordinate Biomimetic Research for Energy-efficient, AI Designs (BREAD); a multinational initiative and a funding strategy for investments in the future integrated design of energetics into AI.
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization.
