research
∙
05/23/2023
Understanding and Improving Optimization in Predictive Coding Networks
Backpropagation (BP), the standard learning algorithm for artificial neu...
share
research
∙
03/21/2023
Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control
Transformers are state-of-the-art networks for most sequence processing ...
research
∙
06/01/2022
A Theoretical Framework for Inference Learning
Backpropagation (BP) is the most successful and widely used algorithm in...
research
∙
05/18/2022
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability
Although deep Reinforcement Learning (RL) has proven successful in a wid...
research
∙
01/26/2022
Meta-learning Spiking Neural Networks with Surrogate Gradient Descent
Adaptive "life-long" learning at the edge and during online task perform...
research
∙
09/27/2021
Training Spiking Neural Networks Using Lessons From Deep Learning
The brain is the perfect place to look for inspiration to develop more e...
research
∙
04/30/2021
Tightening the Biological Constraints on Gradient-Based Predictive Coding
Predictive coding (PC) is a general theory of cortical function. The loc...
research
∙
04/29/2021
Hessian Aware Quantization of Spiking Neural Networks
To achieve the low latency, high throughput, and energy efficiency benef...
research
∙
03/31/2021
Gesture Similarity Analysis on Event Data Using a Hybrid Guided Variational Auto Encoder
While commercial mid-air gesture recognition systems have existed for at...
research
∙
02/20/2021
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference
Many real-world mission-critical applications require continual online l...
research
∙
02/10/2021
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
Despite the recent success of deep reinforcement learning (RL), domain a...
research
∙
08/03/2020
Online Few-shot Gesture Learning on a Neuromorphic Processor
We present the Surrogate-gradient Online Error-triggered Learning (SOEL)...
research
∙
10/27/2019
Inherent Weight Normalization in Stochastic Neural Networks
Multiplicative stochasticity such as Dropout improves the robustness and...
research
∙
10/11/2019
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Recent work suggests that synaptic plasticity dynamics in biological mod...
research
∙
04/09/2019
Embodied Event-Driven Random Backpropagation
Spike-based communication between biological neurons is sparse and unrel...
research
∙
04/09/2019
Embodied Neuromorphic Vision with Event-Driven Random Backpropagation
Spike-based communication between biological neurons is sparse and unrel...
research
∙
11/27/2018
Synaptic Plasticity Dynamics for Deep Continuous Local Learning
A growing body of work underlines striking similarities between spiking ...
research
∙
06/19/2018
Contrastive Hebbian Learning with Random Feedback Weights
Neural networks are commonly trained to make predictions through learnin...
research
∙
09/29/2017
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Embedded, continual learning for autonomous and adaptive behavior is a k...
research
∙
12/16/2016
Neuromorphic Deep Learning Machines
An ongoing challenge in neuromorphic computing is to devise general and ...
research
∙
09/27/2016
Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
Current large scale implementations of deep learning and data mining req...
research
∙
07/11/2016
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
Spike-timing-dependent plasticity (STDP) incurs both causal and acausal ...
research
∙
01/16/2016
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
In recent years the field of neuromorphic low-power systems that consume...
research
∙
01/16/2016
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth
We present an approach to constructing a neuromorphic device that respon...
research
∙
12/23/2014
Learning Non-deterministic Representations with Energy-based Ensembles
The goal of a generative model is to capture the distribution underlying...
research
∙
11/05/2013