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Found 14 papers, 4 papers with code
BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks
We propose a biologically plausible gradient-based learning algorithm for SNN that is functionally equivalent to backprop, while adhering to all three neuromorphic principles.
Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity
With a top accuracy of 97. 61% on MNIST, 97. 51% on N-MNIST, and 85. 84% on Fashion-MNIST, NALSM achieved comparable performance to current fully-connected multi-layer spiking neural networks trained via backpropagation.
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
Here, we propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL).
An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi
Locomotion is a crucial challenge for legged robots that is addressed "effortlessly" by biological networks abundant in nature, named central pattern generators (CPG).
Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware
Here, we propose a neuromorphic approach that combines the energy-efficiency of spiking neural networks with the optimality of DRL and benchmark it in learning control policies for mapless navigation.
Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots
Although cognitive engagement (CE) is crucial for motor learning, it remains underutilized in rehabilitation robots, partly because its assessment currently relies on subjective and gross measurements taken intermittently.
A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head
Robotic vision introduces requirements for real-time processing of fast-varying, noisy information in a continuously changing environment.
Deep Learning of Movement Intent and Reaction Time for EEG-informed Adaptation of Rehabilitation Robots
Here, we propose a deep convolutional neural network (CNN) that uses electroencephalography (EEG) as an objective measurement of two kinematics components that are typically used to assess motor learning and thereby adaptation: i) the intent to initiate a goal-directed movement, and ii) the reaction time (RT) of that movement.
Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
While there is still a lot to learn about astrocytes and their neuromodulatory role in the spatial and temporal integration of neuronal activity, their introduction to neuromorphic hardware is timely, facilitating their computational exploration in basic science questions as well as their exploitation in real-world applications.
Axonal Conduction Velocity Impacts Neuronal Network Oscillations
Increasing experimental evidence suggests that axonal action potential conduction velocity is a highly adaptive parameter in the adult central nervous system.
Computational Astrocyence: Astrocytes encode inhibitory activity into the frequency and spatial extent of their calcium elevations
Deciphering the complex interactions between neurotransmission and astrocytic $Ca^{2+}$ elevations is a target promising a comprehensive understanding of brain function.
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM
We performed comparative analyses for accuracy and energy-efficiency between our neuromorphic approach and the GMapping algorithm, which is widely used in small environments.
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior.
The Causal Role of Astrocytes in Slow-Wave Rhythmogenesis: A Computational Modelling Study
Finding the origin of slow and infra-slow oscillations could reveal or explain brain mechanisms in health and disease.
