Bridging Neuroscience and Machine Learning with Spiking Neural Networks

Decoding and Understanding Muscle Activity and Kinematics

The human brain's exceptional efficiency in performing intricate cognitive tasks is attributed to the communication of neurons through brief, rapid-fire electrical pulses known as spikes. This spiking behavior has inspired the development of artificial neural networks that seek to mimic the brain's computational capabilities.

Artificial Neurons for Thresholding and Self-Organization

Researchers have designed an artificial neuron based on a novel material known as NbOx volatile memristor. This neuron exhibits thresholding behavior similar to biological neurons, enabling it to respond selectively to specific input signals. Additionally, different artificial neurons can self-organize to form complex neural circuits, just as in biological systems.

Spiking Neural Networks for Cognitive Tasks

Spiking neural networks (SNNs) aim to bridge the gap between neuroscience and machine learning by incorporating biologically realistic models of neurons. These networks employ spiking neuron dynamics to process information, offering potential advantages in areas such as pattern recognition, decision-making, and cognitive computing.

Applications in Decoding Muscle Activity

SNNs have shown promise in decoding muscle activity and kinematics. By analyzing spike patterns, these networks can extract meaningful information from electromyography (EMG) signals, which measure electrical activity in muscles. This capability enables the development of advanced prosthetic devices and rehabilitation technologies.