Search Results for author:
Found 8 papers, 4 papers with code
Gradient-based methods for spiking physical systems
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico.
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles.
Learning efficient backprojections across cortical hierarchies in real time
Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas.
Latent Equilibrium: Arbitrarily fast computation with arbitrarily slow neurons
The response time of physical computational elements is finite, and neurons are no exception.
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
The response time of physical computational elements is finite, and neurons are no exception.
The Yin-Yang dataset
The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks.
Fast and energy-efficient neuromorphic deep learning with first-spike times
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance.
An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites
This paper presents an extension of the BrainScaleS accelerated analog neuromorphic hardware model.
