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08/29/2023
Gradient-based methods for spiking physical systems
Recent efforts have fostered significant progress towards deep learning ...
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08/03/2023
Learning beyond sensations: how dreams organize neuronal representations
Semantic representations in higher sensory cortices form the basis for r...
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05/18/2023
A method for the ethical analysis of brain-inspired AI
Despite its successes, to date Artificial Intelligence (AI) is still cha...
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12/20/2022
Learning efficient backprojections across cortical hierarchies in real time
Models of sensory processing and learning in the cortex need to efficien...
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01/28/2022
DELAUNAY: a dataset of abstract art for psychophysical and machine learning research
Image datasets are commonly used in psychophysical experiments and in ma...
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10/27/2021
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
The response time of physical computational elements is finite, and neur...
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09/30/2021
Variational learning of quantum ground states on spiking neuromorphic hardware
Recent research has demonstrated the usefulness of neural networks as va...
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09/09/2021
Memory semantization through perturbed and adversarial dreaming
Classical theories of memory consolidation emphasize the importance of r...
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02/16/2021
The Yin-Yang dataset
The Yin-Yang dataset was developed for research on biologically plausibl...
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02/08/2021
Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming
We formulate the search for phenomenological models of synaptic plastici...
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11/23/2020
Natural-gradient learning for spiking neurons
In many normative theories of synaptic plasticity, weight updates implic...
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08/03/2020
Spiking neuromorphic chip learns entangled quantum states
Neuromorphic systems are designed to emulate certain structural and dyna...
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12/30/2019
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
We present first experimental results on the novel BrainScaleS-2 neuromo...
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12/27/2019
Structural plasticity on an accelerated analog neuromorphic hardware system
In computational neuroscience, as well as in machine learning, neuromorp...
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11/08/2018
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain'...
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09/21/2018
Stochasticity from function - why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variabil...
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07/06/2018
Generative models on accelerated neuromorphic hardware
The traditional von Neumann computer architecture faces serious obstacle...
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09/24/2017
Spiking neurons with short-term synaptic plasticity form superior generative networks
Spiking networks that perform probabilistic inference have been proposed...
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03/17/2017
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artific...
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03/12/2017
Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware
How spiking networks are able to perform probabilistic inference is an i...
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03/06/2017
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
Emulating spiking neural networks on analog neuromorphic hardware offers...
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10/23/2016
Stochastic inference with spiking neurons in the high-conductance state
The highly variable dynamics of neocortical circuits observed in vivo ha...
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01/05/2016
The high-conductance state enables neural sampling in networks of LIF neurons
The apparent stochasticity of in-vivo neural circuits has long been hypo...
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04/29/2014
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Advancing the size and complexity of neural network models leads to an e...
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11/13/2013