Search Results for author:
Found 27 papers, 6 papers with code
A method for the ethical analysis of brain-inspired AI
The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.
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.
DELAUNAY: a dataset of abstract art for psychophysical and machine learning research
This dataset provides a middle ground between natural images and artificial patterns and can thus be used in a variety of contexts, for example to investigate the sample efficiency of humans and artificial neural networks.
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.
Variational learning of quantum ground states on spiking neuromorphic hardware
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states.
Learning cortical representations through perturbed and adversarial dreaming
We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs).
Learning Bayes-optimal dendritic opinion pooling
The biophysics of the membrane combines these opinions by taking account their reliabilities, and the soma thus acts as a decision maker.
The Yin-Yang dataset
The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks.
Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming
We formulate the search for phenomenological models of synaptic plasticity as an optimization problem.
Natural-gradient learning for spiking neurons
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen parametrization of the weights.
Spiking neuromorphic chip learns entangled quantum states
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years.
Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately.
Evolving to learn: discovering interpretable plasticity rules for spiking networks
Continuous adaptation allows survival in an ever-changing world.
Neurons and Cognition
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control.
Structural plasticity on an accelerated analog neuromorphic hardware system
In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations.
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency.
Stochasticity from function -- why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.
Accelerated physical emulation of Bayesian inference in spiking neural networks
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices.
Spiking neurons with short-term synaptic plasticity form superior generative networks
In this work, we use networks of leaky integrate-and-fire neurons that are trained to perform both discriminative and generative tasks in their forward and backward information processing paths, respectively.
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks.
Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware
How spiking networks are able to perform probabilistic inference is an intriguing question, not only for understanding information processing in the brain, but also for transferring these computational principles to neuromorphic silicon circuits.
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate.
Stochastic inference with spiking neurons in the high-conductance state
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro.
The high-conductance state enables neural sampling in networks of LIF neurons
The core idea of our approach is to separately consider two different "modes" of spiking dynamics: burst spiking and transient quiescence, in which the neuron does not spike for longer periods.
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 ever increasing demand for computational resources for their simulation.
Stochastic inference with deterministic spiking neurons
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference.
