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Found 12 papers, 6 papers with code
Backpropagation through space, time, and the brain
In particular, GLE exploits the ability of biological neurons to phase-shift their output rate with respect to their membrane potential, which is essential in both directions of information propagation.
Learning beyond sensations: how dreams organize neuronal representations
However, brains are known to generate virtual experiences, such as during imagination and dreaming, that go beyond previously experienced inputs.
Three-Dimensional Coherent Diffractive Imaging of Isolated Faceted Nanostructures
The structure and dynamics of isolated nanosamples in free flight can be directly visualized via single-shot coherent diffractive imaging using the intense and short pulses of X-ray free-electron lasers.
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.
A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease.
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.
Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring
With growing network size a compute node receives spikes from an increasing number of different source neurons until in the limit each synapse on the compute node has a unique source.
Learning cortical representations through perturbed and adversarial dreaming
We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs).
Conductance-based dendrites perform Bayes-optimal cue integration
We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration.
Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming
We formulate the search for phenomenological models of synaptic plasticity as an optimization problem.
Evolving to learn: discovering interpretable plasticity rules for spiking networks
Continuous adaptation allows survival in an ever-changing world.
Neurons and Cognition
