Search Results for author:
Found 20 papers, 8 papers with code
Order from chaos: Interplay of development and learning in recurrent networks of structured neurons
By applying a fully local, always-on plasticity rule we are able to learn complex sequences in a recurrent network comprised of two populations.
Confidence and second-order errors in cortical circuits
Minimization of cortical prediction errors has been considered a key computational goal of the cerebral cortex underlying perception, action and learning.
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.
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.
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.
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.
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.
Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks
This reduced system combines the essential elements to have a working biologically abstracted analogue of backpropagation with a simple formulation and proofs of the associated results.
Dendritic cortical microcircuits approximate the backpropagation algorithm
Deep learning has seen remarkable developments over the last years, many of them inspired by neuroscience.
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.
Dendritic error backpropagation in deep cortical microcircuits
Animal behaviour depends on learning to associate sensory stimuli with the desired motor command.
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.
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values.
Sequence learning with hidden units in spiking neural networks
We consider a statistical framework in which recurrent networks of spiking neurons learn to generate spatio-temporal spike patterns.
