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Found 33 papers, 9 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Evolving to learn: discovering interpretable plasticity rules for spiking networks
Continuous adaptation allows survival in an ever-changing world.
Neurons and Cognition
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.
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.
Natural-gradient learning for spiking neurons
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen parametrization of the weights.
Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming
We formulate the search for phenomenological models of synaptic plasticity as an optimization problem.
The Yin-Yang dataset
The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks.
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.
Learning cortical representations through perturbed and adversarial dreaming
We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs).
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.
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.
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.
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.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
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.
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.
Gradient-based methods for spiking physical systems
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico.
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.
Emulating insect brains for neuromorphic navigation
Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment.
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.
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.
