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Found 8 papers, 1 papers with code
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research.
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.
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.
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.
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.
