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Found 15 papers, 1 papers with code
jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware
Traditional neuromorphic hardware architectures rely on event-driven computation, where the asynchronous transmission of events, such as spikes, triggers local computations within synapses and neurons.
Emulating insect brains for neuromorphic navigation
Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment.
Gradient-based methods for spiking physical systems
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico.
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.
Event-based Backpropagation for Analog Neuromorphic Hardware
Building on this work has the potential to enable scalable gradient estimation in large-scale neuromorphic hardware as a continuous measurement of the system state would be prohibitive and energy-inefficient in such instances.
hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2
Neuromorphic systems require user-friendly software to support the design and optimization of experiments.
Using Forwards-Backwards Models to Approximate MDP Homomorphisms
Reinforcement learning agents must painstakingly learn through trial and error what sets of state-action pairs are value equivalent -- requiring an often prohibitively large amount of environment experience.
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research.
The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity
Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives.
Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks
Spiking neural networks combine analog computation with event-based communication using discrete spikes.
hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks.
Surrogate gradients for analog neuromorphic computing
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time.
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.
Neuromorphic Hardware learns to learn
In contrast, the hyperparameters and learning algorithms of networks of neurons in the brain, which they aim to emulate, have been optimized through extensive evolutionary and developmental processes for specific ranges of computing and learning tasks.
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.
