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Found 7 papers, 1 papers with code
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.
Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training
Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems.
Sequence learning in a spiking neuronal network with memristive synapses
In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model.
Coherent noise enables probabilistic sequence replay in spiking neuronal networks
Animals rely on different decision strategies when faced with ambiguous or uncertain cues.
Sequence learning, prediction, and replay in networks of spiking neurons
These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences.
On transfer learning using a MAC model variant
We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with a simplified set of equations that achieves comparable accuracy, while training faster.
Learning to Remember, Forget and Ignore using Attention Control in Memory
Typical neural networks with external memory do not effectively separate capacity for episodic and working memory as is required for reasoning in humans.
