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Found 9 papers, 5 papers with code
Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks
Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.
Self-Supervised Learning Through Efference Copies
Specifically, the brain also transforms the environment through efference, i. e. motor commands, however it sends to itself an EC of the full commands, i. e. more than a mere SSL sign.
Hebbian Deep Learning Without Feedback
Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain.
Short-Term Plasticity Neurons Learning to Learn and Forget
Its key mechanism is that synapses have a state, propagated through time by a self-recurrent connection-within-the-synapse.
Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks
We demonstrate these in two toy problems of sequence classification, and in a temporally-encoded MNIST dataset where our RC model achieves 99. 19% accuracy after the first input time-step, outperforming the state of the art in temporal coding with SNNs, as well as in spoken-word classification of Google Speech Commands, outperforming non-RC-trained early inference with LSTMs.
SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networks
All in all, Hebbian efficiency, theoretical underpinning, cross-entropy-minimization, and surprising empirical advantages, suggest that SoftHebb may inspire highly neuromorphic and radically different, but practical and advantageous learning algorithms and hardware accelerators.
Optimality of short-term synaptic plasticity in modelling certain dynamic environments
Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication and local synaptic plasticity.
File Classification Based on Spiking Neural Networks
In this paper, we propose a system for file classification in large data sets based on spiking neural networks (SNNs).
Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes
However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstructed by coarser correlations between firing rates.
