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Found 6 papers, 1 papers with code
Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match.
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain
Bayesian interpretations of neural processing require that biological mechanisms represent and operate upon probability distributions in accordance with Bayes' theorem.
Locally Learned Synaptic Dropout for Complete Bayesian Inference
The random failure of presynaptic vesicles to release neurotransmitters may allow the brain to sample from posterior distributions of network parameters, interpreted as epistemic uncertainty.
Using noise to probe recurrent neural network structure and prune synapses
Here we suggest that noise could play a functional role in synaptic pruning, allowing the brain to probe network structure and determine which synapses are redundant.
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes
Neural network models of memory and error correction famously include the Hopfield network, which can directly store---and error-correct through its dynamics---arbitrary N-bit patterns, but only for ~N such patterns.
Associative content-addressable networks with exponentially many robust stable states
The brain must robustly store a large number of memories, corresponding to the many events encountered over a lifetime.
