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Found 6 papers, 3 papers with code
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference
We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference.
Inherent Weight Normalization in Stochastic Neural Networks
Multiplicative stochasticity such as Dropout improves the robustness and generalizability of deep neural networks.
Contrastive Hebbian Learning with Random Feedback Weights
It operates in two phases, the forward (or free) phase, where the data are fed to the network, and a backward (or clamped) phase, where the target signals are clamped to the output layer of the network and the feedback signals are transformed through the transpose synaptic weight matrices.
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware.
Neuromorphic Deep Learning Machines
Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations in neuromorphic computing hardware.
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation.
