Dynamic Stochastic Synapses as Computational Units

By Wolfgang Maass, Anthony M. Zador. 1997

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Citations in the corpus

Herding Dynamic Weights for Partially Observed Random Field Models (2009, background): “In [10] we proposed a new perspective on “learning” fully observed MRF models that draws inspiration from findings in neuroscience, namely that synapses change their efficacy on short time scales [5].”
Herding dynamical weights to learn (2009, unknown): “( Maass & Zador, 1998; Pantic et al., 2002; Levina et al., 2007).”
Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. (2001, result): “based on the recent activation history of the synapse, In a departure from the common view that memories the straightforward mapping of stable numerical weights are encoded in the overall connection strengths befrom a connectionist learning system onto synapses in tween neurons, our results suggest ”
Evolving Spiking Networks with Variable Resistive Memories (2014, background): “Probabilistic spike emission, which is governed by modulatory Hebbian rules, has also been investigated (Maass and Zador, 1999).”
Evolution of Plastic Learning in Spiking Networks via Memristive Connections (2012, methodology): “In [39], spike transmission from synapse to neuron is probabilistic, with heterogeneous probabilities throughout the network.”
Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks (2023, background): “Such conditions signify deficient or absent metaplastic control, as observed with traditional two-variable nonstochastic Hebbian-type rules [54,56,93], that plague natural and artificial macroscale networks with excessive synaptic densities, insufficient information-storage capacities, and numerous ”