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Synaptic Homeostasis and Restructuring across the Sleep-Wake CycleDeep LearningAttractor metabolic networksThe Role of Temporal Trends in Growing NetworksState-space analysis of time-varying higher-order spike correlation for multiple neural spike train dataOn criticality in high-dimensional data.Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.Resummed mean-field inference for strongly coupled data.Improving landscape inference by integrating heterogeneous data in the inverse Ising problem.Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppression.Learning with a network of competing synapses.Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neuronsProbabilistic inference in general graphical models through sampling in stochastic networks of spiking neuronsThe nature and role of cortical feedback in perception, imagery, and synesthesia.Charles Bonnet syndrome: evidence for a generative model in the cortex?Eye movement dynamics and cognitive self-organization in typical and atypical developmentNetwork Plasticity as Bayesian InferenceUncertain behaviours of integrated circuits improve computational performance.What can ecosystems learn? Expanding evolutionary ecology with learning theory.High-order feature-based mixture models of classification learning predict individual learning curves and enable personalized teachingCONDITIONAL DISTANCE CORRELATIONHierarchical model of natural images and the origin of scale invariance.Parallel distributed processing and neural networks: origins, methodology and cognitive functions.Marginal log-linear parameters for graphical Markov models.Intrinsic optimization using stochastic nanomagnets.A review of cell assemblies.Energy-Based Neural Networks as a Tool for Harmony-Based Virtual Screening.Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.Rationalizable irrationalities of choice.Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states.The Sonda Project: prevention, prediction, and psychological disorder.Chaotic Boltzmann machines.The subjective meaning of cognitive architecture: a Marrian analysis.Fast classification of meat spoilage markers using nanostructured ZnO thin films and unsupervised feature learningInference of the sparse kinetic Ising model using the decimation method.Magnetoelectric oxide based stochastic spin device towards solving combinatorial optimization problemsHardware emulation of stochastic p-bits for invertible logicThe evolution of phenotypic correlations and "developmental memory".Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity.
P2860
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P2860
description
wetenschappelijk artikel
@nl
наукова стаття, опублікована в січні 1985
@uk
name
A Learning Algorithm for Boltzmann Machines
@en
A Learning Algorithm for Boltzmann Machines
@nl
type
label
A Learning Algorithm for Boltzmann Machines
@en
A Learning Algorithm for Boltzmann Machines
@nl
prefLabel
A Learning Algorithm for Boltzmann Machines
@en
A Learning Algorithm for Boltzmann Machines
@nl
P1433
P1476
A Learning Algorithm for Boltzmann Machines
@en
P2093
David H. Ackley
P304
P356
10.1207/S15516709COG0901_7
P577
1985-01-01T00:00:00Z