Tests on a cell assembly theory of the action of the brain, using a large digital computer

By N. Rochester, John H. Holland, L. Haibt, W. Duda. 1956

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, methodology): “We distinguish between models with instantaneous algorithmic re-normalization of the weights [4, 5, 7, 50] and models which proportionally scale synaptic weights in an activity”
A History of Spike-Timing-Dependent Plasticity (2011, unknown): “The co-variance rule was implemented by calculating (x−x̄)(y−ȳ) std(x−x̄)std(y−ȳ) where std is the standard deviation (Rochester et al., 1956).”
Models of Metaplasticity: A Review of Concepts (2015, background): “It has been long known that Hebbian plasticity alone is intrinsically unstable (Rochester et al., 1956; von der Malsburg, 1973; Miller, 1996).”
Merging chrominance and luminance in early, medium, and late fusion using Convolutional Neural Networks (2015, methodology): “Duda made another simulation of a neural network on the IVM Type 704 Electronic Calculator [Rochester and Holland, 1956].”
Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. (2016, methodology): “Specifically, our machinelearning approach integrated 3 separate approaches into 1 algorithm: support vector machines (13), artificial neural networks (14), and random forests (15).”
Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex (2013, background): “Unless it is constrained, Hebbian learning will lead to everincreasing (and thus unstable) values of the weights (Rochester et al., 1956).”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, result): “Similar to previous plasticity models (Rochester et al., 1956; Bienenstock et al., 1982; Gerstner et al., 1996; Song et al., 2000; Fusi, 2002; Pfister and Gerstner, 2006; Morrison et al., 2008), coactivation of presynaptic and postsynaptic neurons induces synaptic memory traces.”
Artificial Neural Network (2020, unknown): “Frank Rosenblatt([5]) (1958) created the perceptron, an algorithm for pattern recognition based on a two-layer computer learning network using simple addition and subtraction.”
Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks (2015, unknown): “þ dSjðtÞ: transmitter induced ð14Þ The first two expressions (Expressions (11) and (12)) correspond to the published triplet STDP model30.”
The Role of Constraints in Hebbian Learning (1994, background): “Von der Malsburg (1973) proposed the use of constraints conserving the total synaptic strength supported by each input or output cell to achieve selectivity; related proposals were also made by others (Perez et al., 1975; Rochester et al., 1956; Rosenblatt, 1961).”
Deep Learning for Single Image Super-Resolution: A Brief Review (2018, background): “Early ANNs can be traced back to perceptron algorithms in the 1960s [33].”