The organization of behavior.

By E. Capaldi. 1992

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “Many papers of the previous millennium, however, were about unsupervised learning (UL) without a teacher (e.g., Atick, Li, & Redlich, 1992; Baldi & Hornik, 1989; Barlow, Kaushal, & Mitchison, 1989; Barrow, 1987; Deco & Parra, 1997; Field, 1987; Földiák, 1990; Földiák & Young, 1995; Grossberg, 1976a,”
Memory Aware Synapses: Learning what (not) to forget (2017, background): “According to Hebbian theory [8], the process at the basis of this phenomenon is the strengthening of synapses connecting neurons that fire synchronously, compared to those connecting neurons with unrelated firing behavior.”
Selfless Sequential Learning (2018, methodology): “The authors also show that a local version of their method resembles applying Hebb rule [15] to consolidate the important parameters.”
Self-organizing neural integration of pose-motion features for human action recognition (2015, positive): “…plasticity in areas of the visual cortex, computational models may exhibit learning capabilities through the development of lateral connections between nodes governed by the principle formulated by Hebb (1949), in which nodes that are concurrently activated increase their synaptic strength.”
Reactivation of Hippocampal Cell Assemblies: Effects of Behavioral State, Experience, and EEG Dynamics (1999, background): “Associative synaptic plasticity (Hebb, 1949) may result in the formation of “cell assemblies” or attractors within the hippocampal formation (Marr, 1971; McNaughton and Morris, 1987; Treves and Rolls, 1994).”
What are the differences between long-term, short-term, and working memory? (2008, background): “One might relate short-term memory to a pattern of neural firing that represents a particular idea and one might consider the idea to be in shortterm memory only when the firing pattern, or cell assembly, is active (Hebb, 1949).”
Information Theory, Inference, and Learning Algorithms (2004, methodology): “A simple model due to Donald Hebb (1949) captures the idea of associative memory.”
Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism (2005, background): “Third, because DA modulates cellular excitability (Nicola, Surmeier, & Malenka, 2000), associative or ‘‘Hebbian’’ learning may be enhanced in the presence of DA, as this type of learning depends on the levels of activity of the cells in question (Schultz, 2002; Hebb, 1949).”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “, 2009) where an eligibility trace represents the Hebbian idea of co-activation of pre- and postsynaptic neurons (Hebb, 1949) while modulation of plasticity by additional gating signals is represented generically by a ’third factor’ (Crow, 1968; Barto, 1985; Legenstein et al.”
The time course of perceptual choice: the leaky, competing accumulator model. (2001, methodology): “We will adopt the dominant approach taken in computational neuroscience today, which follows the Hebbian perspective (Hebb, 1949).”
Sequence learning using the adaptive suffix trie algorithm (2012, methodology): “It states that " When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased " [17].”
Recent Advances in Transistor‐Based Artificial Synapses (2019, background): “The concept of synaptic plasticity is severely affected by Hebb’s postulate which states that continuous and repeated stimulation of presynaptic neurons to postsynaptic neurons can lead to an increase in synaptic transmission efficiency.[73] STDP further improves Hebb’s theory, pointing out that syn”
Quantum simulations with cold trapped ions (2009, methodology): “ond) it is not obvious how a set of J−couplings may be found that creates a minimum of HH for a desired pattern and thus makes the NN evolve towards this pattern. It turns out that using Hebb’s rule [Hebb 1949, Mezard et al. 1987] to determine the values of the couplings Jij gives just the right set”
Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules (2010, unknown): “The hypothesis that memory formation may correspond to changes in the connections between neurons dates back to Konorski, (1948), Hebb, (1949) and other work reviewed in Squire, (1987).”
The existence of implicit bias is beyond reasonable doubt: A refutation of ideological and methodological objections and executive summary of ten studies that no manager should ignore (2009, unknown): “The power of expectations (based on learned contingencies) to unwittingly affect basic perception, judgment, memory, and behavior had already been established in experiments that mark the advent of empirical psychology (e.g., Bartlett, 1932; Ebbinghaus, 1885/1913; Hebb, 1949; Stroop, 1935).”