Computational Models of the Neural Bases of Learning and Memory

Gluck, Mark A.; Granger, Richard

doi:10.1146/annurev.ne.16.030193.003315

Cited by 90 publications

(44 citation statements)

References 127 publications

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“…Moreover, the physiological impact of requiring trials to occur during theta makes the obtained behavioral results quite consistent with what has been predicted by some models of septohippocampal modulation of learning (51,53). Thus, current and prior neurophysiological findings lend support to models that would predict the presence of theta during each trial in the present study should have beneficial effects on behavioral learning, perhaps by means of hippocampal modulation of activity in other areas (e.g., cerebellum) known to be essential for elaboration of an adaptive conditioned response (50,54). The productive exchange of predictions and empirical data between developers of connectionist͞computational models and laboratories studying the hippocampus and eyeblink conditioning can greatly accelerate our understanding of the neurobiology of learning and memory.…”

Section: Discussionsupporting

confidence: 91%

“…Comprehensive theories of brain function (especially those explaining learning and memory) must continue to incorporate the significance of oscillatory potentials such as hippocampal theta and gamma (49)(50)(51)(52)(53) and the cortical gamma that has been related to perceptual binding and use-dependent plasticity (55). Our results demonstrate that it is possible to index optimal levels of such activity in a behaviorally significant manner.…”

Section: Discussionmentioning

confidence: 78%

“…[49][50][51][52][53]. In general, theta periodicities are thought to produce (or result from) neurobiological processes that play a strong, facilitatory role in neural and behavioral plasticity.…”

Section: Discussionmentioning

confidence: 99%

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Oscillatory brain states and learning: Impact of hippocampal theta-contingent training

Seager

Johnson

Chabot

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

186

185

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Eyeblink classical conditioning is a relatively simple form of associative learning that has become an invaluable tool in our understanding of the neural mechanisms of learning. When studying rabbits in this paradigm, we observed a dramatic modification of learning rate by conducting training during episodes of either hippocampal theta or hippocampal non-theta activity as determined by on-line slow-wave spectral analysis. Specifically, if animals were given trials only when a computer analysis verified a predominance of slow-wave oscillations at theta frequencies (3-8 Hz), they learned in half as many trials as animals trained during non-theta hippocampal activity (58 vs. 115). This finding provides important evidence from awake, behaving animals that supports recent advances in our knowledge of (i) brain sites and neurobiological mechanisms of learning and memory, specifically hippocampus and theta oscillations, (ii) the biological plausibility of current models of hippocampal function that posit important roles for oscillatory potentials, and (iii) the design of interfaces between biological and cybernetic (electronic) systems that can optimize cognitive processes and performance.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 78%

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Oscillatory brain states and learning: Impact of hippocampal theta-contingent training

Seager

Johnson

Chabot

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

186

185

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“…In summary, these findings extend early views in the probabilistic domain that the hippocampus is involved in decreasing attention to unimportant events (Douglas, 1967) and further support the idea that it can produce increases in attention to relevant stimuli (Pearce and Hall, 1980). This general computation of cue-outcome uncertainty may represent the underlying mechanism responsible for the involvement of the hippocampus in associative learning, probabilistic classification (Squire and Zola, 1996), binding of stimulus elements (Gluck and Granger, 1993), and transitive inference (Dusek and Eichenbaum, 1997;Frank et al, 2003). Indeed, in all these hippocampus-dependent functions, the encoding of item relationships is based on the strength of their associations, which can be efficiently computed by their degree of uncertainty.…”

supporting

confidence: 80%

The Hippocampus Codes the Uncertainty of Cue–Outcome Associations: An Intracranial Electrophysiological Study in Humans

Vanni-Mercier¹,

Mauguı̀ere²,

Isnard³

et al. 2009

J. Neurosci.

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Learning to predict upcoming outcomes based on environmental cues is essential for adaptative behavior. In monkeys, midbrain dopaminergic neurons code two statistical properties of reward: a prediction error at the outcome and uncertainty during the delay period between cues and outcomes. Although the hippocampus is sensitive to reward processing, and hippocampal-midbrain functional interactions are well documented, it is unknown whether it also codes the statistical properties of reward information. To address this question, we recorded local field potentials from intracranial electrodes in human hippocampus while subjects learned to associate cues of slot machines with various monetary reward probabilities (P). We found that the amplitudes of negative event-related potentials covaried with uncertainty at the outcome, being maximal for P ϭ 0.5 and minimal for P ϭ 0 and P ϭ 1, regardless of winning or not. These results show that the hippocampus computes an uncertainty signal that may constitute a fundamental mechanism underlying the role of this brain region in a number of functions, including attention-based learning, associative learning, probabilistic classification, and binding of stimulus elements.

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“…We are presently applying a neural net model, based on 'delta rule' classical conditioning, to test for habituation or extinction processes in our respiratory associative conditioning paradigms (see Gluck & Granger, 1993).…”

Section: Department Of Of Leeds Leedsmentioning

confidence: 99%

Respiratory Control: From Mechanics to Models

1998

The Journal of Physiology

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Computational Models of the Neural Bases of Learning and Memory

Cited by 90 publications

References 127 publications

Oscillatory brain states and learning: Impact of hippocampal theta-contingent training

Oscillatory brain states and learning: Impact of hippocampal theta-contingent training

The Hippocampus Codes the Uncertainty of Cue–Outcome Associations: An Intracranial Electrophysiological Study in Humans

Respiratory Control: From Mechanics to Models

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