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The development of inhibitory learning and memory in hooded and albino rats
Behavioural Brain Research, 1983A special kind of passive avoidance was elaborated which made it possible to study inhibitory conditioning in hooded (Long-Evans) and albino (Wistar) rat strains from day of birth to 10 or 11 days of age. Remaining on the safe platform for 60 sec, thus avoiding an electrified grid, for the 1st and 2nd time were the criteria.
J, Myslivecek, J, Hassmannová
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Improving efficiency in sparse learning with the feedforward inhibitory motif
Neurocomputing, 2017Abstract Neuro-inspired computing has made significant progress in recent years. However, its computation efficiency and hardware cost still lag behind the biological nervous system, especially during the training stage. This work targets to understand this gap from a neural motif perspective, particularly the feedforward inhibitory motif.
Zihan Xu +7 more
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Role of hippocampal NO in the acquisition and consolidation of inhibitory avoidance learning
NeuroReport, 1995Nitric oxide (NO), an unconventional neurotransmitter in the brain, has been postulated as a retrograde intercellular messenger necessary for the induction, but not the maintenance phase, of activity-dependent forms of synaptic plasticity in the hippocampus.
R, Bernabeu +4 more
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Inhibitory Learning for Anxiety-Related Disorders
Cognitive and Behavioral Practice, 2019Abstract This article reviews the articles in this issue that describe the strategies derived from the inhibitory learning model as applied to exposure therapy for anxiety disorders. The major principles of inhibitory learning are to create and strengthen nonthreat associations in memory (largely by engaging prefrontal cortical regions), and to ...
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Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning
Neural Computation, 2001Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Error-driven backpropagation is computationally powerful and has proven useful for modeling a range of psychological data but is not biologically plausible.
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Machine Learning in Epigenetic Diseases
Medical Epigenetics, 2021Robert E Akins, Karyn Robinson
exaly
Backward learning and the stimulus-familiarization inhibitory-effect.
Journal of Experimental Psychology, 1964S, SIMON, G, WOOD
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