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In addition, the new version permits specifying probe trials among standard trials and extracting their values. This paper introduces a compound-based simulator of the Temporal Difference model that implements new algorithms to work with contexts, stimulus compounds, configural cues, and different time-steps.

In addition, the simulator allows the input of universal trial based designs and computes and displays numerical and graphical outputs per trial and per stimulus component, as well as simulated responses. Simulated results can be directly exported to a data processor spreadsheet. In the present special issue, the performance of current computational models of classical conditioning was evaluated under three requirements: 1 Models were to be tested against a list of previously agreed-upon phenomena; 2 the parameters were fixed across simulations; and 3 the simulations used to test the models had to be made available.

These requirements resulted in three major products: a a list of fundamental classical-conditioning results for which there is a consensus about their reliability; b the necessary information to evaluate each of the models on the basis of its ordinal successes in accounting for the experimental data; and c a repository of computational models ready to generate simulations. We believe that the contents of this issue represent the state of the art in computational modeling of classical conditioning and provide a way to find promising avenues for future model development.

It is able to run whole experimental designs, and compute and display the associative values of elemental and compound stimuli simultaneously, as well as use extra configural cues in generating compound values; it also permits change of the US parameters across phases. The simulator produces both numerical and graphical outputs, and includes a functionality to export the results to a data processor spreadsheet. The book " The significance of this book lies in its theoretical advances, which are grounded in an understanding of computational and biological learning.

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The approach taken moves the entire field closer to a watershed moment of learning models, through the interaction of computer science, psychology and neurobiology. A double error dynamic asymptote model of associative learning.

The models can reproduce and predict experimental results under different conditions. Explanations for the observed behaviors can be derived from the observation of the model variables in a given simulated experiment. Skip to main content Skip to table of contents. Encyclopedia of the Sciences of Learning Edition.

Contents Search. Computational Models of Classical Conditioning. How to cite. This is a preview of subscription content, log in to check access. Blough, D. Steady state data and a quantitative model of operant generalization and discrimination. Google Scholar. Buhusi, C. Attention, configuration, and hippocampal function. Hippocampus, 6 , — Timing in simple conditioning and occasion setting: A neural network approach.

Behavioral Processes, 45 , 33— Bush, R. Stochastic models for learning. New York: Wiley. Denniston, J.

A Rescorla-Wagner drift-diffusion model of conditioning and timing

The extended comparator hypothesis: learning by contiguity, responding by relative strength. Klein Eds. Mahwah: Lawrence Erlbaum. Desmond, J. Adaptive timing in neural models: The conditioned response. Biological Cybernetics, 58 , — Dickinson, A. Within-compound associations mediate the retrospective revaluation of causality judgments. Quarterly Journal of Experimental Psychology, 49B , 60— Gelperin, A. The logic of Limax learning. Selverston Ed. New York: Plenum. Gluck, M.

Original Articles

A biologically realistic network model of acquisition and extinction of conditioned fear associations in lateral amygdala neurons. Assignment of model amygdala neurons to the fear memory trace depends on competitive synaptic interactions. Synaptic competition in the lateral amygdala and the stimulus specificity of conditioned fear: a biophysical modeling study. Armony, J. An anatomically constrained neural network model of fear conditioning. Stimulus generalization of fear responses: effects of auditory cortex lesions in a computational model and in rats. Cortex 7 , — Edeline, J.

Associative retuning in the thalamic source of input to the amygdala and auditory cortex: receptive field plasticity in the medial division of the medial geniculate body. Rapid development of learning-induced receptive field plasticity in the auditory cortex. Bordi, F. Single-unit activity in the lateral nucleus of the amygdala and overlying areas of the striatum in freely behaving rats: Rates, discharge patterns, and responses to acoustic stimuli.

Krasne, F.

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Design of a neurally plausible model of fear learning. Ball, J. Role of sensory input distribution and intrinsic connectivity in lateral amygdala during auditory fear conditioning: a computational study. Neuroscience , — Repa, J. Two different lateral amygdala cell populations contribute to the initiation and storage of memory. Vlachos, I. Context-dependent encoding of fear and extinction memories in a large-scale network model of the basal amygdala. PLoS Comput. Herry, C. Switching on and off fear by distinct neuronal circuits.

Amano, T. The fear circuit revisited: contributions of the basal amygdala nuclei to conditioned fear. Impact of infralimbic inputs on intercalated amygdala neurons: a biophysical modeling study. Tuunanen, J. Do seizures cause neuronal damage in rat amygdala kindling? Epilepsy Res. Faber, E. Morphological and electrophysiological properties of principal neurons in the rat lateral amygdala in vitro. Sah, P. The amygdaloid complex: anatomy and physiology.

Samson, R. A spatially structured network of inhibitory and excitatory connections directs impulse traffic within the lateral amygdala.

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Johnson, L. Regulation of the fear network by mediators of stress: norepinephrine alters the balance between cortical and subcortical afferent excitation of the lateral amygdala.

Mahanty, N. Calcium-permeable AMPA receptors mediate long-term potentiation in interneurons in the amygdala. Fanselow, M. Why we think plasticity underlying Pavlovian fear conditioning occurs in the basolateral amygdala. Neuron 23 , — Han, J. Neuronal competition and selection during memory formation.

Rumpel, S. Postsynaptic receptor trafficking underlying a form of associative learning. Science , 83—88 Selective erasure of a fear memory.