Show Reference: "Design of a Neurally Plausible Model of Fear Learning"

Design of a Neurally Plausible Model of Fear Learning Frontiers in Behavioural Neuroscience, Vol. 5, No. 41. (2011) by Franklin B. Krasne, Michael S. Fanselow, Moriel Zelikowsky
@article{krasne-et-al-2011,
    abstract = {A neurally oriented conceptual and computational model of fear conditioning manifested by freezing behavior ({FRAT}), which accounts for many aspects of delay and context conditioning, has been constructed. Conditioning and extinction are the result of neuromodulation-controlled {LTP} at synapses of thalamic, cortical, and hippocampal afferents on principal cells and inhibitory interneurons of lateral and basal amygdala. The phenomena accounted for by the model (and simulated by the computational version) include conditioning, secondary reinforcement, blocking, the immediate shock deficit, extinction, renewal, and a range of empirically valid effects of pre- and post-training ablation or inactivation of hippocampus or amygdala nuclei.},
    author = {Krasne, Franklin B. and Fanselow, Michael S. and Zelikowsky, Moriel},
    citeulike-article-id = {9671460},
    citeulike-linkout-0 = {http://www.frontiersin.org/behavioral\_neuroscience/10.3389/fnbeh.2011.00041/abstract},
    journal = {Frontiers in Behavioural Neuroscience},
    keywords = {cognitive-model, fear, learning, model},
    number = {41},
    posted-at = {2014-07-24 13:28:46},
    priority = {2},
    title = {Design of a Neurally Plausible Model of Fear Learning},
    url = {http://www.frontiersin.org/behavioral\_neuroscience/10.3389/fnbeh.2011.00041/abstract},
    volume = {5},
    year = {2011},
    editor = {Kim, Jeansok J.},
    month = {June},
    day = {26}
}

See the CiteULike entry for more info, PDF links, BibTex etc.

Krasne et al. present an ANN model for fear conditioning.

Krasne et al. distinguish between 'top-down' and 'bottom-up' models: Top-down models are designed to explain phenomenology. Bottom-up models are constructed from knowledge about low-level features of the object being modeled.

Bottom-up models can be used to test whether we already have most of the important features of the object being modeled; if the phenomenology is right, then probably we have, otherwise there's something missing.

Top-down mechanisms help us understand and interpret the phenomenology we see in the object being modeled.

Top-down and bottom-up models are complementary.