Article Instance

HTTP 200 OK
Allow: GET
Content-Type: application/json
Vary: Accept

{
    "pk": 31353,
    "title": "Self-Organization of Auditory Motion Detectors",
    "subtitle": null,
    "abstract": "This work addresses the question of how neural networks self-organise to recognize familiar sequential patterns. A neural network model with mild constraints on its initial architecture learns to encode the direction of spectral motion as auditory stimuli excite the units in a tonotopically arranged input layer like that found after peripheral processing by the cochlea. The network consists of a series of inhibitory clusters with excitatory interconnections that self-organize as streams of stimuli excite the clusters over time. Self-organization is achieved by application of the learning heuristics developed by Marshall (1990^ for the self-organization of excitatory and inhibitory pathways in visual motion detection. These heuristics are implemented through linear thresholding equations for unit activation having faster-than-linear inhibitory response. Synaptic weights are learned throughout processing according to the competitive algorithm explored in Malsburg (1973).",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Posters",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/1xg3m4s8",
    "frozenauthors": [
        {
            "first_name": "Sven",
            "middle_name": "E.",
            "last_name": "Anderson",
            "name_suffix": "",
            "institution": "Indiana University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1992-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/31353/galley/22422/download/"
        }
    ]
}