[
    {
        "id": "authors:rq268-a6536",
        "collection": "authors",
        "collection_id": "rq268-a6536",
        "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20111201-143851335",
        "type": "article",
        "title": "Disambiguating Different Covariation Types",
        "author": [
            {
                "family_name": "Brody",
                "given_name": "Carlos D.",
                "clpid": "Brody-C-D"
            }
        ],
        "abstract": "Covariations in neuronal latency or excitability can lead to peaks in spike train covariograms that may be very similar to those caused by spike timing synchronization (see companion article). Two quantitative methods are described here. The first is a method to estimate the excitability component of a covariogram, based on trial-by-trial estimates of excitability. Once estimated, this component may be subtracted from the covariogram, leaving only other types of contributions. The other is a method to determine whether the covariogram could potentially have been caused by latency covariations.",
        "doi": "10.1162/089976699300016124",
        "issn": "0899-7667",
        "publisher": "MIT Press",
        "publication": "Neural Computation",
        "publication_date": "1999-10-01",
        "series_number": "7",
        "volume": "11",
        "issue": "7",
        "pages": "1527-1535"
    },
    {
        "id": "authors:10ecx-gkf14",
        "collection": "authors",
        "collection_id": "10ecx-gkf14",
        "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20111201-144231336",
        "type": "article",
        "title": "Correlations Without Synchrony",
        "author": [
            {
                "family_name": "Brody",
                "given_name": "Carlos D.",
                "clpid": "Brody-C-D"
            }
        ],
        "abstract": "Peaks in spike train correlograms are usually taken as indicative of spike timing synchronization between neurons. Strictly speaking, however, a peak merely indicates that the two spike trains were not independent. Two biologically plausible ways of departing from independence that are capable of generating peaks very similar to spike timing peaks are described here: covariations over trials in response latency and covariations over trials in neuronal excitability. Since peaks due to these interactions can be similar to spike timing peaks, interpreting a correlogram may be a problem with ambiguous solutions. What peak shapes do latency or excitability interactions generate? When are they similar to spike timing peaks? When can they be ruled out from having caused an observed correlogram peak? These are the questions addressed here. The previous article in this issue proposes quantitative methods to tell cases apart when latency or excitability covariations cannot be ruled out.",
        "doi": "10.1162/089976699300016133",
        "issn": "0899-7667",
        "publisher": "MIT Press",
        "publication": "Neural Computation",
        "publication_date": "1999-10-01",
        "series_number": "7",
        "volume": "11",
        "issue": "7",
        "pages": "1537-1551"
    },
    {
        "id": "authors:c2ta7-2ts83",
        "collection": "authors",
        "collection_id": "c2ta7-2ts83",
        "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20160223-165608841",
        "type": "book_section",
        "title": "Computing with Action Potentials",
        "book_title": "Advances in Neural Information Processing Systems 10 (NIPS 1997)",
        "author": [
            {
                "family_name": "Hopfield",
                "given_name": "John J.",
                "clpid": "Hopfield-J-J"
            },
            {
                "family_name": "Brody",
                "given_name": "Carlos D.",
                "clpid": "Brody-C-D"
            },
            {
                "family_name": "Roweis",
                "given_name": "Sam",
                "clpid": "Roweis-S"
            }
        ],
        "contributor": [
            {
                "family_name": "Jordan",
                "given_name": "Michael I.",
                "clpid": "Jordan-M-I"
            },
            {
                "family_name": "Kearns",
                "given_name": "Michael J.",
                "clpid": "Kearns-M-J"
            },
            {
                "family_name": "Solla",
                "given_name": "Sara A.",
                "clpid": "Solla-S-A"
            }
        ],
        "abstract": "Most computational engineering based loosely on biology uses continuous variables to represent neural activity. Yet most neurons communicate with action potentials. The engineering view is equivalent to using a rate-code for representing information and for computing. An increasing\nnumber of examples are being discovered in which biology may not be using rate codes. Information can be represented using the timing of action potentials, and efficiently computed with in this representation. The \"analog match\" problem of odour identification is a simple problem\nwhich can be efficiently solved using action potential timing and an underlying rhythm. By using adapting units to effect a fundamental change of representation of a problem, we map the recognition of words (having uniform time-warp) in connected speech into the same analog match\nproblem. We describe the architecture and preliminary results of such a recognition system. Using the fast events of biology in conjunction with an underlying rhythm is one way to overcome the limits of an event-driven view of computation. When the intrinsic hardware is much faster\nthan the time scale of change of inputs, this approach can greatly increase the effective computation per unit time on a given quantity of hardware.",
        "isbn": "0-262-10076-2",
        "publisher": "MIT Press",
        "place_of_publication": "Cambridge, MA",
        "publication_date": "1998",
        "pages": "166-172"
    },
    {
        "id": "authors:5n9me-g0407",
        "collection": "authors",
        "collection_id": "5n9me-g0407",
        "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20160203-164747715",
        "type": "book_section",
        "title": "A Model of Feedback to the Lateral Geniculate Nucleus",
        "book_title": "Advances in Neural Information Processing Systems 5 (NIPS 1992)",
        "author": [
            {
                "family_name": "Brody",
                "given_name": "Carlos D.",
                "clpid": "Brody-C-D"
            }
        ],
        "contributor": [
            {
                "family_name": "Hanson",
                "given_name": "Stephen Jos\u00e9",
                "clpid": "Hanson-S-J"
            },
            {
                "family_name": "Cowan",
                "given_name": "Jack D.",
                "clpid": "Cowan-J-D"
            },
            {
                "family_name": "Giles",
                "given_name": "C. Lee",
                "clpid": "Giles-C-L"
            }
        ],
        "abstract": "Simplified models of the lateral geniculate nucles (LGN) and striate cortex illustrate the possibility that feedback to the LGN may be used for robust, low-level pattern analysis. The information fed back to the LGN is rebroadcast to cortex using the LGN's full fan-out, so the cortex-LGN-cortex pathway mediates extensive cortico-cortical communication while keeping the number of necessary\nconnections small.",
        "isbn": "1-55860-274-7",
        "publisher": "Morgan Kaufmann",
        "place_of_publication": "San Mateo, CA",
        "publication_date": "1993",
        "pages": "409-416"
    }
]