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            "itemType": "journalArticle",
            "title": "Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Timothée",
                    "lastName": "Masquelier"
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                {
                    "creatorType": "author",
                    "firstName": "Etienne",
                    "lastName": "Hugues"
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                {
                    "creatorType": "author",
                    "firstName": "Gustavo",
                    "lastName": "Deco"
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                {
                    "creatorType": "author",
                    "firstName": "Simon J.",
                    "lastName": "Thorpe"
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            ],
            "abstractNote": "Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential—a phenomenon referred to as “phase-of-firing coding” (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions—only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents (∼10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.",
            "publicationTitle": "The Journal of Neuroscience",
            "publisher": "",
            "place": "",
            "date": "October 28 , 2009",
            "volume": "29",
            "issue": "43",
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            "partNumber": "",
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            "pages": "13484 -13493",
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            "DOI": "10.1523/JNEUROSCI.2207-09.2009",
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            "shortTitle": "Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity",
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            "title": "Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Silvia",
                    "lastName": "Scarpetta"
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                    "creatorType": "author",
                    "firstName": "Antonio de",
                    "lastName": "Candia"
                },
                {
                    "creatorType": "author",
                    "firstName": "Ferdinando",
                    "lastName": "Giacco"
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            ],
            "abstractNote": "We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate and fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre and postsynaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully connected networks, we study sparse networks, where each neuron is connected only to a small number z ≪ N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry.",
            "publicationTitle": "Frontiers in Synaptic Neuroscience",
            "publisher": "",
            "place": "",
            "date": "2010",
            "volume": "2",
            "issue": "",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "32",
            "series": "",
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            "journalAbbreviation": "Front. Syn. Neurosci.",
            "DOI": "10.3389/fnsyn.2010.00032",
            "citationKey": "scarpettaStoragePhasecodedPatterns2010",
            "url": "http://www.frontiersin.org/synaptic_neuroscience/10.3389/fnsyn.2010.00032/pdf/abstract",
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            "shortTitle": "Storage of phase-coded patterns via STDP in fully-connected and sparse network",
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            "extra": "00013",
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                    "tag": "Associative memory",
                    "type": 1
                },
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            "creatorSummary": "Beggs and Plenz",
            "parsedDate": "2003-12-03",
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            "itemType": "journalArticle",
            "title": "Neuronal Avalanches in Neocortical Circuits",
            "creators": [
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                    "creatorType": "author",
                    "firstName": "John M.",
                    "lastName": "Beggs"
                },
                {
                    "creatorType": "author",
                    "firstName": "Dietmar",
                    "lastName": "Plenz"
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            ],
            "abstractNote": "Networks of living neurons exhibit diverse patterns of activity, including oscillations, synchrony, and waves. Recent work in physics has shown yet another mode of activity in systems composed of many nonlinear units interacting locally. For example, avalanches, earthquakes, and forest fires all propagate in systems organized into a critical state in which event sizes show no characteristic scale and are described by power laws. We hypothesized that a similar mode of activity with complex emergent properties could exist in networks of cortical neurons. We investigated this issue in mature organotypic cultures and acute slices of rat cortex by recording spontaneous local field potentials continuously using a 60 channel multielectrode array. Here, we show that propagation of spontaneous activity in cortical networks is described by equations that govern avalanches. As predicted by theory for a critical branching process, the propagation obeys a power law with an exponent of -3/2 for event sizes, with a branching parameter close to the critical value of 1. Simulations show that a branching parameter at this value optimizes information transmission in feedforward networks, while preventing runaway network excitation. Our findings suggest that \"neuronal avalanches\" may be a generic property of cortical networks, and represent a mode of activity that differs profoundly from oscillatory, synchronized, or wave-like network states. In the critical state, the network may satisfy the competing demands of information transmission and network stability.",
            "publicationTitle": "J. Neurosci.",
            "publisher": "",
            "place": "",
            "date": "December 3, 2003",
            "volume": "23",
            "issue": "35",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "11167-11177",
            "series": "",
            "seriesTitle": "",
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            "citationKey": "beggsNeuronalAvalanchesNeocortical2003",
            "url": "http://www.jneurosci.org/cgi/content/abstract/23/35/11167",
            "accessDate": "2009-08-19T20:59:55Z",
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            "extra": "01111",
            "tags": [
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                    "tag": "MFToPNoIFN"
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                {
                    "tag": "avalanche"
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            "creatorSummary": "Hahn et al.",
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            "itemType": "journalArticle",
            "title": "Neuronal Avalanches in Spontaneous Activity In Vivo",
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                    "creatorType": "author",
                    "firstName": "Gerald",
                    "lastName": "Hahn"
                },
                {
                    "creatorType": "author",
                    "firstName": "Thomas",
                    "lastName": "Petermann"
                },
                {
                    "creatorType": "author",
                    "firstName": "Martha N.",
                    "lastName": "Havenith"
                },
                {
                    "creatorType": "author",
                    "firstName": "Shan",
                    "lastName": "Yu"
                },
                {
                    "creatorType": "author",
                    "firstName": "Wolf",
                    "lastName": "Singer"
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                    "creatorType": "author",
                    "firstName": "Dietmar",
                    "lastName": "Plenz"
                },
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                    "creatorType": "author",
                    "firstName": "Danko",
                    "lastName": "Nikolić"
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            ],
            "abstractNote": "Many complex systems give rise to events that are clustered in space and time, thereby establishing a correlation structure that is governed by power law statistics. In the cortex, such clusters of activity, called “neuronal avalanches,” were recently found in local field potentials (LFPs) of spontaneous activity in acute cortex slices, slice cultures, the developing cortex of the anesthetized rat, and premotor and motor cortex of awake monkeys. At present, it is unclear whether neuronal avalanches also exist in the spontaneous LFPs and spike activity in vivo in sensory areas of the mature brain. To address this question, we recorded spontaneous LFPs and extracellular spiking activity with multiple 4 × 4 microelectrode arrays (Michigan Probes) in area 17 of adult cats under anesthesia. A cluster of events was defined as a consecutive sequence of time bins Δt (1–32 ms), each containing at least one LFP event or spike anywhere on the array. LFP cluster sizes consistently distributed according to a power law with a slope largely above –1.5. In two thirds of the corresponding experiments, spike clusters also displayed a power law that displayed a slightly steeper slope of −1.8 and was destroyed by subsampling operations. The power law in spike clusters was accompanied with stronger temporal correlations between spiking activities of neurons that spanned longer time periods compared with spike clusters lacking power law statistics. The results suggest that spontaneous activity of the visual cortex under anesthesia has the properties of neuronal avalanches.",
            "publicationTitle": "Journal of Neurophysiology",
            "publisher": "",
            "place": "",
            "date": "December 01 , 2010",
            "volume": "104",
            "issue": "6",
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            "partNumber": "",
            "partTitle": "",
            "pages": "3312 -3322",
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            "DOI": "10.1152/jn.00953.2009",
            "citationKey": "hahnNeuronalAvalanchesSpontaneous2010",
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            "accessDate": "2011-05-10T07:34:02Z",
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            "note": "<p>The learning from STDP is completely driven by forced firing of targeted pattern and does not involve triggered firing of post synaptic neurons.</p>",
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