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  • 1
    Online Resource
    Online Resource
    Multiple Time Scales of Temporal Response in Pyramidal and Fast Spiking Cortical Neurons
    American Physiological Society ; 2006
    In:  Journal of Neurophysiology Vol. 96, No. 6 ( 2006-12), p. 3448-3464
    Details
    In: Journal of Neurophysiology, American Physiological Society, Vol. 96, No. 6 ( 2006-12), p. 3448-3464
    Abstract: Neural dynamic processes correlated over several time scales are found in vivo, in stimulus-evoked as well as spontaneous activity, and are thought to affect the way sensory stimulation is processed. Despite their potential computational consequences, a systematic description of the presence of multiple time scales in single cortical neurons is lacking. In this study, we injected fast spiking and pyramidal (PYR) neurons in vitro with long-lasting episodes of step-like and noisy, in-vivo-like current. Several processes shaped the time course of the instantaneous spike frequency, which could be reduced to a small number (1–4) of phenomenological mechanisms, either reducing (adapting) or increasing (facilitating) the neuron's firing rate over time. The different adaptation/facilitation processes cover a wide range of time scales, ranging from initial adaptation ( 〈 10 ms, PYR neurons only), to fast adaptation ( 〈 300 ms), early facilitation (0.5–1 s, PYR only), and slow (or late) adaptation (order of seconds). These processes are characterized by broad distributions of their magnitudes and time constants across cells, showing that multiple time scales are at play in cortical neurons, even in response to stationary stimuli and in the presence of input fluctuations. These processes might be part of a cascade of processes responsible for the power-law behavior of adaptation observed in several preparations, and may have far-reaching computational consequences that have been recently described.
    Type of Medium: Online Resource
    ISSN: 0022-3077 , 1522-1598
    URL: Article
    DOI: 10.1152/jn.00453.2006
    RVK:
    XA 10000 ; XA 552555
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2006
    detail.hit.zdb_id: 80161-6
    detail.hit.zdb_id: 1467889-5
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  • 2
    Online Resource
    Online Resource
    Neocortical Pyramidal Cells Respond as Integrate-and-Fire Neurons to In Vivo–Like Input Currents
    American Physiological Society ; 2003
    In:  Journal of Neurophysiology Vol. 90, No. 3 ( 2003-09), p. 1598-1612
    Details
    In: Journal of Neurophysiology, American Physiological Society, Vol. 90, No. 3 ( 2003-09), p. 1598-1612
    Abstract: In the intact brain neurons are constantly exposed to intense synaptic activity. This heavy barrage of excitatory and inhibitory inputs was recreated in vitro by injecting a noisy current, generated as an Ornstein–Uhlenbeck process, into the soma of rat neocortical pyramidal cells. The response to such in vivo–like currents was studied systematically by analyzing the time development of the instantaneous spike frequency, and when possible, the stationary mean spike frequency as a function of both the mean and the variance of the input current. All cells responded with an in vivo–like action potential activity with stationary statistics that could be sustained throughout long stimulation intervals (tens of seconds), provided the frequencies were not too high. The temporal evolution of the response revealed the presence of mechanisms of fast and slow spike frequency adaptation, and a medium duration mechanism of facilitation. For strong input currents, the slow adaptation mechanism made the spike frequency response nonstationary. The minimal frequencies that caused strong slow adaptation (a decrease in the spike rate by more than 1 Hz/s), were in the range 30–80 Hz and depended on the pipette solution used. The stationary response function has been fitted by two simple models of integrate-and-fire neurons endowed with a frequency-dependent modification of the input current. This accounts for all the fast and slow mechanisms of adaptation and facilitation that determine the stationary response, and proved necessary to fit the model to the experimental data. The coefficient of variability of the interspike interval was also in part captured by the model neurons, by tuning the parameters of the model to match the mean spike frequencies only. We conclude that the integrate-and-fire model with spike-frequency–dependent adaptation/facilitation is an adequate model reduction of cortical cells when the mean spike-frequency response to in vivo–like currents with stationary statistics is considered.
    Type of Medium: Online Resource
    ISSN: 0022-3077 , 1522-1598
    URL: Article
    DOI: 10.1152/jn.00293.2003
    RVK:
    XA 10000 ; XA 552555
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2003
    detail.hit.zdb_id: 80161-6
    detail.hit.zdb_id: 1467889-5
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  • 3
    Online Resource
    Online Resource
    Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics
    MIT Press ; 2007
    In:  Neural Computation Vol. 19, No. 11 ( 2007-11), p. 2881-2912
    Details
    In: Neural Computation, MIT Press, Vol. 19, No. 11 ( 2007-11), p. 2881-2912
    Abstract: We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).
    Type of Medium: Online Resource
    ISSN: 0899-7667 , 1530-888X
    URL: Article
    DOI: 10.1162/neco.2007.19.11.2881
    Language: English
    Publisher: MIT Press
    Publication Date: 2007
    detail.hit.zdb_id: 1025692-1
    detail.hit.zdb_id: 1498403-9
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  • 4
    Online Resource
    Online Resource
    Comparison between networks of conductance- and current-driven neurons: stationary spike rates and subthreshold depolarization
    Elsevier BV ; 2004
    In:  Neurocomputing Vol. 58-60 ( 2004-6), p. 253-258
    Details
    In: Neurocomputing, Elsevier BV, Vol. 58-60 ( 2004-6), p. 253-258
    Type of Medium: Online Resource
    ISSN: 0925-2312
    URL: Article
    DOI: 10.1016/j.neucom.2004.01.052
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2004
    detail.hit.zdb_id: 1012660-0
    detail.hit.zdb_id: 1479006-3
    detail.hit.zdb_id: 1055250-9
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  • 5
    Online Resource
    Online Resource
    Climbing Neuronal Activity as an Event-Based Cortical Representation of Time
    Society for Neuroscience ; 2004
    In:  The Journal of Neuroscience Vol. 24, No. 13 ( 2004-03-31), p. 3295-3303
    Details
    In: The Journal of Neuroscience, Society for Neuroscience, Vol. 24, No. 13 ( 2004-03-31), p. 3295-3303
    Abstract: The brain has the ability to represent the passage of time between two behaviorally relevant events. Recordings from different areas in the cortex of monkeys suggest the existence of neurons representing time by increasing (climbing) activity, which is triggered by a first event and peaks at the expected time of a second event, e.g., a visual stimulus or a reward. When the typical interval between the two events is changed, the slope of the climbing activity adapts to the new timing. We present a model in which the climbing activity results from slow firing rate adaptation in inhibitory neurons. Hebbian synaptic modifications allow for learning the new time interval by changing the degree of firing rate adaptation. This event-based representation of time is consistent with Weber's law in interval timing, according to which the error in estimating a time interval is proportional to the interval length.
    Type of Medium: Online Resource
    ISSN: 0270-6474 , 1529-2401
    URL: Article
    DOI: 10.1523/JNEUROSCI.4098-03.2004
    Language: English
    Publisher: Society for Neuroscience
    Publication Date: 2004
    detail.hit.zdb_id: 1475274-8
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Learning flexible sensori-motor mappings in a complex network
    Springer Science and Business Media LLC ; 2009
    In:  Biological Cybernetics Vol. 100, No. 2 ( 2009-2), p. 147-158
    Details
    In: Biological Cybernetics, Springer Science and Business Media LLC, Vol. 100, No. 2 ( 2009-2), p. 147-158
    Type of Medium: Online Resource
    ISSN: 0340-1200 , 1432-0770
    URL: Article
    DOI: 10.1007/s00422-008-0288-z
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2009
    detail.hit.zdb_id: 1458477-3
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs
    Springer Science and Business Media LLC ; 2008
    In:  Biological Cybernetics Vol. 99, No. 4-5 ( 2008-11), p. 303-318
    Details
    In: Biological Cybernetics, Springer Science and Business Media LLC, Vol. 99, No. 4-5 ( 2008-11), p. 303-318
    Type of Medium: Online Resource
    ISSN: 0340-1200 , 1432-0770
    URL: Article
    DOI: 10.1007/s00422-008-0270-9
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2008
    detail.hit.zdb_id: 1458477-3
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    The response of cortical neurons to in vivo-like input current: theory and experiment : I. Noisy inputs with stationary statistics
    Springer Science and Business Media LLC ; 2008
    In:  Biological Cybernetics Vol. 99, No. 4-5 ( 2008-11), p. 279-301
    Details
    In: Biological Cybernetics, Springer Science and Business Media LLC, Vol. 99, No. 4-5 ( 2008-11), p. 279-301
    Type of Medium: Online Resource
    ISSN: 0340-1200 , 1432-0770
    URL: Article
    DOI: 10.1007/s00422-008-0272-7
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2008
    detail.hit.zdb_id: 1458477-3
    SSG: 12
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  • 9
    Online Resource
    Online Resource
    Minimal Models of Adapted Neuronal Response to In Vivo –Like Input Currents
    MIT Press ; 2004
    In:  Neural Computation Vol. 16, No. 10 ( 2004-10-01), p. 2101-2124
    Details
    In: Neural Computation, MIT Press, Vol. 16, No. 10 ( 2004-10-01), p. 2101-2124
    Abstract: Rate models are often used to study the behavior of large networks of spiking neurons. Here we propose a procedure to derive rate models that take into account the fluctuations of the input current and firing-rate adaptation, two ubiquitous features in the central nervous system that have been previously overlooked in constructing rate models. The procedure is general and applies to any model of firing unit. As examples, we apply it to the leaky integrate-and-fire (IF) neuron, the leaky IF neuron with reversal potentials, and to the quadratic IF neuron. Two mechanisms of adaptation are considered, one due to an after hyperpolarization current and the other to an adapting threshold for spike emission. The parameters of these simple models can be tuned to match experimental data obtained from neocortical pyramidal neurons. Finally, we show how the stationary model can be used to predict the time-varying activity of a large population of adapting neurons.
    Type of Medium: Online Resource
    ISSN: 0899-7667 , 1530-888X
    URL: Article
    DOI: 10.1162/0899766041732468
    Language: English
    Publisher: MIT Press
    Publication Date: 2004
    detail.hit.zdb_id: 1025692-1
    detail.hit.zdb_id: 1498403-9
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  • 10
    Online Resource
    Online Resource
    Learning Only When Necessary: Better Memories of Correlated Patterns in Networks with Bounded Synapses
    MIT Press ; 2005
    In:  Neural Computation Vol. 17, No. 10 ( 2005-10-01), p. 2106-2138
    Details
    In: Neural Computation, MIT Press, Vol. 17, No. 10 ( 2005-10-01), p. 2106-2138
    Abstract: Learning in a neuronal network is often thought of as a linear superposition of synaptic modifications induced by individual stimuli. However, since biological synapses are naturally bounded, a linear superposition would cause fast forgetting of previously acquired memories. Here we show that this forgetting can be avoided by introducing additional constraints on the synaptic and neural dynamics. We consider Hebbian plasticity of excitatory synapses. A synapse is modified only if the postsynaptic response does not match the desired output. With this learning rule, the original memory performances with unbounded weights are regained, provided that (1) there is some global inhibition, (2) the learning rate is small, and (3) the neurons can discriminate small differences in the total synaptic input (e.g., by making the neuronal threshold small compared to the total postsynaptic input). We prove in the form of a generalized perceptron convergence theorem that under these constraints, a neuron learns to classify any linearly separable set of patterns, including a wide class of highly correlated random patterns. During the learning process, excitation becomes roughly balanced by inhibition, and the neuron classifies the patterns on the basis of small differences around this balance. The fact that synapses saturate has the additional benefit that nonlinearly separable patterns, such as similar patterns with contradicting outputs, eventually generate a subthreshold response, and therefore silence neurons that cannot provide any information.
    Type of Medium: Online Resource
    ISSN: 0899-7667 , 1530-888X
    URL: Article
    DOI: 10.1162/0899766054615644
    Language: English
    Publisher: MIT Press
    Publication Date: 2005
    detail.hit.zdb_id: 1025692-1
    detail.hit.zdb_id: 1498403-9
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