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  • American Physiological Society  (4)
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  • American Physiological Society  (4)
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  • 1
    Online Resource
    Online Resource
    American Physiological Society ; 2005
    In:  Journal of Neurophysiology Vol. 93, No. 2 ( 2005-02), p. 1090-1098
    In: Journal of Neurophysiology, American Physiological Society, Vol. 93, No. 2 ( 2005-02), p. 1090-1098
    Abstract: Dissociated neurons cultured in vitro can serve as a model system for studying the dynamics of neural networks. Such studies depend on techniques for stimulating patterns of neural activity. We show a technique for extracellular stimulation of dissociated neurons cultured on silicon wafers. When the silicon surface is reverse biased, electrical current can be generated near any neuron by pulsing a laser. Complex spatiotemporal stimulation patterns can be produced by directing a single beam with an acousto-optic deflector. The technique can generate a stimulating current at any location in the culture. This contrasts with multielectrode arrays (MEAs), which can stimulate only at fixed electrode locations. To characterize reliability and spatial selectivity of stimulation, we used intracellular (patch-clamp) recordings to monitor the effect of targeted laser pulses on cultured hippocampal neurons. Action potentials could be stimulated with submillisecond precision and 100-micron spatial resolution at rates exceeding 100 Hz. Optimal control parameters for stimulation are discussed.
    Type of Medium: Online Resource
    ISSN: 0022-3077 , 1522-1598
    RVK:
    RVK:
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2005
    detail.hit.zdb_id: 80161-6
    detail.hit.zdb_id: 1467889-5
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    American Physiological Society ; 2004
    In:  Journal of Neurophysiology Vol. 92, No. 4 ( 2004-10), p. 2274-2282
    In: Journal of Neurophysiology, American Physiological Society, Vol. 92, No. 4 ( 2004-10), p. 2274-2282
    Abstract: Sparse neural codes have been widely observed in cortical sensory and motor areas. A striking example of sparse temporal coding is in the song-related premotor area high vocal center (HVC) of songbirds: The motor neurons innervating avian vocal muscles are driven by premotor nucleus robustus archistriatalis (RA), which is in turn driven by nucleus HVC. Recent experiments reveal that RA-projecting HVC neurons fire just one burst per song motif. However, the function of this remarkable temporal sparseness has remained unclear. Because birdsong is a clear example of a learned complex motor behavior, we explore in a neural network model with the help of numerical and analytical techniques the possible role of sparse premotor neural codes in song-related motor learning. In numerical simulations with nonlinear neurons, as HVC activity is made progressively less sparse, the minimum learning time increases significantly. Heuristically, this slowdown arises from increasing interference in the weight updates for different synapses. If activity in HVC is sparse, synaptic interference is reduced, and is minimized if each synapse from HVC to RA is used only once in the motif, which is the situation observed experimentally. Our numerical results are corroborated by a theoretical analysis of learning in linear networks, for which we derive a relationship between sparse activity, synaptic interference, and learning time. If songbirds acquire their songs under significant pressure to learn quickly, this study predicts that HVC activity, currently measured only in adults, should also be sparse during the sensorimotor phase in the juvenile bird. We discuss the relevance of these results, linking sparse codes and learning speed, to other multilayered sensory and motor systems.
    Type of Medium: Online Resource
    ISSN: 0022-3077 , 1522-1598
    RVK:
    RVK:
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2004
    detail.hit.zdb_id: 80161-6
    detail.hit.zdb_id: 1467889-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    American Physiological Society ; 2007
    In:  Journal of Neurophysiology Vol. 98, No. 4 ( 2007-10), p. 2038-2057
    In: Journal of Neurophysiology, American Physiological Society, Vol. 98, No. 4 ( 2007-10), p. 2038-2057
    Abstract: We propose a model of songbird learning that focuses on avian brain areas HVC and RA, involved in song production, and area LMAN, important for generating song variability. Plasticity at HVC → RA synapses is driven by hypothetical “rules” depending on three signals: activation of HVC → RA synapses, activation of LMAN → RA synapses, and reinforcement from an internal critic that compares the bird's own song with a memorized template of an adult tutor's song. Fluctuating glutamatergic input to RA from LMAN generates behavioral variability for trial-and-error learning. The plasticity rules perform gradient-based reinforcement learning in a spiking neural network model of song production. Although the reinforcement signal is delayed, temporally imprecise, and binarized, the model learns in a reasonable amount of time in numerical simulations. Varying the number of neurons in HVC and RA has little effect on learning time. The model makes specific predictions for the induction of bidirectional long-term plasticity at HVC → RA synapses.
    Type of Medium: Online Resource
    ISSN: 0022-3077 , 1522-1598
    RVK:
    RVK:
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2007
    detail.hit.zdb_id: 80161-6
    detail.hit.zdb_id: 1467889-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    Online Resource
    Online Resource
    American Physiological Society ; 2005
    In:  Journal of Neurophysiology Vol. 94, No. 6 ( 2005-12), p. 4038-4050
    In: Journal of Neurophysiology, American Physiological Society, Vol. 94, No. 6 ( 2005-12), p. 4038-4050
    Abstract: The tilt aftereffect (TAE) is a visual illusion in which prolonged adaptation to an oriented stimulus causes shifts in subsequent perceived orientations. Historically, neural models of the TAE have explained it as the outcome of response suppression of neurons tuned to the adapting orientation. Recent physiological studies of neurons in primary visual cortex (V1) have confirmed that such response suppression exists. However, it was also found that the preferred orientations of neurons shift away from the adapting orientation. Here we show that adding this second factor to a population coding model of V1 improves the correspondence between neurophysiological data and TAE measurements. According to our model, the shifts in preferred orientation have the opposite effect as response suppression, reducing the magnitude of the TAE.
    Type of Medium: Online Resource
    ISSN: 0022-3077 , 1522-1598
    RVK:
    RVK:
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2005
    detail.hit.zdb_id: 80161-6
    detail.hit.zdb_id: 1467889-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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