Spike timing, synchronization and information processing on the sensory side of the central nervous system

Lestienne, R.

doi:10.1016/s0301-0082(01)00019-3

Cited by 143 publications

(109 citation statements)

References 157 publications

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“…It has been already suggested that different features of stimuli were represented through different dimensions of the neural pulse code in one neural system (Masuda & Aihara, 2007). Also, it has been realized that the neural code refers to population activity since individual neurons could not provide sufficient signal-to-noise ratio and speed to transmit information (Lestienne, 2001;Shadlen & Newsome, 1998;Eggermont, 1998). Relying on these findings we suggest that the dimension 'pulse amplitude' in our hidden-inputs metrics could correspond well to the 'population firing rate code' of the neural signal (Masuda & Aihara, 2007).…”

Section: Putative Scenario Of Neural Computations During the Scr Processmentioning

confidence: 99%

Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

Branković¹

2012

Neuroscience - Dealing With Frontiers

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Section: Putative Scenario Of Neural Computations During the Scr Processmentioning

confidence: 99%

Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

Branković¹

2012

Neuroscience - Dealing With Frontiers

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“…First, the average and peak amplitude of the population responses to song were greater than to ml noise. This may increase the probability of firing in downstream neurons that receive convergent inputs from MLd neurons, thus efficiently propagating the response and potentially maintaining correlated activity in other brain regions (Lestienne, 2001). Second, the response synchrony resulted in a highly temporally precise population response to song.…”

Section: Stimulus-dependent Tuning Population Coding and Ensemble Smentioning

confidence: 99%

Stimulus-Dependent Auditory Tuning Results in Synchronous Population Coding of Vocalizations in the Songbird Midbrain

2006

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Physiological studies in vocal animals such as songbirds indicate that vocalizations drive auditory neurons particularly well. But the neural mechanisms whereby vocalizations are encoded differently from other sounds in the auditory system are unknown. We used spectrotemporal receptive fields (STRFs) to study the neural encoding of song versus the encoding of a generic sound, modulationlimited noise, by single neurons and the neuronal population in the zebra finch auditory midbrain. The noise was designed to match song in frequency, spectrotemporal modulation boundaries, and power. STRF calculations were balanced between the two stimulus types by forcing a common stimulus subspace. We found that 91% of midbrain neurons showed significant differences in spectral and temporal tuning properties when birds heard song and when birds heard modulation-limited noise. During the processing of noise, spectrotemporal tuning was highly variable across cells. During song processing, the tuning of individual cells became more similar; frequency tuning bandwidth increased, best temporal modulation frequency increased, and spike timing became more precise. The outcome was a population response to song that encoded rapidly changing sounds with power and precision, resulting in a faithful neural representation of the temporal pattern of a song. Modeling responses to song using the tuning to modulation-limited noise showed that the population response would not encode song as precisely or robustly. We conclude that stimulus-dependent changes in auditory tuning during song processing facilitate the high-fidelity encoding of the temporal pattern of a song.

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“…In the visual system, variability was studied extensively in the geniculate pathway, from the retina, through the dorsal lateral geniculate nucleus to visual cortex (for review, see Lestienne, 2001;Field and Chichilnisky, 2007). It was shown that, when stepping up the levels of the hierarchy of the geniculate visual pathway, the variability of neuronal signal increases (Kara et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Variability of Visual Responses of Superior Colliculus Neurons Depends on Stimulus Velocity

Mochol

Wójcik²,

Wypych³

et al. 2010

J. Neurosci.

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Visually responding neurons in the superficial, retinorecipient layers of the cat superior colliculus receive input from two primarily parallel information processing channels, Y and W, which is reflected in their velocity response profiles. We quantified the timedependent variability of responses of these neurons to stimuli moving with different velocities by Fano factor (FF) calculated in discrete time windows. The FF for cells responding to low-velocity stimuli, thus receiving W inputs, increased with the increase in the firing rate. In contrast, the dynamics of activity of the cells responding to fast moving stimuli, processed by Y pathway, correlated negatively with FF whether the response was excitatory or suppressive. These observations were tested against several types of surrogate data. Whereas Poisson description failed to reproduce the variability of all collicular responses, the inclusion of secondary structure to the generating point process recovered most of the observed features of responses to fast moving stimuli. Neither model could reproduce the variability of low-velocity responses, which suggests that, in this case, more complex time dependencies need to be taken into account. Our results indicate that Y and W channels may differ in reliability of responses to visual stimulation. Apart from previously reported morphological and physiological differences of the cells belonging to Y and W channels, this is a new feature distinguishing these two pathways.

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Spike timing, synchronization and information processing on the sensory side of the central nervous system

Cited by 143 publications

References 157 publications

Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

Assessment of Brain Monoaminergic Signaling Through Mathematical Modeling of Skin Conductance Response

Stimulus-Dependent Auditory Tuning Results in Synchronous Population Coding of Vocalizations in the Songbird Midbrain

Variability of Visual Responses of Superior Colliculus Neurons Depends on Stimulus Velocity

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