Rapid Changes in Thalamic Firing Synchrony during Repetitive Whisker Stimulation

Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15261, USA.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience (Impact Factor: 6.34). 11/2008; 28(44):11153-64. DOI: 10.1523/JNEUROSCI.1586-08.2008
Source: PubMed


Thalamic firing synchrony is thought to ensure selective transmission of relevant sensory information to the recipient cortical neurons by rendering them more responsive to temporally correlated input spikes. However, direct evidence for a synchrony code in the thalamus is limited. Here, we directly measure thalamic firing synchrony and its stimulus-induced modulation over time, using simultaneous single unit recordings from individual thalamic barreloids in the rat somatosensory whisker/barrel system. Employing whisker deflections varying in velocity or frequency and a cross-correlation approach, we find systematic changes in both time course and strength of thalamic firing synchrony as a function of stimulus parameters and sensory adaptation. Synchrony develops faster and is greater with higher velocity deflections. Greater firing synchrony reflects stimulus-dependent increases in instantaneous firing rates, greater spike time precision relative to stimulus onset as well as common input that likely arises from divergent trigeminothalamic and corticothalamic neurons. With adaptation, synchrony decreases and takes longer to develop but is more dependent on the cells' common inputs. Rapid, sharp increases in thalamic synchrony mirroring quick increases in whisker velocity occur also during ongoing random, high-frequency whisker vibrations. Together, results demonstrate millisecond by millisecond changes in thalamic near-synchronous firing during complex patterns of ongoing vibrissa movements that may ensure transmission of preferred sensory information in local thalamocortical circuits during whisking and active touch.

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Available from: Simona Temereanca, Mar 22, 2014
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    • "ata for group analyses , single whisker and whisker array data sets for each subject were shifted horizontally to align peak LFP responses in layers 2 / 3 and 4 for the strongest stimulus amplitude ( 7 . 5 • ) . For suprathreshold responses , an abundance of overlapping spike waveforms ( Supplementary Figure 1 ; see also Bar - Gad et al . , 2001 ; Temereanca et al . , 2008 ) made it difficult to interpret PSTHs of spike times although these results were still consistent with main findings ( Supplementary Figures 2 , 3 ) ."
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    ABSTRACT: Invariant sensory coding is the robust coding of some sensory information (e.g., stimulus type) despite major changes in other sensory parameters (e.g., stimulus strength). The contribution of large populations of neurons (ensembles) to invariant sensory coding is not well understood, but could offer distinct advantages over invariance in single cell receptive fields. To test invariant sensory coding in neuronal ensembles evoked by single whisker stimulation as early as primary sensory cortex, we recorded detailed spatiotemporal movies of evoked ensemble activity through the depth of rat barrel cortex using microelectrode arrays. We found that an emergent property of whisker evoked ensemble activity, its spatiotemporal profile, was notably invariant across major changes in stimulus amplitude (up to >200-fold). Such ensemble-based invariance was found for single whisker stimulation as well as for the integrated profile of activity evoked by the more naturalistic stimulation of the entire whisker array. Further, the integrated profile of whisker array evoked ensemble activity and its invariance to stimulus amplitude shares striking similarities to "funneled" tactile perception in humans. We therefore suggest that ensemble-based invariance could provide a robust neurobiological substrate for invariant sensory coding and integration at an early stage of cortical sensory processing already in primary sensory cortex.
    Frontiers in Neural Circuits 07/2015; 9:34. DOI:10.3389/fncir.2015.00034 · 3.60 Impact Factor
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    • "However, they require the CIF of the ground process to be uniformly small. This is an assumption that may be plausible in some cases but is hard to justify for all data from neurophysiology experiments, particularly those that use explicit stimuli such as the data from the whisking experiment (Temereanca et al., 2008), a small subset of which we analyze below. "
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    ABSTRACT: Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble.
    Frontiers in Computational Neuroscience 02/2014; 8:6. DOI:10.3389/fncom.2014.00006 · 2.20 Impact Factor
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    • "Second, we find that our formulation partly obviates the use of the correction techniques introduced in (Haslinger et al., 2010) for goodness-of-fit assessment using the time-rescaling theorem and discrete-time approximations to the CIF. We demonstrate our claims on simulated data, as well as real data from rat thalamic neurons recorded in response to periodic whisker deflections varying in velocity (Temereanca et al., 2008). These data are characterized by high mean and instantaneous firing rates, on the order of 20 and 200 Hz respectively. "
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    ABSTRACT: Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural populations. We propose an approximation to the likelihood of a point-process model of neurons that holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity function, and its integrability. These are properties that apply to a large class of point processes arising in applications other than neuroscience. The proposed approach has several advantages over conventional ones. In particular, one can use standard fitting procedures for generalized linear models based on iteratively reweighted least squares while improving the accuracy of the approximation to the likelihood and reducing bias in the estimation of the parameters of the underlying continuous-time model. As a result, the proposed approach can use a larger bin size to achieve the same accuracy as conventional approaches would with a smaller bin size. This is particularly important when analyzing neural data with high mean and instantaneous firing rates. We demonstrate these claims on simulated and real neural spiking activity. By allowing a substantive increase in the required bin size, our algorithm has the potential to lower the barrier to the use of point-process methods in an increasing number of applications.
    Neural Computation 11/2013; 26(2). DOI:10.1162/NECO_a_00548 · 2.21 Impact Factor
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