Limits...
Modulation of temporal precision in thalamic population responses to natural visual stimuli.

Desbordes G, Jin J, Alonso JM, Stanley GB - Front Syst Neurosci (2010)

Bottom Line: In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise - on a time scale of 10-25 ms - both within single cells and across cells within a population.This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene.Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment.

View Article: PubMed Central - PubMed

Affiliation: Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA.

ABSTRACT
Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise - on a time scale of 10-25 ms - both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment.

No MeSH data available.


Related in: MedlinePlus

The GLM globally captures the fine timing precision across cells. (A) Global spike cross-correlation width in response to natural scenes, as predicted by the GLM versus as measured in experimental data (correlation coefficient: r = 0.41). For each pair, the spike cross-correlation width is the standard deviation of the Gaussian that best fits the (global) spike cross-correlation function. A fitted line (green) and the corresponding proportionality coefficient are shown, computed between the cross-correlation width predicted by the GLM and that measured experimentally using a least-mean-square fit on all data points. The mean ± standard deviation are represented along each axis by an arrowhead and bar. The dashed line has unity slope. (B) Global spike cross-correlation width in response to spatiotemporal white noise, as predicted by the GLM versus measured in experimental data, in all pairs in which there was a measurable hump in the cross-correlation for this class of visual stimuli (n = 24 pairs). Same conventions as in (A).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2992450&req=5

Figure 11: The GLM globally captures the fine timing precision across cells. (A) Global spike cross-correlation width in response to natural scenes, as predicted by the GLM versus as measured in experimental data (correlation coefficient: r = 0.41). For each pair, the spike cross-correlation width is the standard deviation of the Gaussian that best fits the (global) spike cross-correlation function. A fitted line (green) and the corresponding proportionality coefficient are shown, computed between the cross-correlation width predicted by the GLM and that measured experimentally using a least-mean-square fit on all data points. The mean ± standard deviation are represented along each axis by an arrowhead and bar. The dashed line has unity slope. (B) Global spike cross-correlation width in response to spatiotemporal white noise, as predicted by the GLM versus measured in experimental data, in all pairs in which there was a measurable hump in the cross-correlation for this class of visual stimuli (n = 24 pairs). Same conventions as in (A).

Mentions: A summary of the variability in the shape of local cross-correlation functions is shown in Figure 10A for the same pair of cells as in Figure 8. The local cross-correlation functions for all events (black curves) are superimposed with the global cross-correlation computed on the full stimulus duration (dashed blue curve). Across all 38 pairs, there was a wide distribution of event-by-event cross-correlation widths (σ; Figure 10B) and of mean latencies (μ; Figure 10C). See figure caption for statistics. As a validation, the global temporal precision across cell pairs averaged across the entire natural scene movie was estimated using the GLM, resulting in predictions that were consistent with our previously reported experimental measurements (Desbordes et al., 2008), as shown in Figure 11A. Even though the cross-correlation widths in the model prediction and in the experimental data slightly differed (paired t-test, p = 0.006, n = 37 cells), the best-fitting ratio between model prediction and experimental measurement was 1.10 (Figure 11A), corresponding to an error of only 10%, suggesting that the GLM was indeed a good predictor of correlations across geniculate neurons in response to natural scene stimuli. Not surprisingly, the GLM performed even better in the case of spatiotemporal white noise (Figure 11B), where the model predictions were not significantly different from the experimental measurements (paired t-test: p = 0.36), with a ratio of 0.96.


Modulation of temporal precision in thalamic population responses to natural visual stimuli.

Desbordes G, Jin J, Alonso JM, Stanley GB - Front Syst Neurosci (2010)

The GLM globally captures the fine timing precision across cells. (A) Global spike cross-correlation width in response to natural scenes, as predicted by the GLM versus as measured in experimental data (correlation coefficient: r = 0.41). For each pair, the spike cross-correlation width is the standard deviation of the Gaussian that best fits the (global) spike cross-correlation function. A fitted line (green) and the corresponding proportionality coefficient are shown, computed between the cross-correlation width predicted by the GLM and that measured experimentally using a least-mean-square fit on all data points. The mean ± standard deviation are represented along each axis by an arrowhead and bar. The dashed line has unity slope. (B) Global spike cross-correlation width in response to spatiotemporal white noise, as predicted by the GLM versus measured in experimental data, in all pairs in which there was a measurable hump in the cross-correlation for this class of visual stimuli (n = 24 pairs). Same conventions as in (A).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2992450&req=5

Figure 11: The GLM globally captures the fine timing precision across cells. (A) Global spike cross-correlation width in response to natural scenes, as predicted by the GLM versus as measured in experimental data (correlation coefficient: r = 0.41). For each pair, the spike cross-correlation width is the standard deviation of the Gaussian that best fits the (global) spike cross-correlation function. A fitted line (green) and the corresponding proportionality coefficient are shown, computed between the cross-correlation width predicted by the GLM and that measured experimentally using a least-mean-square fit on all data points. The mean ± standard deviation are represented along each axis by an arrowhead and bar. The dashed line has unity slope. (B) Global spike cross-correlation width in response to spatiotemporal white noise, as predicted by the GLM versus measured in experimental data, in all pairs in which there was a measurable hump in the cross-correlation for this class of visual stimuli (n = 24 pairs). Same conventions as in (A).
Mentions: A summary of the variability in the shape of local cross-correlation functions is shown in Figure 10A for the same pair of cells as in Figure 8. The local cross-correlation functions for all events (black curves) are superimposed with the global cross-correlation computed on the full stimulus duration (dashed blue curve). Across all 38 pairs, there was a wide distribution of event-by-event cross-correlation widths (σ; Figure 10B) and of mean latencies (μ; Figure 10C). See figure caption for statistics. As a validation, the global temporal precision across cell pairs averaged across the entire natural scene movie was estimated using the GLM, resulting in predictions that were consistent with our previously reported experimental measurements (Desbordes et al., 2008), as shown in Figure 11A. Even though the cross-correlation widths in the model prediction and in the experimental data slightly differed (paired t-test, p = 0.006, n = 37 cells), the best-fitting ratio between model prediction and experimental measurement was 1.10 (Figure 11A), corresponding to an error of only 10%, suggesting that the GLM was indeed a good predictor of correlations across geniculate neurons in response to natural scene stimuli. Not surprisingly, the GLM performed even better in the case of spatiotemporal white noise (Figure 11B), where the model predictions were not significantly different from the experimental measurements (paired t-test: p = 0.36), with a ratio of 0.96.

Bottom Line: In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise - on a time scale of 10-25 ms - both within single cells and across cells within a population.This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene.Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment.

View Article: PubMed Central - PubMed

Affiliation: Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA.

ABSTRACT
Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise - on a time scale of 10-25 ms - both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment.

No MeSH data available.


Related in: MedlinePlus