High baseline activity in inferior temporal cortex improves neural and behavioral discriminability during visual categorization

Spontaneous firing is a ubiquitous property of neural activity in the brain. Recent literature suggests that this baseline activity plays a key role in perception. However, it is not known how the baseline activity contributes to neural coding and behavior. Here, by recording from the single neurons in the inferior temporal cortex of monkeys performing a visual categorization task, we thoroughly explored the relationship between baseline activity, the evoked response, and behavior. Specifically we found that a low-frequency (<8 Hz) oscillation in the spike train, prior and phase-locked to the stimulus onset, was correlated with increased gamma power and neuronal baseline activity. This enhancement of the baseline activity was then followed by an increase in the neural selectivity and the response reliability and eventually a higher behavioral performance.Iran National Science Foundation (INSF

Neuronal Spike Train Analysis in Likelihood Space

Conventional methods for spike train analysis are predominantly based on the rate function. Additionally, many experiments have utilized a temporal coding mechanism. Several techniques have been used for analyzing these two sources of information separately, but using both sources in a single framework remains a challenging problem. Here, an innovative technique is proposed for spike train analysis that considers both rate and temporal information.Point process modeling approach is used to estimate the stimulus conditional distribution, based on observation of repeated trials. The extended Kalman filter is applied for estimation of the parameters in a parametric model. The marked point process strategy is used in order to extend this model from a single neuron to an entire neuronal population. Each spike train is transformed into a binary vector and then projected from the observation space onto the likelihood space. This projection generates a newly structured space that integrates temporal and rate information, thus improving performance of distribution-based classifiers. In this space, the stimulus-specific information is used as a distance metric between two stimuli. To illustrate the advantages of the proposed technique, spiking activity of inferior temporal cortex neurons in the macaque monkey are analyzed in both the observation and likelihood spaces. Based on goodness-of-fit, performance of the estimation method is demonstrated and the results are subsequently compared with the firing rate-based framework.From both rate and temporal information integration and improvement in the neural discrimination of stimuli, it may be concluded that the likelihood space generates a more accurate representation of stimulus space. Further, an understanding of the neuronal mechanism devoted to visual object categorization may be addressed in this framework as well

Neural Representation of Ambiguous Visual Objects in the Inferior Temporal Cortex

<div><p>Inferior temporal (IT) cortex as the final stage of the ventral visual pathway is involved in visual object recognition. In our everyday life we need to recognize visual objects that are degraded by noise. Psychophysical studies have shown that the accuracy and speed of the object recognition decreases as the amount of visual noise increases. However, the neural representation of ambiguous visual objects and the underlying neural mechanisms of such changes in the behavior are not known. Here, by recording the neuronal spiking activity of macaque monkeys’ IT we explored the relationship between stimulus ambiguity and the IT neural activity. We found smaller amplitude, later onset, earlier offset and shorter duration of the response as visual ambiguity increased. All of these modulations were gradual and correlated with the level of stimulus ambiguity. We found that while category selectivity of IT neurons decreased with noise, it was preserved for a large extent of visual ambiguity. This noise tolerance for category selectivity in IT was lost at 60% noise level. Interestingly, while the response of the IT neurons to visual stimuli at 60% noise level was significantly larger than their baseline activity and full (100%) noise, it was not category selective anymore. The latter finding shows a neural representation that signals the presence of visual stimulus without signaling what it is. In general these findings, in the context of a drift diffusion model, explain the neural mechanisms of perceptual accuracy and speed changes in the process of recognizing ambiguous objects.</p> </div

Response onset latency, offset latency and duration in different noise levels.

<div><p>A. Onset latency, offset latency and duration of the response of the exemplar unit (U10) to body images in different levels of ambiguity. When full noise images were presented there was no increase in the response of this unit relative to the baseline activity (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076856#pone-0076856-g002" target="_blank">Figure 2A</a>). Therefore, onset, offset and duration were not measurable in this noise level and are not shown in this figure.</p> <p>B. Onset latency of the response of the units (n=25) to body images with different levels of noise.</p> <p>C. P-values of the comparison of onset latency values in different pairs of noise levels (t-test, paired).</p> <p>D. Offset latency of the response of the units (n=21) to body images with different levels of noise.</p> <p>E. P-values of the comparison of offset latency values in different pairs of noise levels (t-test, paired). Conventions as in C.</p> <p>F. Duration of the response of the units (n=21) to body images with different levels of noise.</p> <p>G. P-values of the comparison of response duration values in different pairs of noise levels (t-test, paired). Conventions as in C.</p></div

Category discriminability in different noise levels.

<div><p>A. SI of the all units in different noise levels, measured during 100 to 300 ms after stimulus onset. Stars show p-values of t-tests between pairs of noise levels (**: P<0.005).</p> <p>B. The performance of a classification trained to categorize body versus object stimuli. Stars show p-values of t-tests between pairs of noise levels (*****: P<0.00001).</p> <p>C. Temporal dynamic of SI of the exemplar unit (U10). SI was measured in different noise levels in sliding 50-ms windows. Data points are plotted at the middle of each bin. The gray box represents the window used for the analysis in A.</p> <p>D. Temporal dynamic of SI of all body selective units in different noise levels. The gray box represents the window used for the analysis in B. Conventions as in C.</p></div

Schematic model.

<div><p>A. Body cells’ cumulative SI in more and less noisy conditions. Cumulative SI was measured separately for less (10% and 30%, blue line) and more (45% and 60%, red line) noisy images in non-overlapping 50-ms windows during 100 to 300 ms after the stimulus onset. Dashed lines (a and b) represent hypothetical lines representing possible decision boundaries.</p> <p>B. Drift diffusion model and evidence accumulation for visible and ambiguous stimuli. Decision variable (DV) is the cumulative sum of the evidence. The bounds represent the decision boundaries for different choices. Slower drift rate in ambiguous condition is the result of lower response amplitude, shorter response duration and smaller response selectivity in this condition.</p></div

Response to body vs. object images in different noise levels.

<div><p>A. Averaged response of all units to object images with different levels of noise. Normalization was done as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076856#pone-0076856-g002" target="_blank">Figure 2E</a>. The gray box represents the period of evoked activity used for the further analysis.</p> <p>B. Mean response of all units to object images in different levels of noise, during 100 to 300 ms after stimulus onset. Conventions as in Figure 2B.</p> <p>C. Response of all body selective units (n=48) to body and object images in different noise levels during 100 to 300 ms after stimulus onset. Each data point shows the mean response of one unit. The red data point shows the exemplar unit (U10). Full noise (100%) is not shown in this figure because there is no category information in full noise images. The inset p-values show the results of paired t-ttest between responses to body and object images.</p></div

Response amplitude in different noise levels.

<div><p>A. The response of an exemplar unit (U10) to body images in different noise levels (black: 10%, red: 30%, green: 45%, blue: 60% and magenta: 100%). Here and in all other plots of temporal pattern of the events, responses of units were measured in 1-ms bins and smoothed by convolving with a 30-ms Gaussian kernel. The gray box represents the period of evoked activity used for the further analysis.</p> <p>B. Mean response of the exemplar unit in A (U10) to body images with different levels of ambiguity, during 100 to 300 ms after stimulus onset. Error bars denote s.e.m. across different trials. Stars show the p-values of the t-test between pairs of noise levels (*: P<0.05; **: P<0.01; ***: P<0.001; ****: P<0.0001; *****: P<0.00001). Inset r and P show the correlation coefficient and its p-value for the Pearson correlation analysis between responses and noise levels.</p> <p>C. The response of the exemplar unit (U10) to object images. Conventions as in A.</p> <p>D. Distribution of the body selectivity index (see Methods) for all of the recorded units. Red data point shows the exemplar unit (U10).</p> <p>E. Averaged response of all units to body images with different levels of noise. For normalization, peak response of each unit was measured before smoothing. Then smoothed responses of each unit in different noise levels were normalized to the peak response of that unit. Finally, normalized responses of different units in each noise level were averaged. Shaded area shows s.e.m. across different units. Conventions as in A.</p> <p>F. Mean normalized response of all units to body images in different levels of noise, during 100 to 300 ms after stimulus onset. Error bars here and in other figures denote s.e.m. across different units. Conventions as in B.</p></div

Stimuli and task.

<div><p>A. Exemplar body (monkey body) and object (car) stimuli in different levels of noise. Numbers below the images show percent of the noise for each column of images.</p> <p>B. Sequence of task events. Two macaque monkeys were trained to perform a passive viewing task. The presentation of the stimulus sequence started after the monkey maintained fixation for 400 ms on a small white fixation point at the center of the screen. Images from two different categories (body and object) were presented randomly for 70 ms with a variable (650 to 950 ms) inter-stimulus interval (ISI). The monkeys’ task was to keep their gaze fixed on the center of the screen. They were rewarded every 1.5 to 2 seconds for maintaining the fixation.</p></div