Limits...
Converging neuronal activity in inferior temporal cortex during the classification of morphed stimuli.

Akrami A, Liu Y, Treves A, Jagadeesh B - Cereb. Cortex (2008)

Bottom Line: IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image).This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image.Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons.

View Article: PubMed Central - PubMed

Affiliation: SISSA International School for Advanced Studies, Trieste, Italy.

ABSTRACT
How does the brain dynamically convert incoming sensory data into a representation useful for classification? Neurons in inferior temporal (IT) cortex are selective for complex visual stimuli, but their response dynamics during perceptual classification is not well understood. We studied IT dynamics in monkeys performing a classification task. The monkeys were shown visual stimuli that were morphed (interpolated) between pairs of familiar images. Their ability to classify the morphed images depended systematically on the degree of morph. IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image). The responses tended to peak approximately 120 ms following stimulus onset with an amplitude that depended almost linearly on the degree of morph. The responses then declined, but remained above baseline for several hundred ms. This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image. Thus, these neurons represented the dynamic conversion of graded sensory information into a task-relevant classification. Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons.

Show MeSH

Related in: MedlinePlus

(a, b) Scatter plots of slope of linear regression (in Spikes/Second/Morph Level) in late versus early epoch (100–200 ms vs. 400–500 ms after sample onset) for each individual cell in the population. Histograms are the distributions of slopes for individual cells in early and late epochs. n = 128 experiments. (a) Slope of Eff image and Eff morphs (Eff, 6–9), (b) slope of Ineff image, and Ineff morphs (Ineff, 1–4). (c) Time course of slope (across population) as a function of time. One hundred millisecond bins, stepped 10 ms.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2651479&req=5

fig4: (a, b) Scatter plots of slope of linear regression (in Spikes/Second/Morph Level) in late versus early epoch (100–200 ms vs. 400–500 ms after sample onset) for each individual cell in the population. Histograms are the distributions of slopes for individual cells in early and late epochs. n = 128 experiments. (a) Slope of Eff image and Eff morphs (Eff, 6–9), (b) slope of Ineff image, and Ineff morphs (Ineff, 1–4). (c) Time course of slope (across population) as a function of time. One hundred millisecond bins, stepped 10 ms.

Mentions: All the tests of significance were performed on firing rate functions FR(t). FR(t) was calculated for each neuron, for each sample image, by averaging firing rate across multiple presentations of each sample in overlapping time bins (also called epochs) of 100 ms, shifted in time steps of 10 ms (Zoccolan et al. 2007). This procedure smoothes the data. The average FR(t) was plotted at the middle of the 100-ms bin. Therefore, average responses at time 0 consist of the average of responses from -50 to 50 ms after stimulus onset. To calculate the dependence of the neural responses on morph level, we performed a regression analysis for each cell for each epoch. We regressed the spike rate in an epoch against the morph level, separately for Eff and Ineff images (Fig. 4). To compare the response to Eff with its 4 variants and also Ineff with the other 4 ineffective variants (Fig. 5), we applied an unbalanced 2-way ANOVA. In this ANOVA, we treated the cell as one factor (128 level), and stimulus as the second factor (2 level: Eff vs. 9, Eff vs. 8, Eff vs. 7, Eff vs. 6, Ineff vs. 1, Ineff vs. 2, Ineff vs. 3, Ineff vs. 4). Morphs 2 levels apart were also compared using an unbalanced one-way ANOVA, considering again “stimulus” and “cell” as 2 factors with 2 levels (2 vs. image Ineff, 4 vs. 2, 6 vs. 4, 8 vs. 6 and Eff vs. 8) and 128 levels, respectively (Fig. 5).


Converging neuronal activity in inferior temporal cortex during the classification of morphed stimuli.

Akrami A, Liu Y, Treves A, Jagadeesh B - Cereb. Cortex (2008)

(a, b) Scatter plots of slope of linear regression (in Spikes/Second/Morph Level) in late versus early epoch (100–200 ms vs. 400–500 ms after sample onset) for each individual cell in the population. Histograms are the distributions of slopes for individual cells in early and late epochs. n = 128 experiments. (a) Slope of Eff image and Eff morphs (Eff, 6–9), (b) slope of Ineff image, and Ineff morphs (Ineff, 1–4). (c) Time course of slope (across population) as a function of time. One hundred millisecond bins, stepped 10 ms.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651479&req=5

fig4: (a, b) Scatter plots of slope of linear regression (in Spikes/Second/Morph Level) in late versus early epoch (100–200 ms vs. 400–500 ms after sample onset) for each individual cell in the population. Histograms are the distributions of slopes for individual cells in early and late epochs. n = 128 experiments. (a) Slope of Eff image and Eff morphs (Eff, 6–9), (b) slope of Ineff image, and Ineff morphs (Ineff, 1–4). (c) Time course of slope (across population) as a function of time. One hundred millisecond bins, stepped 10 ms.
Mentions: All the tests of significance were performed on firing rate functions FR(t). FR(t) was calculated for each neuron, for each sample image, by averaging firing rate across multiple presentations of each sample in overlapping time bins (also called epochs) of 100 ms, shifted in time steps of 10 ms (Zoccolan et al. 2007). This procedure smoothes the data. The average FR(t) was plotted at the middle of the 100-ms bin. Therefore, average responses at time 0 consist of the average of responses from -50 to 50 ms after stimulus onset. To calculate the dependence of the neural responses on morph level, we performed a regression analysis for each cell for each epoch. We regressed the spike rate in an epoch against the morph level, separately for Eff and Ineff images (Fig. 4). To compare the response to Eff with its 4 variants and also Ineff with the other 4 ineffective variants (Fig. 5), we applied an unbalanced 2-way ANOVA. In this ANOVA, we treated the cell as one factor (128 level), and stimulus as the second factor (2 level: Eff vs. 9, Eff vs. 8, Eff vs. 7, Eff vs. 6, Ineff vs. 1, Ineff vs. 2, Ineff vs. 3, Ineff vs. 4). Morphs 2 levels apart were also compared using an unbalanced one-way ANOVA, considering again “stimulus” and “cell” as 2 factors with 2 levels (2 vs. image Ineff, 4 vs. 2, 6 vs. 4, 8 vs. 6 and Eff vs. 8) and 128 levels, respectively (Fig. 5).

Bottom Line: IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image).This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image.Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons.

View Article: PubMed Central - PubMed

Affiliation: SISSA International School for Advanced Studies, Trieste, Italy.

ABSTRACT
How does the brain dynamically convert incoming sensory data into a representation useful for classification? Neurons in inferior temporal (IT) cortex are selective for complex visual stimuli, but their response dynamics during perceptual classification is not well understood. We studied IT dynamics in monkeys performing a classification task. The monkeys were shown visual stimuli that were morphed (interpolated) between pairs of familiar images. Their ability to classify the morphed images depended systematically on the degree of morph. IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image). The responses tended to peak approximately 120 ms following stimulus onset with an amplitude that depended almost linearly on the degree of morph. The responses then declined, but remained above baseline for several hundred ms. This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image. Thus, these neurons represented the dynamic conversion of graded sensory information into a task-relevant classification. Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons.

Show MeSH
Related in: MedlinePlus