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
Time course of population responses to morphed images. (a) Time course of average differences between the responses to images Eff and Ineff (black) and to morphs successively different from the images (Eff & Ineff) between which they were morphed, averaged across the population of cells n=128. In both panels, as in Figure 2 spike counts are binned into 100 ms bins, which slide every 10 ms from stimulus onset, and are averaged across 15–20 trials per unit and morph step. (b) Mean response difference between Eff and Ineff morphs in successive 100 ms epochs after sample onset. (c) Time course of firing rate to Eff and Ineff, and each morph variant, as in (a). (d) Mean response to Eff and Ineff image and morph variants in successive 100 ms epochs as a function of morph level.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Time course of population responses to morphed images. (a) Time course of average differences between the responses to images Eff and Ineff (black) and to morphs successively different from the images (Eff & Ineff) between which they were morphed, averaged across the population of cells n=128. In both panels, as in Figure 2 spike counts are binned into 100 ms bins, which slide every 10 ms from stimulus onset, and are averaged across 15–20 trials per unit and morph step. (b) Mean response difference between Eff and Ineff morphs in successive 100 ms epochs after sample onset. (c) Time course of firing rate to Eff and Ineff, and each morph variant, as in (a). (d) Mean response to Eff and Ineff image and morph variants in successive 100 ms epochs as a function of morph level.

Mentions: Average spike rates (Figs 2 and 3) were calculated by aligning action potentials to the onset of the sample stimulus presentation, and analyzing the data from 100 ms before the onset of the image to the period 1000 ms after the onset of the image. The peristimulus time histogram (PSTH) for each cell was calculated by averaging the rate functions across the repeated trials of presentation of the same stimulus. The population PSTH was calculated by averaging the PSTHs across the set of 128 selective cells. All completed trials were included in the analyses; trials were excluded if the monkey did not make a choice from the 2 possible choice stimuli. Both correct and incorrect trials were included.


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

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

Time course of population responses to morphed images. (a) Time course of average differences between the responses to images Eff and Ineff (black) and to morphs successively different from the images (Eff & Ineff) between which they were morphed, averaged across the population of cells n=128. In both panels, as in Figure 2 spike counts are binned into 100 ms bins, which slide every 10 ms from stimulus onset, and are averaged across 15–20 trials per unit and morph step. (b) Mean response difference between Eff and Ineff morphs in successive 100 ms epochs after sample onset. (c) Time course of firing rate to Eff and Ineff, and each morph variant, as in (a). (d) Mean response to Eff and Ineff image and morph variants in successive 100 ms epochs as a function of morph level.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Time course of population responses to morphed images. (a) Time course of average differences between the responses to images Eff and Ineff (black) and to morphs successively different from the images (Eff & Ineff) between which they were morphed, averaged across the population of cells n=128. In both panels, as in Figure 2 spike counts are binned into 100 ms bins, which slide every 10 ms from stimulus onset, and are averaged across 15–20 trials per unit and morph step. (b) Mean response difference between Eff and Ineff morphs in successive 100 ms epochs after sample onset. (c) Time course of firing rate to Eff and Ineff, and each morph variant, as in (a). (d) Mean response to Eff and Ineff image and morph variants in successive 100 ms epochs as a function of morph level.
Mentions: Average spike rates (Figs 2 and 3) were calculated by aligning action potentials to the onset of the sample stimulus presentation, and analyzing the data from 100 ms before the onset of the image to the period 1000 ms after the onset of the image. The peristimulus time histogram (PSTH) for each cell was calculated by averaging the rate functions across the repeated trials of presentation of the same stimulus. The population PSTH was calculated by averaging the PSTHs across the set of 128 selective cells. All completed trials were included in the analyses; trials were excluded if the monkey did not make a choice from the 2 possible choice stimuli. Both correct and incorrect trials were included.

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