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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.

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Single cells show a variety of neural responses to different morphed images. (a) Response time course of 6 different cells to the 2 end point images Eff (black) and Ineff (black dashed) and to the midlevel morph (blue dashed). (b) Firing rates to the Eff and Ineff and 9 morph variants computed over time period 100–200 ms. The black horizontal line shows the period of sample presentation (320 ms).
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fig2: Single cells show a variety of neural responses to different morphed images. (a) Response time course of 6 different cells to the 2 end point images Eff (black) and Ineff (black dashed) and to the midlevel morph (blue dashed). (b) Firing rates to the Eff and Ineff and 9 morph variants computed over time period 100–200 ms. The black horizontal line shows the period of sample presentation (320 ms).

Mentions: Each of the 12 pairs of images was morphed using MorphX (http://www.norrkross.com/software/morphx/MorphX.php), a freeware, open source program for morphing between 2 photographic images. We constructed 9 intermediate images in between the 2 original images, as described in Liu and Jagadeesh (2008); the images and their morph variants are presented in Figure 2 of Liu and Jagadeesh (2008). These 9 intermediate images, along with the 2 images in the pair were used as samples in the 2AFC-DMS task described above. The particular pair used in a recording session depended on observing selectivity for one of the images in the pair.


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

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

Single cells show a variety of neural responses to different morphed images. (a) Response time course of 6 different cells to the 2 end point images Eff (black) and Ineff (black dashed) and to the midlevel morph (blue dashed). (b) Firing rates to the Eff and Ineff and 9 morph variants computed over time period 100–200 ms. The black horizontal line shows the period of sample presentation (320 ms).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Single cells show a variety of neural responses to different morphed images. (a) Response time course of 6 different cells to the 2 end point images Eff (black) and Ineff (black dashed) and to the midlevel morph (blue dashed). (b) Firing rates to the Eff and Ineff and 9 morph variants computed over time period 100–200 ms. The black horizontal line shows the period of sample presentation (320 ms).
Mentions: Each of the 12 pairs of images was morphed using MorphX (http://www.norrkross.com/software/morphx/MorphX.php), a freeware, open source program for morphing between 2 photographic images. We constructed 9 intermediate images in between the 2 original images, as described in Liu and Jagadeesh (2008); the images and their morph variants are presented in Figure 2 of Liu and Jagadeesh (2008). These 9 intermediate images, along with the 2 images in the pair were used as samples in the 2AFC-DMS task described above. The particular pair used in a recording session depended on observing selectivity for one of the images in the pair.

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