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Differing effects of attention in single-units and populations are well predicted by heterogeneous tuning and the normalization model of attention.

Hara Y, Pestilli F, Gardner JL - Front Comput Neurosci (2014)

Bottom Line: The normalization model of attention elegantly predicts the diversity of effects of attention reported in single-units well-tuned to the stimulus, but what predictions does it make for more realistic populations of neurons with heterogeneous tuning?We found that within the population, neurons well-tuned to the stimulus showed a response-gain effect, while less-well-tuned neurons showed a contrast-gain effect.More generally, computational models can unify physiological findings across different scales of measurement, and make links to behavior, but only if factors such as heterogeneous tuning within a population are properly accounted for.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Human Systems Neuroscience, RIKEN Brain Science Institute Wako, Japan.

ABSTRACT
Single-unit measurements have reported many different effects of attention on contrast-response (e.g., contrast-gain, response-gain, additive-offset dependent on visibility), while functional imaging measurements have more uniformly reported increases in response across all contrasts (additive-offset). The normalization model of attention elegantly predicts the diversity of effects of attention reported in single-units well-tuned to the stimulus, but what predictions does it make for more realistic populations of neurons with heterogeneous tuning? Are predictions in accordance with population-scale measurements? We used functional imaging data from humans to determine a realistic ratio of attention-field to stimulus-drive size (a key parameter for the model) and predicted effects of attention in a population of model neurons with heterogeneous tuning. We found that within the population, neurons well-tuned to the stimulus showed a response-gain effect, while less-well-tuned neurons showed a contrast-gain effect. Averaged across the population, these disparate effects of attention gave rise to additive-offsets in contrast-response, similar to reports in human functional imaging as well as population averages of single-units. Differences in predictions for single-units and populations were observed across a wide range of model parameters (ratios of attention-field to stimulus-drive size and the amount of baseline response modifiable by attention), offering an explanation for disparity in physiological reports. Thus, by accounting for heterogeneity in tuning of realistic neuronal populations, the normalization model of attention can not only predict responses of well-tuned neurons, but also the activity of large populations of neurons. More generally, computational models can unify physiological findings across different scales of measurement, and make links to behavior, but only if factors such as heterogeneous tuning within a population are properly accounted for.

No MeSH data available.


Attention effect for best-matched neuron (A) and population average (B) predicted by model in which attention effects are restricted to neurons whose tuning matches the orientation of the stimulus. All conventions are same as Figure 5.
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Figure 6: Attention effect for best-matched neuron (A) and population average (B) predicted by model in which attention effects are restricted to neurons whose tuning matches the orientation of the stimulus. All conventions are same as Figure 5.

Mentions: We tested the effect on the well-tuned model neuron and average of the population of heterogeneously-tuned neurons when attention was also restricted along the orientation feature. In this case, the well-tuned model neuron showed response-gain effects along the whole continuum of attention-field size to stimulus-drive ratios. This is in contrast to the previous simulation in which effects ranged from contrast-gain to response-gain effects (read left-right, Figure 6A compared to Figure 5A). This was due to the suppressive-drive being effectively weaker when attention was also restricted in the orientation domain. This caused even very large ratios of attention-field to stimulus-drive size (even when ratio was 10) to still not be able to normalize responses at high contrasts. Thus, predicted attention effects across the whole set of parameters we tested were generally consistent with response-gain (note green background, Figure 6A).


Differing effects of attention in single-units and populations are well predicted by heterogeneous tuning and the normalization model of attention.

Hara Y, Pestilli F, Gardner JL - Front Comput Neurosci (2014)

Attention effect for best-matched neuron (A) and population average (B) predicted by model in which attention effects are restricted to neurons whose tuning matches the orientation of the stimulus. All conventions are same as Figure 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928538&req=5

Figure 6: Attention effect for best-matched neuron (A) and population average (B) predicted by model in which attention effects are restricted to neurons whose tuning matches the orientation of the stimulus. All conventions are same as Figure 5.
Mentions: We tested the effect on the well-tuned model neuron and average of the population of heterogeneously-tuned neurons when attention was also restricted along the orientation feature. In this case, the well-tuned model neuron showed response-gain effects along the whole continuum of attention-field size to stimulus-drive ratios. This is in contrast to the previous simulation in which effects ranged from contrast-gain to response-gain effects (read left-right, Figure 6A compared to Figure 5A). This was due to the suppressive-drive being effectively weaker when attention was also restricted in the orientation domain. This caused even very large ratios of attention-field to stimulus-drive size (even when ratio was 10) to still not be able to normalize responses at high contrasts. Thus, predicted attention effects across the whole set of parameters we tested were generally consistent with response-gain (note green background, Figure 6A).

Bottom Line: The normalization model of attention elegantly predicts the diversity of effects of attention reported in single-units well-tuned to the stimulus, but what predictions does it make for more realistic populations of neurons with heterogeneous tuning?We found that within the population, neurons well-tuned to the stimulus showed a response-gain effect, while less-well-tuned neurons showed a contrast-gain effect.More generally, computational models can unify physiological findings across different scales of measurement, and make links to behavior, but only if factors such as heterogeneous tuning within a population are properly accounted for.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Human Systems Neuroscience, RIKEN Brain Science Institute Wako, Japan.

ABSTRACT
Single-unit measurements have reported many different effects of attention on contrast-response (e.g., contrast-gain, response-gain, additive-offset dependent on visibility), while functional imaging measurements have more uniformly reported increases in response across all contrasts (additive-offset). The normalization model of attention elegantly predicts the diversity of effects of attention reported in single-units well-tuned to the stimulus, but what predictions does it make for more realistic populations of neurons with heterogeneous tuning? Are predictions in accordance with population-scale measurements? We used functional imaging data from humans to determine a realistic ratio of attention-field to stimulus-drive size (a key parameter for the model) and predicted effects of attention in a population of model neurons with heterogeneous tuning. We found that within the population, neurons well-tuned to the stimulus showed a response-gain effect, while less-well-tuned neurons showed a contrast-gain effect. Averaged across the population, these disparate effects of attention gave rise to additive-offsets in contrast-response, similar to reports in human functional imaging as well as population averages of single-units. Differences in predictions for single-units and populations were observed across a wide range of model parameters (ratios of attention-field to stimulus-drive size and the amount of baseline response modifiable by attention), offering an explanation for disparity in physiological reports. Thus, by accounting for heterogeneity in tuning of realistic neuronal populations, the normalization model of attention can not only predict responses of well-tuned neurons, but also the activity of large populations of neurons. More generally, computational models can unify physiological findings across different scales of measurement, and make links to behavior, but only if factors such as heterogeneous tuning within a population are properly accounted for.

No MeSH data available.