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The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition.

Wyatte D, Herd S, Mingus B, O'Reilly R - Front Psychol (2012)

Bottom Line: Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions.Finally, we argue that temporal synchrony plays only a limited role in binding - it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features.Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, USA.

ABSTRACT
How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain's binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding - it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.

No MeSH data available.


Results for multiple object binding. (A) We tested the effect of removing top-down feedback and both top-down feedback and inhibitory competition from the model. The purely feedforward model missing both of these critical mechanisms made the most recognition errors. (B) Grouping responses according to whether they were corresponded to the model’s output (relevant responses) or not (irrelevant responses) suggests that the reason for the purely feedforward model’s poor performance was that it had a higher overall signal-to-noise ratio (mean relevant response divided by mean irrelevant response). This type of representation could lead to illusory feature conjunctions and thus, recognition errors.
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Figure 4: Results for multiple object binding. (A) We tested the effect of removing top-down feedback and both top-down feedback and inhibitory competition from the model. The purely feedforward model missing both of these critical mechanisms made the most recognition errors. (B) Grouping responses according to whether they were corresponded to the model’s output (relevant responses) or not (irrelevant responses) suggests that the reason for the purely feedforward model’s poor performance was that it had a higher overall signal-to-noise ratio (mean relevant response divided by mean irrelevant response). This type of representation could lead to illusory feature conjunctions and thus, recognition errors.

Mentions: Binding errors can occur when neurons representing irrelevant features are not entirely out-competed (Figure 3B). This allows invalid feature conjunctions to manifest, which subsequently get reinforced from top-down feedback, resulting in the formation of illusory conjunctions. To determine more specifically how inhibition and top-down feedback contribute to minimizing illusory conjunctions, we tested the effect of removing top-down feedback and both top-down feedback and inhibition from the model1 (see Methods for details). The results of these tests are indicated in Figure 4.


The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition.

Wyatte D, Herd S, Mingus B, O'Reilly R - Front Psychol (2012)

Results for multiple object binding. (A) We tested the effect of removing top-down feedback and both top-down feedback and inhibitory competition from the model. The purely feedforward model missing both of these critical mechanisms made the most recognition errors. (B) Grouping responses according to whether they were corresponded to the model’s output (relevant responses) or not (irrelevant responses) suggests that the reason for the purely feedforward model’s poor performance was that it had a higher overall signal-to-noise ratio (mean relevant response divided by mean irrelevant response). This type of representation could lead to illusory feature conjunctions and thus, recognition errors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376426&req=5

Figure 4: Results for multiple object binding. (A) We tested the effect of removing top-down feedback and both top-down feedback and inhibitory competition from the model. The purely feedforward model missing both of these critical mechanisms made the most recognition errors. (B) Grouping responses according to whether they were corresponded to the model’s output (relevant responses) or not (irrelevant responses) suggests that the reason for the purely feedforward model’s poor performance was that it had a higher overall signal-to-noise ratio (mean relevant response divided by mean irrelevant response). This type of representation could lead to illusory feature conjunctions and thus, recognition errors.
Mentions: Binding errors can occur when neurons representing irrelevant features are not entirely out-competed (Figure 3B). This allows invalid feature conjunctions to manifest, which subsequently get reinforced from top-down feedback, resulting in the formation of illusory conjunctions. To determine more specifically how inhibition and top-down feedback contribute to minimizing illusory conjunctions, we tested the effect of removing top-down feedback and both top-down feedback and inhibition from the model1 (see Methods for details). The results of these tests are indicated in Figure 4.

Bottom Line: Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions.Finally, we argue that temporal synchrony plays only a limited role in binding - it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features.Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, USA.

ABSTRACT
How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain's binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding - it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.

No MeSH data available.