Limits...
Towards a mathematical theory of cortical micro-circuits.

George D, Hawkins J - PLoS Comput. Biol. (2009)

Bottom Line: Anatomical data provide a contrasting set of organizational constraints.The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others.We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model.

View Article: PubMed Central - PubMed

Affiliation: Numenta Inc., Redwood City, California, United States of America. dgeorge@numenta.com

ABSTRACT
The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains. Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation. Anatomical data provide a contrasting set of organizational constraints. The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others. We describe the pattern recognition capabilities of HTM networks and demonstrate the application of the derived circuits for modeling the subjective contour effect. We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model.

Show MeSH

Related in: MedlinePlus

Kanizsa diagrams.A Kanizsa square (left) and a Kanizsa triangle (right) are shown.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2749218&req=5

pcbi-1000532-g012: Kanizsa diagrams.A Kanizsa square (left) and a Kanizsa triangle (right) are shown.

Mentions: The subjective contour effect is a well known cognitive and physiological phenomenon. Figure 12 shows examples of Kanizsa diagrams that produce this effect. When viewing such diagrams, humans perceive edges even in regions where there is no direct visual evidence for edges. Lee and Nguyen [64] found that neurons in area V1 responded to such illusory contours even though their feed-forward receptive fields do not have any evidence supporting the presence of a line. In addition to finding the neurons in V1 that respond to the illusory contours, Lee and Nguyen also studied the temporal dynamics of their responses. The summary of their findings is that the population averaged response to illusory contours emerged 100 milliseconds after stimulus onset in the superficial layers of V1 and at approximately 120 to 190 millisecond in the deep layers. The responses to illusory contours in area V2 occurred earlier, at 70 milliseconds in the superficial layers and at 95 milliseconds in the deep layers. These findings suggest that top-down feedback is used in the generation of illusory contours.


Towards a mathematical theory of cortical micro-circuits.

George D, Hawkins J - PLoS Comput. Biol. (2009)

Kanizsa diagrams.A Kanizsa square (left) and a Kanizsa triangle (right) are shown.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2749218&req=5

pcbi-1000532-g012: Kanizsa diagrams.A Kanizsa square (left) and a Kanizsa triangle (right) are shown.
Mentions: The subjective contour effect is a well known cognitive and physiological phenomenon. Figure 12 shows examples of Kanizsa diagrams that produce this effect. When viewing such diagrams, humans perceive edges even in regions where there is no direct visual evidence for edges. Lee and Nguyen [64] found that neurons in area V1 responded to such illusory contours even though their feed-forward receptive fields do not have any evidence supporting the presence of a line. In addition to finding the neurons in V1 that respond to the illusory contours, Lee and Nguyen also studied the temporal dynamics of their responses. The summary of their findings is that the population averaged response to illusory contours emerged 100 milliseconds after stimulus onset in the superficial layers of V1 and at approximately 120 to 190 millisecond in the deep layers. The responses to illusory contours in area V2 occurred earlier, at 70 milliseconds in the superficial layers and at 95 milliseconds in the deep layers. These findings suggest that top-down feedback is used in the generation of illusory contours.

Bottom Line: Anatomical data provide a contrasting set of organizational constraints.The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others.We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model.

View Article: PubMed Central - PubMed

Affiliation: Numenta Inc., Redwood City, California, United States of America. dgeorge@numenta.com

ABSTRACT
The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains. Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation. Anatomical data provide a contrasting set of organizational constraints. The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others. We describe the pattern recognition capabilities of HTM networks and demonstrate the application of the derived circuits for modeling the subjective contour effect. We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model.

Show MeSH
Related in: MedlinePlus