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Representing where along with what information in a model of a cortical patch.

Roudi Y, Treves A - PLoS Comput. Biol. (2008)

Bottom Line: Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons.Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position.These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects.

View Article: PubMed Central - PubMed

Affiliation: Gatsby Computational Neuroscience Unit, UCL, United Kingdom. yasser@gatsby.ucl.ac.uk

ABSTRACT
Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects.

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Local gain modulation largely fixes the position of the bump.The panels summarise the results of simulations conducted as for Fig. 3 except for two factors. First, the pattern-selective cue is not localised, i.e. the local overlap at the beginning of each simulation is a uniform function across the network; hence, the distribution of the peak of the local overlap in the beginning of the simulation is not shown: there are no significant peaks. Second, neurons inside the 15×15 square centred around each green square in (A) have a gain factor g which is 1.5 times larger than the rest of the network, and in (C), 3 times larger. (B) and (D) report the distributions of distances between the centre of the gain modulated square and the peak of the final local overlap corresponding to simulations in (A) and (C), respectively. Red circles in (A) and (C), and red portions of the bars in (B) and (D) correspond to successful runs (defined as runs in which the overlap with the cued pattern, after 200 time steps, is higher than the overlap with any other pattern) and black circles and black portions of the bars represent unsuccessful ones. mean(d) and std(d) are the mean and the standard deviation of the distances averaged over successful runs and mean(d*) and std(d*) averaged over unsuccessful runs.
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pcbi-1000012-g005: Local gain modulation largely fixes the position of the bump.The panels summarise the results of simulations conducted as for Fig. 3 except for two factors. First, the pattern-selective cue is not localised, i.e. the local overlap at the beginning of each simulation is a uniform function across the network; hence, the distribution of the peak of the local overlap in the beginning of the simulation is not shown: there are no significant peaks. Second, neurons inside the 15×15 square centred around each green square in (A) have a gain factor g which is 1.5 times larger than the rest of the network, and in (C), 3 times larger. (B) and (D) report the distributions of distances between the centre of the gain modulated square and the peak of the final local overlap corresponding to simulations in (A) and (C), respectively. Red circles in (A) and (C), and red portions of the bars in (B) and (D) correspond to successful runs (defined as runs in which the overlap with the cued pattern, after 200 time steps, is higher than the overlap with any other pattern) and black circles and black portions of the bars represent unsuccessful ones. mean(d) and std(d) are the mean and the standard deviation of the distances averaged over successful runs and mean(d*) and std(d*) averaged over unsuccessful runs.

Mentions: Suppose that a non-pattern-selective signal changes the gain of those neurons which correspond to the position of the object in the visual scene. The effect of such gain modulation is shown in Fig. 5.


Representing where along with what information in a model of a cortical patch.

Roudi Y, Treves A - PLoS Comput. Biol. (2008)

Local gain modulation largely fixes the position of the bump.The panels summarise the results of simulations conducted as for Fig. 3 except for two factors. First, the pattern-selective cue is not localised, i.e. the local overlap at the beginning of each simulation is a uniform function across the network; hence, the distribution of the peak of the local overlap in the beginning of the simulation is not shown: there are no significant peaks. Second, neurons inside the 15×15 square centred around each green square in (A) have a gain factor g which is 1.5 times larger than the rest of the network, and in (C), 3 times larger. (B) and (D) report the distributions of distances between the centre of the gain modulated square and the peak of the final local overlap corresponding to simulations in (A) and (C), respectively. Red circles in (A) and (C), and red portions of the bars in (B) and (D) correspond to successful runs (defined as runs in which the overlap with the cued pattern, after 200 time steps, is higher than the overlap with any other pattern) and black circles and black portions of the bars represent unsuccessful ones. mean(d) and std(d) are the mean and the standard deviation of the distances averaged over successful runs and mean(d*) and std(d*) averaged over unsuccessful runs.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2268242&req=5

pcbi-1000012-g005: Local gain modulation largely fixes the position of the bump.The panels summarise the results of simulations conducted as for Fig. 3 except for two factors. First, the pattern-selective cue is not localised, i.e. the local overlap at the beginning of each simulation is a uniform function across the network; hence, the distribution of the peak of the local overlap in the beginning of the simulation is not shown: there are no significant peaks. Second, neurons inside the 15×15 square centred around each green square in (A) have a gain factor g which is 1.5 times larger than the rest of the network, and in (C), 3 times larger. (B) and (D) report the distributions of distances between the centre of the gain modulated square and the peak of the final local overlap corresponding to simulations in (A) and (C), respectively. Red circles in (A) and (C), and red portions of the bars in (B) and (D) correspond to successful runs (defined as runs in which the overlap with the cued pattern, after 200 time steps, is higher than the overlap with any other pattern) and black circles and black portions of the bars represent unsuccessful ones. mean(d) and std(d) are the mean and the standard deviation of the distances averaged over successful runs and mean(d*) and std(d*) averaged over unsuccessful runs.
Mentions: Suppose that a non-pattern-selective signal changes the gain of those neurons which correspond to the position of the object in the visual scene. The effect of such gain modulation is shown in Fig. 5.

Bottom Line: Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons.Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position.These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects.

View Article: PubMed Central - PubMed

Affiliation: Gatsby Computational Neuroscience Unit, UCL, United Kingdom. yasser@gatsby.ucl.ac.uk

ABSTRACT
Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects.

Show MeSH