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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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Two patterns, pattern one and two, are cued at two different positions at the beginning of the simulation.The cue corresponding to pattern one is given by setting the activity of neurons inside a 15×15 square box whose lower left corner is on neuron (1,1) to their activity in pattern one. The cue corresponding to pattern two is given by setting the activity of neurons inside a 20×20 square box whose lower left corner is on neuron (33,35) to their activity in pattern two. Plotted are the final dot product overlap (minus the mean activity; Eq. (9)) with the first stored pattern (full line) and the second stored pattern (dashed line) for two values of the connectivity width (A) σ = 7.5 and (B) σ = 10. Since the initial overlap with the second pattern is larger, without gain modulation it wins the competition, and it will be retrieved, as shown by the final dot product overlap with the two patterns. However, localised gain modulation biases the competition in favour of the first pattern.
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pcbi-1000012-g009: Two patterns, pattern one and two, are cued at two different positions at the beginning of the simulation.The cue corresponding to pattern one is given by setting the activity of neurons inside a 15×15 square box whose lower left corner is on neuron (1,1) to their activity in pattern one. The cue corresponding to pattern two is given by setting the activity of neurons inside a 20×20 square box whose lower left corner is on neuron (33,35) to their activity in pattern two. Plotted are the final dot product overlap (minus the mean activity; Eq. (9)) with the first stored pattern (full line) and the second stored pattern (dashed line) for two values of the connectivity width (A) σ = 7.5 and (B) σ = 10. Since the initial overlap with the second pattern is larger, without gain modulation it wins the competition, and it will be retrieved, as shown by the final dot product overlap with the two patterns. However, localised gain modulation biases the competition in favour of the first pattern.

Mentions: One of the roles of attention is to bias the competition for limited processing resources in favour of the object that it is acting on [103],[104]. Therefore, if the localised gain modulation that is needed in our model for combining what and where is induced by attention, it should be able to do the same. This is verified by computer simulations as shown in Fig. 9. Two localised partial cues, corresponding to two different objects, are simultaneously given to a network. When the neuronal gain is uniform, the object with the larger cue will be retrieved, while the other one will be suppressed. However, if the neuronal gain in the area that receives the smaller cue is sufficiently large, the competition will be biased in favour of it. Interestingly, the level of gain modulation that is required to bias the competition towards the object with the small cue depends on the width of the connectivity, σ. Increasing the width of the neuronal connectivity increases the minimum level of gain modulation that is required for biasing the competition. This emphasises the role of local connectivity.


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

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

Two patterns, pattern one and two, are cued at two different positions at the beginning of the simulation.The cue corresponding to pattern one is given by setting the activity of neurons inside a 15×15 square box whose lower left corner is on neuron (1,1) to their activity in pattern one. The cue corresponding to pattern two is given by setting the activity of neurons inside a 20×20 square box whose lower left corner is on neuron (33,35) to their activity in pattern two. Plotted are the final dot product overlap (minus the mean activity; Eq. (9)) with the first stored pattern (full line) and the second stored pattern (dashed line) for two values of the connectivity width (A) σ = 7.5 and (B) σ = 10. Since the initial overlap with the second pattern is larger, without gain modulation it wins the competition, and it will be retrieved, as shown by the final dot product overlap with the two patterns. However, localised gain modulation biases the competition in favour of the first pattern.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000012-g009: Two patterns, pattern one and two, are cued at two different positions at the beginning of the simulation.The cue corresponding to pattern one is given by setting the activity of neurons inside a 15×15 square box whose lower left corner is on neuron (1,1) to their activity in pattern one. The cue corresponding to pattern two is given by setting the activity of neurons inside a 20×20 square box whose lower left corner is on neuron (33,35) to their activity in pattern two. Plotted are the final dot product overlap (minus the mean activity; Eq. (9)) with the first stored pattern (full line) and the second stored pattern (dashed line) for two values of the connectivity width (A) σ = 7.5 and (B) σ = 10. Since the initial overlap with the second pattern is larger, without gain modulation it wins the competition, and it will be retrieved, as shown by the final dot product overlap with the two patterns. However, localised gain modulation biases the competition in favour of the first pattern.
Mentions: One of the roles of attention is to bias the competition for limited processing resources in favour of the object that it is acting on [103],[104]. Therefore, if the localised gain modulation that is needed in our model for combining what and where is induced by attention, it should be able to do the same. This is verified by computer simulations as shown in Fig. 9. Two localised partial cues, corresponding to two different objects, are simultaneously given to a network. When the neuronal gain is uniform, the object with the larger cue will be retrieved, while the other one will be suppressed. However, if the neuronal gain in the area that receives the smaller cue is sufficiently large, the competition will be biased in favour of it. Interestingly, the level of gain modulation that is required to bias the competition towards the object with the small cue depends on the width of the connectivity, σ. Increasing the width of the neuronal connectivity increases the minimum level of gain modulation that is required for biasing the competition. This emphasises the role of local connectivity.

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