Limits...
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.

Show MeSH
Formation of multiple bumps.In the beginning of the simulation, pattern 1 is presented to all units in a 15×15 square whose lower left corner is at node (1,1); that is, for each neuron i inside the square the activity is set to ηi1, and for those outside to zero. While simulating the network, background threshold values are set to regulate the mean network activity to a fixed level equal to 0.2. If mean activity inside a 30×30 square centred on the cue centre exceeds 1.0, the threshold of neurons inside this square will be regulated to keep its mean activity equal to 1.08, and neurons outside it will be assigned a high threshold. In the second phase of the simulation, in the right column, a second pattern is also presented to all units in a 15×15 square whose lower left corner is at node (36,36), accompanied by a local threshold decrease to facilitate the pattern “holding on”. The threshold is then regulated in the same way as the first pattern. (A) The distribution of activity, (B) the local overlap with the first pattern (cued in the beginning of the first phase) and (C) the local overlap with the second pattern (cued in the beginning of the second phase), all 100 time steps after the presentation of the first pattern. (D), (E), and (F) are the same quantities as (A), (B) and (C) but 100 time steps after the presentation of the second pattern.
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pcbi-1000012-g008: Formation of multiple bumps.In the beginning of the simulation, pattern 1 is presented to all units in a 15×15 square whose lower left corner is at node (1,1); that is, for each neuron i inside the square the activity is set to ηi1, and for those outside to zero. While simulating the network, background threshold values are set to regulate the mean network activity to a fixed level equal to 0.2. If mean activity inside a 30×30 square centred on the cue centre exceeds 1.0, the threshold of neurons inside this square will be regulated to keep its mean activity equal to 1.08, and neurons outside it will be assigned a high threshold. In the second phase of the simulation, in the right column, a second pattern is also presented to all units in a 15×15 square whose lower left corner is at node (36,36), accompanied by a local threshold decrease to facilitate the pattern “holding on”. The threshold is then regulated in the same way as the first pattern. (A) The distribution of activity, (B) the local overlap with the first pattern (cued in the beginning of the first phase) and (C) the local overlap with the second pattern (cued in the beginning of the second phase), all 100 time steps after the presentation of the first pattern. (D), (E), and (F) are the same quantities as (A), (B) and (C) but 100 time steps after the presentation of the second pattern.

Mentions: Assume that a pattern is retrieved and, using localised gain modulation, the bump of activity is stabilised on a desired position. A second cue may then be presented to the network at another position. Even though most of the connections to each neuron in the network come from nearby neurons, the second pattern would still affect the first retrieval bump, because of the global inhibition in the simplest version of our model, as inhibition is taken to regulate a common threshold, such that the mean activity of the network is constant (Eq. (3)). This introduces interactions between distal neurons, which are not directly connected by excitatory synapses, and such interactions are generally disruptive. A simple way to reduce such interaction is to assume that when the local mean activity in part of the network exceeds some limit value, the threshold is raised but only locally, regardless of the activity of neurons outside that region. The local threshold may also be regulated downward, to facilitate the emergence of a retrieved pattern in a region which would otherwise be kept at too low a mean activity level. With such additional provisions, multiple bumps can be formed and stabilised, as shown in the example in Fig. 8.


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

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

Formation of multiple bumps.In the beginning of the simulation, pattern 1 is presented to all units in a 15×15 square whose lower left corner is at node (1,1); that is, for each neuron i inside the square the activity is set to ηi1, and for those outside to zero. While simulating the network, background threshold values are set to regulate the mean network activity to a fixed level equal to 0.2. If mean activity inside a 30×30 square centred on the cue centre exceeds 1.0, the threshold of neurons inside this square will be regulated to keep its mean activity equal to 1.08, and neurons outside it will be assigned a high threshold. In the second phase of the simulation, in the right column, a second pattern is also presented to all units in a 15×15 square whose lower left corner is at node (36,36), accompanied by a local threshold decrease to facilitate the pattern “holding on”. The threshold is then regulated in the same way as the first pattern. (A) The distribution of activity, (B) the local overlap with the first pattern (cued in the beginning of the first phase) and (C) the local overlap with the second pattern (cued in the beginning of the second phase), all 100 time steps after the presentation of the first pattern. (D), (E), and (F) are the same quantities as (A), (B) and (C) but 100 time steps after the presentation of the second pattern.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2268242&req=5

pcbi-1000012-g008: Formation of multiple bumps.In the beginning of the simulation, pattern 1 is presented to all units in a 15×15 square whose lower left corner is at node (1,1); that is, for each neuron i inside the square the activity is set to ηi1, and for those outside to zero. While simulating the network, background threshold values are set to regulate the mean network activity to a fixed level equal to 0.2. If mean activity inside a 30×30 square centred on the cue centre exceeds 1.0, the threshold of neurons inside this square will be regulated to keep its mean activity equal to 1.08, and neurons outside it will be assigned a high threshold. In the second phase of the simulation, in the right column, a second pattern is also presented to all units in a 15×15 square whose lower left corner is at node (36,36), accompanied by a local threshold decrease to facilitate the pattern “holding on”. The threshold is then regulated in the same way as the first pattern. (A) The distribution of activity, (B) the local overlap with the first pattern (cued in the beginning of the first phase) and (C) the local overlap with the second pattern (cued in the beginning of the second phase), all 100 time steps after the presentation of the first pattern. (D), (E), and (F) are the same quantities as (A), (B) and (C) but 100 time steps after the presentation of the second pattern.
Mentions: Assume that a pattern is retrieved and, using localised gain modulation, the bump of activity is stabilised on a desired position. A second cue may then be presented to the network at another position. Even though most of the connections to each neuron in the network come from nearby neurons, the second pattern would still affect the first retrieval bump, because of the global inhibition in the simplest version of our model, as inhibition is taken to regulate a common threshold, such that the mean activity of the network is constant (Eq. (3)). This introduces interactions between distal neurons, which are not directly connected by excitatory synapses, and such interactions are generally disruptive. A simple way to reduce such interaction is to assume that when the local mean activity in part of the network exceeds some limit value, the threshold is raised but only locally, regardless of the activity of neurons outside that region. The local threshold may also be regulated downward, to facilitate the emergence of a retrieved pattern in a region which would otherwise be kept at too low a mean activity level. With such additional provisions, multiple bumps can be formed and stabilised, as shown in the example in Fig. 8.

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