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Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence.

Kerr RR, Grayden DB, Thomas DA, Gilson M, Burkitt AN - Front Neural Circuits (2014)

Bottom Line: We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units.With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation.Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

View Article: PubMed Central - PubMed

Affiliation: NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Melbourne, VIC, Australia ; Centre for Neural Engineering, The University of Melbourne Melbourne, VIC, Australia ; NICTA, Victoria Research Lab, The University of Melbourne Melbourne, VIC, Australia.

ABSTRACT
The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

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Non-interacting and Interacting Operations. (A) Two output units, Y1 and Y2, which individually perform operations “X1 or X2” and “X2 and not X3,” respectively. Feedforward connections to input units from lower-level units and feedforward connections from output units to higher-level units (as shown in Figure 3) have been omitted. (B) Same as (A) but with an additional feedforward connection which does not change the individual operations but introduces interactions when they are initiated together. (C) Same as (A) but Y2 instead needs to be initiated in phase with the input units for it to perform the same operation. There is also an additional feedback connection that, similar to the additional connection in (B), does not change the individual operations but introduces interactions. (D) Input-output table for the networks in (A–C). The input units (or cues), X1, X2, and X3, either receive feedforward input (1) or not (0), and the output units, Y1 and Y2, are either activated (1) or not (0) for each of the networks initiated with external feedback to only Y1, only Y2, or to both Y1 and Y2. Green (red) outputs are ones that are activated (not activated) when the units are initiated together but were not activated (activated) when the units were initiated separately. The final row indicates the number of inputs that the output unit's operation depends upon (relevant inputs), where green (red) indicates that the number has increased (decreased) from being initiated separately to being initiated together.
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Figure 4: Non-interacting and Interacting Operations. (A) Two output units, Y1 and Y2, which individually perform operations “X1 or X2” and “X2 and not X3,” respectively. Feedforward connections to input units from lower-level units and feedforward connections from output units to higher-level units (as shown in Figure 3) have been omitted. (B) Same as (A) but with an additional feedforward connection which does not change the individual operations but introduces interactions when they are initiated together. (C) Same as (A) but Y2 instead needs to be initiated in phase with the input units for it to perform the same operation. There is also an additional feedback connection that, similar to the additional connection in (B), does not change the individual operations but introduces interactions. (D) Input-output table for the networks in (A–C). The input units (or cues), X1, X2, and X3, either receive feedforward input (1) or not (0), and the output units, Y1 and Y2, are either activated (1) or not (0) for each of the networks initiated with external feedback to only Y1, only Y2, or to both Y1 and Y2. Green (red) outputs are ones that are activated (not activated) when the units are initiated together but were not activated (activated) when the units were initiated separately. The final row indicates the number of inputs that the output unit's operation depends upon (relevant inputs), where green (red) indicates that the number has increased (decreased) from being initiated separately to being initiated together.

Mentions: Figure 4 illustrates the contrast between interacting and non-interacting operations. It also demonstrates how small changes to a network with non-interacting operations cause it's operations to interact. Figure 4A shows a network where two operations have overlapping inputs but no interactions occur. Figures 4B,C provide examples of networks with overlapping motifs where the same operations are performed when the outputs are initiated separately but interactions occur when they are initiated together. These interactions are evident in the table in Figure 4D, where there exist inputs for which a different output is produced depending on whether the two hypotheses are initiated separately or together. The last row in Figure 4D contains the number of inputs that are involved in the operations performed. Interactions can cause this to either increase or decrease.


Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence.

Kerr RR, Grayden DB, Thomas DA, Gilson M, Burkitt AN - Front Neural Circuits (2014)

Non-interacting and Interacting Operations. (A) Two output units, Y1 and Y2, which individually perform operations “X1 or X2” and “X2 and not X3,” respectively. Feedforward connections to input units from lower-level units and feedforward connections from output units to higher-level units (as shown in Figure 3) have been omitted. (B) Same as (A) but with an additional feedforward connection which does not change the individual operations but introduces interactions when they are initiated together. (C) Same as (A) but Y2 instead needs to be initiated in phase with the input units for it to perform the same operation. There is also an additional feedback connection that, similar to the additional connection in (B), does not change the individual operations but introduces interactions. (D) Input-output table for the networks in (A–C). The input units (or cues), X1, X2, and X3, either receive feedforward input (1) or not (0), and the output units, Y1 and Y2, are either activated (1) or not (0) for each of the networks initiated with external feedback to only Y1, only Y2, or to both Y1 and Y2. Green (red) outputs are ones that are activated (not activated) when the units are initiated together but were not activated (activated) when the units were initiated separately. The final row indicates the number of inputs that the output unit's operation depends upon (relevant inputs), where green (red) indicates that the number has increased (decreased) from being initiated separately to being initiated together.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Figure 4: Non-interacting and Interacting Operations. (A) Two output units, Y1 and Y2, which individually perform operations “X1 or X2” and “X2 and not X3,” respectively. Feedforward connections to input units from lower-level units and feedforward connections from output units to higher-level units (as shown in Figure 3) have been omitted. (B) Same as (A) but with an additional feedforward connection which does not change the individual operations but introduces interactions when they are initiated together. (C) Same as (A) but Y2 instead needs to be initiated in phase with the input units for it to perform the same operation. There is also an additional feedback connection that, similar to the additional connection in (B), does not change the individual operations but introduces interactions. (D) Input-output table for the networks in (A–C). The input units (or cues), X1, X2, and X3, either receive feedforward input (1) or not (0), and the output units, Y1 and Y2, are either activated (1) or not (0) for each of the networks initiated with external feedback to only Y1, only Y2, or to both Y1 and Y2. Green (red) outputs are ones that are activated (not activated) when the units are initiated together but were not activated (activated) when the units were initiated separately. The final row indicates the number of inputs that the output unit's operation depends upon (relevant inputs), where green (red) indicates that the number has increased (decreased) from being initiated separately to being initiated together.
Mentions: Figure 4 illustrates the contrast between interacting and non-interacting operations. It also demonstrates how small changes to a network with non-interacting operations cause it's operations to interact. Figure 4A shows a network where two operations have overlapping inputs but no interactions occur. Figures 4B,C provide examples of networks with overlapping motifs where the same operations are performed when the outputs are initiated separately but interactions occur when they are initiated together. These interactions are evident in the table in Figure 4D, where there exist inputs for which a different output is produced depending on whether the two hypotheses are initiated separately or together. The last row in Figure 4D contains the number of inputs that are involved in the operations performed. Interactions can cause this to either increase or decrease.

Bottom Line: We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units.With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation.Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

View Article: PubMed Central - PubMed

Affiliation: NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Melbourne, VIC, Australia ; Centre for Neural Engineering, The University of Melbourne Melbourne, VIC, Australia ; NICTA, Victoria Research Lab, The University of Melbourne Melbourne, VIC, Australia.

ABSTRACT
The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

Show MeSH