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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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Interaction effects with network parameters. (A) The mean fraction of relevant inputs (either phase) for an operation initiated alone, NR0/NI (blue), and the mean increase and decrease in the fraction of relevant inputs (either phase) when feedback from a second operation or external unit is also present, NR+/NI (green) and NR−/NI (red), respectively, plotted as functions of the fraction pff−only/pff (the fraction of unreciprocated feedforward connections) as given by Equation (8). The values of other network parameters used were: pff = 0.5, pfb = p*fb = 0.5, and α = α* = 0.5. The dashed vertical line shows the fraction of pff−only/pff used in (B). (B) Same as (A) but varying the probability of the phase of the different types of feedback: α (phase probability of initiating feedback), α* (phase probability of orchestrating feedback), and α = α* (phase probability of any external feedback), for NR0/NI, NR+/NI, and NR−/NI, respectively. Also shown is the fraction of relevant inputs of a particular phase (dashed) that, compared to the fraction of relevant inputs of either phase (solid), illustrates the split between the two phases.
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Figure 8: Interaction effects with network parameters. (A) The mean fraction of relevant inputs (either phase) for an operation initiated alone, NR0/NI (blue), and the mean increase and decrease in the fraction of relevant inputs (either phase) when feedback from a second operation or external unit is also present, NR+/NI (green) and NR−/NI (red), respectively, plotted as functions of the fraction pff−only/pff (the fraction of unreciprocated feedforward connections) as given by Equation (8). The values of other network parameters used were: pff = 0.5, pfb = p*fb = 0.5, and α = α* = 0.5. The dashed vertical line shows the fraction of pff−only/pff used in (B). (B) Same as (A) but varying the probability of the phase of the different types of feedback: α (phase probability of initiating feedback), α* (phase probability of orchestrating feedback), and α = α* (phase probability of any external feedback), for NR0/NI, NR+/NI, and NR−/NI, respectively. Also shown is the fraction of relevant inputs of a particular phase (dashed) that, compared to the fraction of relevant inputs of either phase (solid), illustrates the split between the two phases.

Mentions: When the output unit of interest is initiated alone (NR0), only reciprocally connected units where the feedback is in phase with the inputs will be involved in the operation. Only input units which are not reciprocally connected but make a feedforward connection to the output unit can be added (NR+) and they are added by receiving feedback that is in phase. This is shown in Figure 8A, where we plot the total number of units (of either phase) originally in the operation and the total number added and removed as functions of the ratio pff−only/pff. As expected, when there are only reciprocal connections (i.e., pff−only/pff = 0), no units can be added; when there are no reciprocal connections (i.e., pff−only/pff = 1), no units are originally in the operation (and so none can be removed either).


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)

Interaction effects with network parameters. (A) The mean fraction of relevant inputs (either phase) for an operation initiated alone, NR0/NI (blue), and the mean increase and decrease in the fraction of relevant inputs (either phase) when feedback from a second operation or external unit is also present, NR+/NI (green) and NR−/NI (red), respectively, plotted as functions of the fraction pff−only/pff (the fraction of unreciprocated feedforward connections) as given by Equation (8). The values of other network parameters used were: pff = 0.5, pfb = p*fb = 0.5, and α = α* = 0.5. The dashed vertical line shows the fraction of pff−only/pff used in (B). (B) Same as (A) but varying the probability of the phase of the different types of feedback: α (phase probability of initiating feedback), α* (phase probability of orchestrating feedback), and α = α* (phase probability of any external feedback), for NR0/NI, NR+/NI, and NR−/NI, respectively. Also shown is the fraction of relevant inputs of a particular phase (dashed) that, compared to the fraction of relevant inputs of either phase (solid), illustrates the split between the two phases.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126059&req=5

Figure 8: Interaction effects with network parameters. (A) The mean fraction of relevant inputs (either phase) for an operation initiated alone, NR0/NI (blue), and the mean increase and decrease in the fraction of relevant inputs (either phase) when feedback from a second operation or external unit is also present, NR+/NI (green) and NR−/NI (red), respectively, plotted as functions of the fraction pff−only/pff (the fraction of unreciprocated feedforward connections) as given by Equation (8). The values of other network parameters used were: pff = 0.5, pfb = p*fb = 0.5, and α = α* = 0.5. The dashed vertical line shows the fraction of pff−only/pff used in (B). (B) Same as (A) but varying the probability of the phase of the different types of feedback: α (phase probability of initiating feedback), α* (phase probability of orchestrating feedback), and α = α* (phase probability of any external feedback), for NR0/NI, NR+/NI, and NR−/NI, respectively. Also shown is the fraction of relevant inputs of a particular phase (dashed) that, compared to the fraction of relevant inputs of either phase (solid), illustrates the split between the two phases.
Mentions: When the output unit of interest is initiated alone (NR0), only reciprocally connected units where the feedback is in phase with the inputs will be involved in the operation. Only input units which are not reciprocally connected but make a feedforward connection to the output unit can be added (NR+) and they are added by receiving feedback that is in phase. This is shown in Figure 8A, where we plot the total number of units (of either phase) originally in the operation and the total number added and removed as functions of the ratio pff−only/pff. As expected, when there are only reciprocal connections (i.e., pff−only/pff = 0), no units can be added; when there are no reciprocal connections (i.e., pff−only/pff = 1), no units are originally in the operation (and so none can be removed either).

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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