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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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Phase-dependent operations. (A) An output unit connected to the five input units, X1-X5, which receive orchestrating feedback from unit Z1 that is out-of-phase with the inputs, performs the operation “(X1 or X3) and not X4” when it is out-of-phase with the inputs and “X3 and not X1 and not X5” when it is in phase with the inputs. (B) The operation performed by output unit Y1 is changed depending on which other output (or external) units are initiated with it. Intermediate unit X5 is added by either Y2 or Y3 but in different ways, causing it to play a different role in the operation of Y1, and to perform different operations on its own inputs (X2 and X3).
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Figure 7: Phase-dependent operations. (A) An output unit connected to the five input units, X1-X5, which receive orchestrating feedback from unit Z1 that is out-of-phase with the inputs, performs the operation “(X1 or X3) and not X4” when it is out-of-phase with the inputs and “X3 and not X1 and not X5” when it is in phase with the inputs. (B) The operation performed by output unit Y1 is changed depending on which other output (or external) units are initiated with it. Intermediate unit X5 is added by either Y2 or Y3 but in different ways, causing it to play a different role in the operation of Y1, and to perform different operations on its own inputs (X2 and X3).

Mentions: We found that the relative phase between the output units and the units to which they send feedback can also affect the computations performed. If a network has feedback connections with a mix of short and long time lags and all the inputs are in phase, then some of the units receiving feedback will be activated and others blocked. Figure 7A shows this situation, where switching the phase of the feedback switches which of the input units are activated and which are blocked. It, therefore, modifies the set of inputs that are used in the operation. For example, if Y1 is initiated in phase with the inputs, then only X2 and X5 will be involved in the operation (“X2 and not X5”). But, if Y1 is initiated out-of-phase with the inputs, then only X3 and X4 will be included (“X3 and not X4”). Inputs, such as X1, are persistent and involved in the operation regardless of the phase if they do not receive the feedback from the output units but instead feedback from another source (Z1 in this case).


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)

Phase-dependent operations. (A) An output unit connected to the five input units, X1-X5, which receive orchestrating feedback from unit Z1 that is out-of-phase with the inputs, performs the operation “(X1 or X3) and not X4” when it is out-of-phase with the inputs and “X3 and not X1 and not X5” when it is in phase with the inputs. (B) The operation performed by output unit Y1 is changed depending on which other output (or external) units are initiated with it. Intermediate unit X5 is added by either Y2 or Y3 but in different ways, causing it to play a different role in the operation of Y1, and to perform different operations on its own inputs (X2 and X3).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Phase-dependent operations. (A) An output unit connected to the five input units, X1-X5, which receive orchestrating feedback from unit Z1 that is out-of-phase with the inputs, performs the operation “(X1 or X3) and not X4” when it is out-of-phase with the inputs and “X3 and not X1 and not X5” when it is in phase with the inputs. (B) The operation performed by output unit Y1 is changed depending on which other output (or external) units are initiated with it. Intermediate unit X5 is added by either Y2 or Y3 but in different ways, causing it to play a different role in the operation of Y1, and to perform different operations on its own inputs (X2 and X3).
Mentions: We found that the relative phase between the output units and the units to which they send feedback can also affect the computations performed. If a network has feedback connections with a mix of short and long time lags and all the inputs are in phase, then some of the units receiving feedback will be activated and others blocked. Figure 7A shows this situation, where switching the phase of the feedback switches which of the input units are activated and which are blocked. It, therefore, modifies the set of inputs that are used in the operation. For example, if Y1 is initiated in phase with the inputs, then only X2 and X5 will be involved in the operation (“X2 and not X5”). But, if Y1 is initiated out-of-phase with the inputs, then only X3 and X4 will be included (“X3 and not X4”). Inputs, such as X1, are persistent and involved in the operation regardless of the phase if they do not receive the feedback from the output units but instead feedback from another source (Z1 in this case).

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