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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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Diagram of Model. (A) A cortical unit, composed of pyramidal neurons and inhibitory interneurons, exhibits activity (green) based on the feedforward, basal/soma-targeted (blue) and feedback, apical-target (red) inputs it receives. (B) Table describing how the unit activity depends on these inputs, as described by Equations (2) and (3). The inputs and outputs are shown by solid and dashed arrows, which correspond to active and searching inputs/outputs, respectively. The direction of each arrow indicates the phase of gamma oscillations (active) or the timing of sporadic, feedback-propagating bursts (searching). The different rows correspond to feedforward and feedback inputs, and unit output, respectively. Multiple feedforward or feedback arrows indicate multiple inputs of these types. Note that the same effects are achieved with sporadic, bursting feedback inputs (but not so for feedforward inputs). (C) Modulating effect of feedback on a unit's responsiveness to feedforward input, as described by Equation (2). Without feedback, the unit will remain in the resting state, regardless of the feedforward input. Coherent feedforward input must be coherent within itself but also with any feedback activity.
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Figure 1: Diagram of Model. (A) A cortical unit, composed of pyramidal neurons and inhibitory interneurons, exhibits activity (green) based on the feedforward, basal/soma-targeted (blue) and feedback, apical-target (red) inputs it receives. (B) Table describing how the unit activity depends on these inputs, as described by Equations (2) and (3). The inputs and outputs are shown by solid and dashed arrows, which correspond to active and searching inputs/outputs, respectively. The direction of each arrow indicates the phase of gamma oscillations (active) or the timing of sporadic, feedback-propagating bursts (searching). The different rows correspond to feedforward and feedback inputs, and unit output, respectively. Multiple feedforward or feedback arrows indicate multiple inputs of these types. Note that the same effects are achieved with sporadic, bursting feedback inputs (but not so for feedforward inputs). (C) Modulating effect of feedback on a unit's responsiveness to feedforward input, as described by Equation (2). Without feedback, the unit will remain in the resting state, regardless of the feedforward input. Coherent feedforward input must be coherent within itself but also with any feedback activity.

Mentions: We model the cortex as being composed of a network of small units of pyramidal neurons and inhibitory interneurons (Figure 1A). These units are modeled as neural masses and the individual neurons are not explicitly modeled. The units receive two types of inputs: feedforward inputs to the soma and basal dendrites (blue) and feedback inputs to the apical dendrites (red). As proposed by Larkum (2013) for individual pyramidal neurons, we hypothesize that these units are associative and generate much stronger output when they are activated simultaneously by both of these types of inputs. We further hypothesize that these units can only be activated by inputs with gamma oscillations. Importantly, it is an assumption of the model that the input activity, and the activity elicited in the units, is oscillatory. While inputs and units in the model are not actually composed of networks of neurons that generate these oscillations, the oscillations do represent fluctuations of the instantaneous spiking rate of neural populations and we assume that in the brain they would arise due to the reciprocal excitation and inhibition within the population. In addition to receiving both feedforward and feedback input, activation of units requires that these inputs are in phase, or coherent (Figure 1B). The requirement for units to receive both feedforward and feedback activity in order to become active can be thought of as binary gain modulation or a gating of the unit's activity (see Figure 1C).


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)

Diagram of Model. (A) A cortical unit, composed of pyramidal neurons and inhibitory interneurons, exhibits activity (green) based on the feedforward, basal/soma-targeted (blue) and feedback, apical-target (red) inputs it receives. (B) Table describing how the unit activity depends on these inputs, as described by Equations (2) and (3). The inputs and outputs are shown by solid and dashed arrows, which correspond to active and searching inputs/outputs, respectively. The direction of each arrow indicates the phase of gamma oscillations (active) or the timing of sporadic, feedback-propagating bursts (searching). The different rows correspond to feedforward and feedback inputs, and unit output, respectively. Multiple feedforward or feedback arrows indicate multiple inputs of these types. Note that the same effects are achieved with sporadic, bursting feedback inputs (but not so for feedforward inputs). (C) Modulating effect of feedback on a unit's responsiveness to feedforward input, as described by Equation (2). Without feedback, the unit will remain in the resting state, regardless of the feedforward input. Coherent feedforward input must be coherent within itself but also with any feedback activity.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Diagram of Model. (A) A cortical unit, composed of pyramidal neurons and inhibitory interneurons, exhibits activity (green) based on the feedforward, basal/soma-targeted (blue) and feedback, apical-target (red) inputs it receives. (B) Table describing how the unit activity depends on these inputs, as described by Equations (2) and (3). The inputs and outputs are shown by solid and dashed arrows, which correspond to active and searching inputs/outputs, respectively. The direction of each arrow indicates the phase of gamma oscillations (active) or the timing of sporadic, feedback-propagating bursts (searching). The different rows correspond to feedforward and feedback inputs, and unit output, respectively. Multiple feedforward or feedback arrows indicate multiple inputs of these types. Note that the same effects are achieved with sporadic, bursting feedback inputs (but not so for feedforward inputs). (C) Modulating effect of feedback on a unit's responsiveness to feedforward input, as described by Equation (2). Without feedback, the unit will remain in the resting state, regardless of the feedforward input. Coherent feedforward input must be coherent within itself but also with any feedback activity.
Mentions: We model the cortex as being composed of a network of small units of pyramidal neurons and inhibitory interneurons (Figure 1A). These units are modeled as neural masses and the individual neurons are not explicitly modeled. The units receive two types of inputs: feedforward inputs to the soma and basal dendrites (blue) and feedback inputs to the apical dendrites (red). As proposed by Larkum (2013) for individual pyramidal neurons, we hypothesize that these units are associative and generate much stronger output when they are activated simultaneously by both of these types of inputs. We further hypothesize that these units can only be activated by inputs with gamma oscillations. Importantly, it is an assumption of the model that the input activity, and the activity elicited in the units, is oscillatory. While inputs and units in the model are not actually composed of networks of neurons that generate these oscillations, the oscillations do represent fluctuations of the instantaneous spiking rate of neural populations and we assume that in the brain they would arise due to the reciprocal excitation and inhibition within the population. In addition to receiving both feedforward and feedback input, activation of units requires that these inputs are in phase, or coherent (Figure 1B). The requirement for units to receive both feedforward and feedback activity in order to become active can be thought of as binary gain modulation or a gating of the unit's activity (see Figure 1C).

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