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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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Stimulus-response Tasks. (A) An example of how the cortical architecture would be utilized for a stimulus-response task where a subject pulls one of two levers when presented with one of two sensory cues. The task switches between one of three “goals”: lever L1 should be pulled for cue C1 and cue C2 ignored (G1), L2 for C2 and C1 ignored (G2), and L1 for C1 and L2 for C2 (G3). Network activity is shown for when G1 is active. Similarly, the arrows to the cue units from the left and those leaving the lever units depict the inputs and outputs of the network (i.e., only “active” connections) for a particular set of inputs. (B) Input-output tables for the network shown in (A) for the three different goals. The final row indicates the number of inputs that the output unit's operation depends on (the relevant inputs). (C) Same as (A) but the third task (G3) now involves pulling both levers if and only if both cues occur together. Note that the feedback from the three goals is the same as in (A) but there is an extra layer in the network. (D) Same as (B) but for the network in (C). (E) Similar to (A,C) but with four different goals: L1 should be pulled for C1 and L2 for C2 (G1), L2 for C1 and L1 for C2 (G2), L1 for either C1 or C2 (G3), and L2 either C1 or C2 (G4). G* is not actually one of the four goals but instead always provides feedback (each of the goals could instead provide this feedback). The feedback from the goals is no longer only to output units. (F) Same as (B,D) but for the network in (E).
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Figure 6: Stimulus-response Tasks. (A) An example of how the cortical architecture would be utilized for a stimulus-response task where a subject pulls one of two levers when presented with one of two sensory cues. The task switches between one of three “goals”: lever L1 should be pulled for cue C1 and cue C2 ignored (G1), L2 for C2 and C1 ignored (G2), and L1 for C1 and L2 for C2 (G3). Network activity is shown for when G1 is active. Similarly, the arrows to the cue units from the left and those leaving the lever units depict the inputs and outputs of the network (i.e., only “active” connections) for a particular set of inputs. (B) Input-output tables for the network shown in (A) for the three different goals. The final row indicates the number of inputs that the output unit's operation depends on (the relevant inputs). (C) Same as (A) but the third task (G3) now involves pulling both levers if and only if both cues occur together. Note that the feedback from the three goals is the same as in (A) but there is an extra layer in the network. (D) Same as (B) but for the network in (C). (E) Similar to (A,C) but with four different goals: L1 should be pulled for C1 and L2 for C2 (G1), L2 for C1 and L1 for C2 (G2), L1 for either C1 or C2 (G3), and L2 either C1 or C2 (G4). G* is not actually one of the four goals but instead always provides feedback (each of the goals could instead provide this feedback). The feedback from the goals is no longer only to output units. (F) Same as (B,D) but for the network in (E).

Mentions: Figure 6 shows examples of networks that can perform possible stimulus-response experiments, where switching between different rules or goals, is required. Each of the networks in Figures 6A–C, has two different percepts (stimulus cues) as inputs, two different actions (levers to pull) as outputs, and a number of different goals, rules, or stimulus-response mappings that direct how these inputs lead to different outputs. The tables correspond to the binary functions, gi(x;;), where i corresponds to the two levers, or output units, x denotes the cues that are present, and and denote the initiating and orchestrating feedback, respectively, from the goal units.


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)

Stimulus-response Tasks. (A) An example of how the cortical architecture would be utilized for a stimulus-response task where a subject pulls one of two levers when presented with one of two sensory cues. The task switches between one of three “goals”: lever L1 should be pulled for cue C1 and cue C2 ignored (G1), L2 for C2 and C1 ignored (G2), and L1 for C1 and L2 for C2 (G3). Network activity is shown for when G1 is active. Similarly, the arrows to the cue units from the left and those leaving the lever units depict the inputs and outputs of the network (i.e., only “active” connections) for a particular set of inputs. (B) Input-output tables for the network shown in (A) for the three different goals. The final row indicates the number of inputs that the output unit's operation depends on (the relevant inputs). (C) Same as (A) but the third task (G3) now involves pulling both levers if and only if both cues occur together. Note that the feedback from the three goals is the same as in (A) but there is an extra layer in the network. (D) Same as (B) but for the network in (C). (E) Similar to (A,C) but with four different goals: L1 should be pulled for C1 and L2 for C2 (G1), L2 for C1 and L1 for C2 (G2), L1 for either C1 or C2 (G3), and L2 either C1 or C2 (G4). G* is not actually one of the four goals but instead always provides feedback (each of the goals could instead provide this feedback). The feedback from the goals is no longer only to output units. (F) Same as (B,D) but for the network in (E).
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Related In: Results  -  Collection

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Figure 6: Stimulus-response Tasks. (A) An example of how the cortical architecture would be utilized for a stimulus-response task where a subject pulls one of two levers when presented with one of two sensory cues. The task switches between one of three “goals”: lever L1 should be pulled for cue C1 and cue C2 ignored (G1), L2 for C2 and C1 ignored (G2), and L1 for C1 and L2 for C2 (G3). Network activity is shown for when G1 is active. Similarly, the arrows to the cue units from the left and those leaving the lever units depict the inputs and outputs of the network (i.e., only “active” connections) for a particular set of inputs. (B) Input-output tables for the network shown in (A) for the three different goals. The final row indicates the number of inputs that the output unit's operation depends on (the relevant inputs). (C) Same as (A) but the third task (G3) now involves pulling both levers if and only if both cues occur together. Note that the feedback from the three goals is the same as in (A) but there is an extra layer in the network. (D) Same as (B) but for the network in (C). (E) Similar to (A,C) but with four different goals: L1 should be pulled for C1 and L2 for C2 (G1), L2 for C1 and L1 for C2 (G2), L1 for either C1 or C2 (G3), and L2 either C1 or C2 (G4). G* is not actually one of the four goals but instead always provides feedback (each of the goals could instead provide this feedback). The feedback from the goals is no longer only to output units. (F) Same as (B,D) but for the network in (E).
Mentions: Figure 6 shows examples of networks that can perform possible stimulus-response experiments, where switching between different rules or goals, is required. Each of the networks in Figures 6A–C, has two different percepts (stimulus cues) as inputs, two different actions (levers to pull) as outputs, and a number of different goals, rules, or stimulus-response mappings that direct how these inputs lead to different outputs. The tables correspond to the binary functions, gi(x;;), where i corresponds to the two levers, or output units, x denotes the cues that are present, and and denote the initiating and orchestrating feedback, respectively, from the goal units.

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