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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-matching Experiment. (A) Network for responding to a stimulus (C1, C2 or any other stimulus CX), which is dependent on the current rule that determines whether to respond to a match or non-match with the previous stimulus. In contrast to previous tasks, working memory is required to remember the previous stimulus (P1 and P2 which correspond to the same stimulus as C1 and C2, respectively) in the same way that the rules (R1 and R2) are remembered. (B) Input-output table for the network presented with a stimulus for combinations of rules and remembered stimuli. (C) Same network as in (A) but with units assigned to anatomical regions of the cortex.
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Figure 10: Stimulus-matching Experiment. (A) Network for responding to a stimulus (C1, C2 or any other stimulus CX), which is dependent on the current rule that determines whether to respond to a match or non-match with the previous stimulus. In contrast to previous tasks, working memory is required to remember the previous stimulus (P1 and P2 which correspond to the same stimulus as C1 and C2, respectively) in the same way that the rules (R1 and R2) are remembered. (B) Input-output table for the network presented with a stimulus for combinations of rules and remembered stimuli. (C) Same network as in (A) but with units assigned to anatomical regions of the cortex.

Mentions: Wallis and Miller (2003) and Muhammad et al. (2006) recorded from neurons in the prefrontal (PRC), premotor (PMC), and inferior temporal (ITC) cortices and the striatum (STR) during a stimulus-matching task. During the task, two visual stimuli were presented and, depending on the rule (which was indicated via a visual cue presented with the first stimulus), the subject was required to either continue holding a lever or release the lever. They observed different neurons that responded selectively to the rule (desired stimulus-response mapping), the behavioral response carried out, the visual stimulus being remembered, or whether the subsequent stimulus matched this remembered stimulus. We constructed a possible network to carry out this task (Figures 10A,B). In addition to external feedback that depends on the rule to be employed, this network receives external feedback based on the stimulus being remembered (held in working memory). Based on the selectivity that was observed of neurons in different cortical regions, we divided this network into these different regions (Figure 10C). While this is not necessarily the exact network used for this task or the correct allocation of units to cortical regions, this demonstrates how our model may be useful in understanding the role of neurons in different regions.


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-matching Experiment. (A) Network for responding to a stimulus (C1, C2 or any other stimulus CX), which is dependent on the current rule that determines whether to respond to a match or non-match with the previous stimulus. In contrast to previous tasks, working memory is required to remember the previous stimulus (P1 and P2 which correspond to the same stimulus as C1 and C2, respectively) in the same way that the rules (R1 and R2) are remembered. (B) Input-output table for the network presented with a stimulus for combinations of rules and remembered stimuli. (C) Same network as in (A) but with units assigned to anatomical regions of the cortex.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Stimulus-matching Experiment. (A) Network for responding to a stimulus (C1, C2 or any other stimulus CX), which is dependent on the current rule that determines whether to respond to a match or non-match with the previous stimulus. In contrast to previous tasks, working memory is required to remember the previous stimulus (P1 and P2 which correspond to the same stimulus as C1 and C2, respectively) in the same way that the rules (R1 and R2) are remembered. (B) Input-output table for the network presented with a stimulus for combinations of rules and remembered stimuli. (C) Same network as in (A) but with units assigned to anatomical regions of the cortex.
Mentions: Wallis and Miller (2003) and Muhammad et al. (2006) recorded from neurons in the prefrontal (PRC), premotor (PMC), and inferior temporal (ITC) cortices and the striatum (STR) during a stimulus-matching task. During the task, two visual stimuli were presented and, depending on the rule (which was indicated via a visual cue presented with the first stimulus), the subject was required to either continue holding a lever or release the lever. They observed different neurons that responded selectively to the rule (desired stimulus-response mapping), the behavioral response carried out, the visual stimulus being remembered, or whether the subsequent stimulus matched this remembered stimulus. We constructed a possible network to carry out this task (Figures 10A,B). In addition to external feedback that depends on the rule to be employed, this network receives external feedback based on the stimulus being remembered (held in working memory). Based on the selectivity that was observed of neurons in different cortical regions, we divided this network into these different regions (Figure 10C). While this is not necessarily the exact network used for this task or the correct allocation of units to cortical regions, this demonstrates how our model may be useful in understanding the role of neurons in different regions.

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