Decision-making dynamics and behavior of a parietal-prefrontal loop model
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1
Yale University, School of Medicine, United States
Single-units studies with behaving monkeys have revealed that the posterior parietal cortex area LIP and the prefrontal cortex (PFC) are critically involved in cognitive functions such as working memory and decision making, and similar neural signals are often found in these areas. Computational models propose that the observed activity patterns, such as persistent or ramping activity, are generated by a strongly recurrent (multistable attractor) network, but it remains unclear whether such neural signals originate within each area or arise from the reciprocal loop interactions. It is also poorly understood what the differential circuit properties and functions of these regions are. To explore these questions, we considered a model of mutually interacting parietal and prefrontal modules. Each module contains excitatory populations selective for different choice options that compete with each other through a local inhibitory pool. By varying the strength of local recurrent excitation, each module can operate in different regimes, i.e. exhibiting attractor dynamics or not. The modules are connected via long-range excitatory projections to both excitatory (E) and inhibitory (I) populations, determining the strength and balance of E and I connections. This framework allows us to explore the different patterns of local and long-range connections in a systematic way, and to characterize the resulting networks behaviors. We find new emergent behaviors when two modules are reciprocally connected. First, when none of the modules individually behaves as an attractor network, the long-range interactions (providing recurrent excitation between the two modules) can give rise to slow ramping activity underlying time integration and self-sustained activity for working memory storage. Second, we find that coupling between the two modules can enlarge their domain of bistability. Thus, a local area can be shifted in and out of the attractor regime by a change in the effective inter-areal connectivity, providing a mechanism to control and gate the dynamical states of the different areas. Third, the two modules can reach different choices (one module selects option A, whereas the other module selects B). Such conflict states are observed when both modules are strongly recurrent and the long-range projections are biased toward targeting inhibitory cells, in hard decisions with noisy sensory information—a situation possibly relevant in complex decision tasks. These results reveal that novel computational behaviors can result from synaptic interactions between different areas in the brain. Importantly, the global behavior is not predetermined by the individual states (attractors or not) of the two local circuits in isolation. Furthermore, we envisage that different global functional states can be achieved depending on task-specific control inputs to the different modules. The coupling between LIP and PFC thus provide them with rich gating mechanisms. In this way the system can show flexible and adaptive behavior in accordance with the task at hand.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session III
Citation:
Andrieux
D and
Wang
X
(2010). Decision-making dynamics and behavior of a parietal-prefrontal loop model.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00130
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Received:
01 Mar 2010;
Published Online:
01 Mar 2010.
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Correspondence:
David Andrieux, Yale University, School of Medicine, New Haven, United States, David.Andrieux@ulb.ac.be