Network Model Of Prefrontal Cortex Research Articles

Adaptive chunking improves effective working memory capacity in a prefrontal cortex and basal ganglia circuit.

How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). Here we show that a neural network model of prefrontal cortex and basal ganglia can learn to reuse the same prefrontal populations to store multiple items, leading to resource-like constraints within a slot-like system, and inducing a trade-off between quantity and precision of information. Such "chunking" strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. Moreover, adaptive performance requires a dynamic range of dopaminergic signals to adjust striatal gating policies, providing a new interpretation of WM difficulties in patient populations such as Parkinson's disease, ADHD and schizophrenia. These simulations also suggest a computational rather than anatomical limit to WM capacity.

An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity.

Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short-term memory (STM) and long-term memory (LTM) interactions for WM. Here, we investigate these using a novel multiarea spiking neural network model of prefrontal cortex (PFC) and two parietotemporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain, and update multimodal LTM representations. Our simulations demonstrate how simultaneous, brief multimodal memory cues could build a temporary joint memory representation as an “index” in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby also the associated LTM representations. Cueing one LTM item rapidly pattern completes the associated uncued item via PFC. The PFC–STM network updates flexibly as new stimuli arrive, thereby gradually overwriting older representations.

Application of a neural network model of prefrontal cortex to emulate human probability matching behavior

Abstract Probability matching behavior occurs in a variety of decision-making domains that can be mapped to the n -arm bandit problem. Prefrontal cortex has been implicated in executive control over several tasks including the n -arm bandit problem. Previously the Prefrontal cortex Basal Ganglia Working Memory (PBWM) model has been used to replicate other decision-making functions of prefrontal cortex such as recognizing sequences of symbols or visual scenes. In this work, we emulate probability matching behavior from human subjects using the PBWM model in n -arm bandit-like problems. Possible extensions to the current work such as including other biases like loss aversion and misperception of both large gains and losses are also discussed.

Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex.

The prefrontal cortex (PFC) is critically involved in working memory, which underlies memory-guided, goal-directed behavior. During working-memory tasks, PFC neurons exhibit sustained elevated activity, which may reflect the active holding of goal-related information or the preparation of forthcoming actions. Dopamine via the D1 receptor strongly modulates both this sustained (delay-period) activity and behavioral performance in working-memory tasks. However, the function of dopamine during delay-period activity and the underlying neural mechanisms are only poorly understood. Recently we proposed that dopamine might stabilize active neural representations in PFC circuits during tasks involving working memory and render them robust against interfering stimuli and noise. To further test this idea and to examine the dopamine-modulated ionic currents that could give rise to increased stability of neural representations, we developed a network model of the PFC consisting of multicompartment neurons equipped with Hodgkin-Huxley-like channel kinetics that could reproduce in vitro whole cell and in vivo recordings from PFC neurons. Dopaminergic effects on intrinsic ionic and synaptic conductances were implemented in the model based on in vitro data. Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. Predictions of the model can be tested in vivo by locally applying specific D1 receptor, NMDA, or GABA(A) antagonists while recording from PFC neurons in delayed reaction-type tasks with interfering stimuli.