Orbitofrontal cortex computations in a systems-level perspective

2019 ◽  
pp. 237-256
Author(s):  
Edmund T. Rolls
Keyword(s):  
Decision Making ◽  
Computational Model ◽  
Orbitofrontal Cortex ◽  
Neuronal Firing ◽  
Computational Approaches ◽  
Attractor Network ◽  
Attractor Networks ◽  
Association Learning ◽  
Integrate And Fire ◽  
Theory And Model

This chapter describes some of the computational approaches that are useful to understand the functions of the orbitofrontal cortex. Section 9.1 describes the operation of pattern association networks which may be used in the orbitofrontal cortex to associate the sight of a stimulus with its taste. Section 9.2 describes the operation of autoassociation or attractor networks which may be used in the orbitofrontal cortex to maintain a rule online by continuing neuronal firing. Section 9.3 describes the operation of the integrate-and-fire attractor network used to model probabilistic decision-making. Section 9.4 describes a neurophysiological and computational model for stimulus-reinforcer association learning and reversal in the orbitofrontal cortex. Section 9.5 describes a theory and model of how non-reward neurons are produced in the orbitofrontal cortex.

scholarly journals Interacting roles of lateral and medial Orbitofrontal cortex in decision-making and learning : A system-level computational model

2019 ◽  
Author(s):  
Bhargav Teja Nallapu ◽  
Frédéric Alexandre
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Computational Model ◽  
Orbitofrontal Cortex ◽  
Temporal Dynamics ◽  
System Level ◽  
Interacting Networks ◽  
Cortical Regions ◽  
Experimental Findings

AbstractIn the context of flexible and adaptive animal behavior, the orbitofrontal cortex (OFC) is found to be one of the crucial regions in the prefrontal cortex (PFC) influencing the downstream processes of decision-making and learning in the sub-cortical regions. Although OFC has been implicated to be important in a variety of related behavioral processes, the exact mechanisms are unclear, through which the OFC encodes or processes information related to decision-making and learning. Here, we propose a systems-level view of the OFC, positioning it at the nexus of sub-cortical systems and other prefrontal regions. Particularly we focus on one of the most recent implications of neuroscientific evidences regarding the OFC - possible functional dissociation between two of its sub-regions : lateral and medial. We present a system-level computational model of decision-making and learning involving the two sub-regions taking into account their individual roles as commonly implicated in neuroscientific studies. We emphasize on the role of the interactions between the sub-regions within the OFC as well as the role of other sub-cortical structures which form a network with them. We leverage well-known computational architecture of thalamo-cortical basal ganglia loops, accounting for recent experimental findings on monkeys with lateral and medial OFC lesions, performing a 3-arm bandit task. First we replicate the seemingly dissociate effects of lesions to lateral and medial OFC during decision-making as a function of value-difference of the presented options. Further we demonstrate and argue that such an effect is not necessarily due to the dissociate roles of both the subregions, but rather a result of complex temporal dynamics between the interacting networks in which they are involved.Author summaryWe first highlight the role of the Orbitofrontal Cortex (OFC) in value-based decision making and goal-directed behavior in primates. We establish the position of OFC at the intersection of cortical mechanisms and thalamo-basal ganglial circuits. In order to understand possible mechanisms through which the OFC exerts emotional control over behavior, among several other possibilities, we consider the case of dissociate roles of two of its topographical subregions - lateral and medial parts of OFC. We gather predominant roles of each of these sub-regions as suggested by numerous experimental evidences in the form of a system-level computational model that is based on existing neuronal architectures. We argue that besides possible dissociation, there could be possible interaction of these sub-regions within themselves and through other sub-cortical structures, in distinct mechanisms of choice and learning. The computational framework described accounts for experimental data and can be extended to more comprehensive detail of representations required to understand the processes of decision-making, learning and the role of OFC and subsequently the regions of prefrontal cortex in general.

scholarly journals Confidence-Related Decision Making

2010 ◽  
Vol 104 (1) ◽  
pp. 539-547 ◽  
Author(s):  
Andrea Insabato ◽  
Mario Pannunzi ◽  
Edmund T. Rolls ◽  
Gustavo Deco
Keyword(s):  
Decision Making ◽  
Cognitive Functioning ◽  
Neuronal Activity ◽  
Emergent Property ◽  
Neuronal Responses ◽  
Attractor Network ◽  
Firing Rates ◽  
Integrate And Fire ◽  
The Brain ◽  
High Firing

Neurons have been recorded that reflect in their firing rates the confidence in a decision. Here we show how this could arise as an emergent property in an integrate-and-fire attractor network model of decision making. The attractor network has populations of neurons that respond to each of the possible choices, each biased by the evidence for that choice, and there is competition between the attractor states until one population wins the competition and finishes with high firing that represents the decision. Noise resulting from the random spiking times of individual neurons makes the decision making probabilistic. We also show that a second attractor network can make decisions based on the confidence in the first decision. This system is supported by and accounts for neuronal responses recorded during decision making and makes predictions about the neuronal activity that will be found when a decision is made about whether to stay with a first decision or to abort the trial and start again. The research shows how monitoring can be performed in the brain and this has many implications for understanding cognitive functioning.

The somatosensory system

2020 ◽  
pp. 232-243
Author(s):  
Edmund T. Rolls
Keyword(s):  
Decision Making ◽  
Visual System ◽  
Somatosensory Cortex ◽  
Parietal Cortex ◽  
Orbitofrontal Cortex ◽  
Somatosensory System ◽  
Attractor Network ◽  
System P ◽  
Affective Value ◽  
Somatosensory Stimuli

A hierarchical system through the somatosensory cortex builds representations of touch and of the positions of the limbs in space up through parietal cortex areas 5 and 7b, where the system is interfaced to the visual system for the computations involved in reaching into space and grasping objects. Attractor network mechanisms for decision-making between somatosensory stimuli are described. In the orbitofrontal cortex, the affective value of pleasant touch and of pain is represented.

scholarly journals The orbitofrontal cortex: reward, emotion and depression

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Edmund T Rolls ◽  
Wei Cheng ◽  
Jianfeng Feng
Keyword(s):  
Decision Making ◽  
Functional Connectivity ◽  
Orbitofrontal Cortex ◽  
Medial Temporal Lobe ◽  
Large Scale ◽  
Brain Area ◽  
Angular Gyrus ◽  
Special Role ◽  
Attractor Networks ◽  
Reward Value

Abstract The orbitofrontal cortex in primates including humans is the key brain area in emotion, and in the representation of reward value and in non-reward, that is not obtaining an expected reward. Cortical processing before the orbitofrontal cortex is about the identity of stimuli, i.e. ‘what’ is present, and not about reward value. There is evidence that this holds for taste, visual, somatosensory and olfactory stimuli. The human medial orbitofrontal cortex represents many different types of reward, and the lateral orbitofrontal cortex represents non-reward and punishment. Not obtaining an expected reward can lead to sadness, and feeling depressed. The concept is advanced that an important brain region in depression is the orbitofrontal cortex, with depression related to over-responsiveness and over-connectedness of the non-reward-related lateral orbitofrontal cortex, and to under-responsiveness and under-connectivity of the reward-related medial orbitofrontal cortex. Evidence from large-scale voxel-level studies and supported by an activation study is described that provides support for this hypothesis. Increased functional connectivity of the lateral orbitofrontal cortex with brain areas that include the precuneus, posterior cingulate cortex and angular gyrus is found in patients with depression and is reduced towards the levels in controls when treated with medication. Decreased functional connectivity of the medial orbitofrontal cortex with medial temporal lobe areas involved in memory is found in patients with depression. Some treatments for depression may act by reducing activity or connectivity of the lateral orbitofrontal cortex. New treatments that increase the activity or connectivity of the medial orbitofrontal cortex may be useful for depression. These concepts, and that of increased activity in non-reward attractor networks, have potential for advancing our understanding and treatment of depression. The focus is on the orbitofrontal cortex in primates including humans, because of differences of operation of the orbitofrontal cortex, and indeed of reward systems, in rodents. Finally, the hypothesis is developed that the orbitofrontal cortex has a special role in emotion and decision-making in part because as a cortical area it can implement attractor networks useful in maintaining reward and emotional states online, and in decision-making.

scholarly journals Experimental and Computational Approaches for Solubility Measurement of Pyridazinone Derivative in Binary (DMSO + Water) Systems

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 171 ◽  
Author(s):  
Faiyaz Shakeel ◽  
Sultan Alshehri ◽  
Mohd Imran ◽  
Nazrul Haq ◽  
Abdullah Alanazi ◽  
...  
Keyword(s):  
Computational Model ◽  
Mole Fraction ◽  
Research Work ◽  
Solvent System ◽  
Binary Solvent ◽  
Thermodynamic Evaluation ◽  
Computational Approaches ◽  
Neat Water ◽  
Poorly Soluble ◽  
Binary Solvent Systems

The current research work was performed to evaluate the solubilization behavior, solution thermodynamics, and solvation behavior of poorly soluble pyridazinone derivative i.e., 6-phenyl-pyridazin-3(2H)-one (PPD) in various binary solvent systems of dimethyl sulfoxide (DMSO) and water using experimental and various computational approaches. The solubility of PPD in various binary solvent system of DMSO and water was investigated within the temperature range T = 298.2 K to 318.2 K at constant air pressure p = 0.1 MPa, by employing an isothermal technique. The generated solubility data of PPD was computationally represented by five different cosolvency models including van’t Hoff, Apelblat, Yalkowsky–Roseman, Jouyban–Acree, and Jouyban–Acree–van’t Hoff models. The performance of each computational model for correlation studies was illustrated using root mean square deviations (RMSD). The overall RMSD value was obtained <2.0% for each computational model. The maximum solubility of PPD in mole fraction was recorded in neat DMSO (4.67 × 10−1 at T = 318.2 K), whereas the lowest one was obtained in neat water (5.82 × 10−6 at T = 298.2 K). The experimental solubility of PPD in mole fraction in neat DMSO was much higher than its ideal solubility, indicating the potential of DMSO for solubility enhancement of PPD. The computed values of activity coefficients showed maximum molecular interaction in PPD-DMSO compared with PPD-water. Thermodynamic evaluation showed an endothermic and entropy-driven dissolution of PPD in all the mixtures of DMSO and water. Additionally, enthalpy–entropy compensation evaluation indicated an enthalpy-driven mechanism as a driven mechanism for the solvation property of PPD.

scholarly journals Memory Dynamics in Attractor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guoqi Li ◽  
Kiruthika Ramanathan ◽  
Ning Ning ◽  
Luping Shi ◽  
Changyun Wen
Keyword(s):  
Energy Function ◽  
Stable Equilibrium ◽  
Equilibrium Points ◽  
Saddle Points ◽  
Memory Systems ◽  
Initial Stimulus ◽  
Attractor Network ◽  
Local Maxima ◽  
Attractor Networks ◽  
New Energy

As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented to the network, and its states converge to one of stable equilibrium points. Consequently, the existence of the spurious points, that is, local maxima, saddle points, or other local minima which are undesired memory patterns, can be avoided. The simulation results show the effectiveness of the proposed method.

scholarly journals Corticosterone and decision-making in male Wistar rats: the effect of corticosterone application in the infralimbic and orbitofrontal cortex

2014 ◽  
Vol 8 ◽  
Author(s):  
Susanne Koot ◽  
Magdalini Koukou ◽  
Annemarie Baars ◽  
Peter Hesseling ◽  
José van ’t Klooster ◽  
...  
Keyword(s):  
Decision Making ◽  
Orbitofrontal Cortex ◽  
Wistar Rats ◽  
Male Wistar Rats

scholarly journals A Computational Model of Non-optimal Suspiciousness in the Minnesota Trust Game

2021 ◽  
Author(s):  
Rebecca Kazinka ◽  
Iris Vilares ◽  
Angus MacDonald
Keyword(s):  
Decision Making ◽  
Risk Aversion ◽  
Computational Model ◽  
Inequity Aversion ◽  
Trust Game ◽  
Social Decision ◽  
Future Studies ◽  
Two Samples ◽  
Highly Correlated ◽  
Social Decision Making

This study modeled spite sensitivity (the worry that others are willing to incur a loss to hurt you), which is thought to undergird suspiciousness and persecutory ideation. Two samples performed a parametric, non-iterative trust game known as the Minnesota Trust Game (MTG). The MTG is designed to distinguish suspicious decision-making from otherwise rational mistrust by incentivizing the player to trust in certain situations. Individuals who do not trust even under these circumstances are particularly suspicious of their potential partner’s intentions. In Sample 1, 243 undergraduates who completed the MTG showed less trust as the amount of money they could lose increased. However, for choices where partners had a financial disincentive to betray the player, variation in the willingness to trust the partner was associated with suspicious beliefs. To further examine spite sensitivity, we modified the Fehr-Schmidt (1999) inequity aversion model, which compares unequal outcomes in social decision-making tasks, to include the possibility for spite sensitivity. In this case, an anticipated partner’s dislike of advantageous inequity (i.e., guilt) parameter could take on negative values, with negative guilt indicating spite. We hypothesized that the anticipated guilt parameter would be strongly related to suspicious beliefs. Our modification of the Fehr-Schmidt model improved estimation of MTG behavior. We isolated the estimation of partner’s spite-guilt, which was highly correlated with choices most associated with persecutory ideation. We replicated our findings in a second sample, where the estimated spite-guilt parameter correlated with self-reported suspiciousness. The “Suspiciousness” condition, unique to the MTG, can be modeled to isolate spite sensitivity, suggesting that spite sensitivity is separate from inequity aversion or risk aversion, and may provide a means to quantify persecution. The MTG offers promise for future studies to quantify persecutory beliefs in clinical populations.

scholarly journals Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sean E Cavanagh ◽  
Joni D Wallis ◽  
Steven W Kennerley ◽  
Laurence T Hunt
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Orbitofrontal Cortex ◽  
Receptive Fields ◽  
Neural Mechanism ◽  
Spike Rate ◽  
Accumulation Process ◽  
Single Neurons ◽  
Evidence Accumulation ◽  
Autocorrelation Structure

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.

scholarly journals Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

2019 ◽  
Author(s):  
Marwen Belkaid ◽  
Jeffrey L. Krichmar
Keyword(s):  
Decision Making ◽  
Dopaminergic Neurons ◽  
Cholinergic System ◽  
Dopamine Neurons ◽  
Comprehensive Understanding ◽  
Dopaminergic Activity ◽  
Integrate And Fire ◽  
Major Implication ◽  
Seeking Behavior

AbstractRecent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons – another neuromodulatory system known for its major implication in reinforcement learning and decision-making. In this paper, we propose a leaky-integrate-and-fire model of this mechanism. It implements a softmax-like selection with an uncertainty bonus by a cholinergic drive to dopaminergic neurons, which in turn influence synaptic currents of downstream neurons. The model is able to reproduce experimental data in two decision-making tasks. It also predicts that i) in the absence of cholinergic input, dopaminergic activity would not correlate with uncertainty, and that ii) the adaptive advantage brought by the implemented uncertainty-seeking mechanism is most useful when sources of reward are not highly uncertain. Moreover, this modeling work allows us to propose novel experiments which might shed new light on the role of acetylcholine in both random and directed exploration. Overall, this study thus contributes to a more comprehensive understanding of the roles of the cholinergic system and its involvement in decision-making in particular.

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