The somatosensory system

2020 ◽  
pp. 232-243
Author(s):  
Edmund T. Rolls

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.

2020 ◽  
pp. 176-191
Author(s):  
Edmund T. Rolls

The dorsal visual system computes information about where objects are in space, and their motion, and this is used for actions performed in space. This requires coordinate transforms from retinal coordinates to head based coordinates, and then in parietal cortex areas to coordinates for reaching into space, and for allocentric, world-based, spatial coordinates. Recent approaches to how these transforms are performed, with analogies to transform invariance learning in the ventral visual system, are described.


2019 ◽  
pp. 237-256
Author(s):  
Edmund T. Rolls

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.


Author(s):  
Susanne Koot ◽  
Magdalini Koukou ◽  
Annemarie Baars ◽  
Peter Hesseling ◽  
José van ’t Klooster ◽  
...  

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
David Wisniewski ◽  
Birte Forstmann ◽  
Marcel Brass

AbstractValue-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding.


2019 ◽  
Author(s):  
Bhargav Teja Nallapu ◽  
Frédéric Alexandre

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.


eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sean E Cavanagh ◽  
Joni D Wallis ◽  
Steven W Kennerley ◽  
Laurence T Hunt

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.


2013 ◽  
Vol 24 ◽  
pp. e28
Author(s):  
Lena Wischhof ◽  
Kerstin Wernecke ◽  
Ellen Irrsack ◽  
Malte Feja ◽  
Michael Koch

2010 ◽  
Vol 104 (1) ◽  
pp. 539-547 ◽  
Author(s):  
Andrea Insabato ◽  
Mario Pannunzi ◽  
Edmund T. Rolls ◽  
Gustavo Deco

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.


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