Neuromodulation and Neural Circuit Performativity: Adequacy Conditions for Their Computational Modelling

Author(s):  
Roberto Prevete ◽  
Guglielmo Tamburrini
2019 ◽  
Author(s):  
Jaejoong Kim ◽  
Bumseok Jeong

AbstractIn many decision-making situations, uncertainty facilitates suboptimal choices. However, when individuals are in a socially dangerous situation such that wrong choice would lead to a social punishment such as blame of the supervisor, they might try to minimize suboptimal choices to avoid it. In this functional MRI study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. Using computational models of behavior, we found that participants optimized their decision by suppressing the decision noise induced by uncertainty. Simultaneously, expecting blame significantly deteriorated participants’ mood. Model-based fMRI analyses and dynamic causal modeling revealed that the optimization mechanism based on the expectation of being blamed was controlled by a neural circuit centered on right medial prefrontal cortex. These results show novel behavioral and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed that negatively influences mood.Significance StatementPeople occasionally encounter a situation that forces us to make an optimal decision under uncertainty, which is difficult, and a failure to make a good choice might be blamed by their supervisor. Although it might be hard to make right decision, they make more effort to make a good decision, which might help them to escape from the aversive outcome. However, such kind of stressful situation influences our mood to be negative. Using the computational modelling, we showed that participants computed how it is likely to be blamed and this computation motivated people to control uncertainty-induced decision noise by recruiting a neural circuit centered on the medial prefrontal cortex. However, an expectation of being blamed significantly deteriorated participants’ mood.


2019 ◽  
Author(s):  
Sean E. Cavanagh ◽  
Norman H. Lam ◽  
John D. Murray ◽  
Laurence T. Hunt ◽  
Steven W. Kennerley

AbstractDecision-making biases can be systematic features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases in health and disease. Monkeys performed an evidence integration task in which they showed a pro-variance bias (PVB): a preference to choose options with more variable evidence. The PVB was also present in a spiking circuit model, revealing a neural mechanism for this behaviour. Because NMDA receptor (NMDA-R) hypofunction is a leading hypothesis for neuropathology in schizophrenia, we simulated behavioural effects of NMDA-R hypofunction onto either excitatory or inhibitory neurons in the model. These were tested experimentally using the NMDA-R antagonist ketamine, yielding changes in decision-making consistent with lowered cortical excitation/inhibition balance from NMDA-R hypofunction onto excitatory neurons. These results provide a circuit-level mechanism that bridges across explanatory scales, from the synaptic to the behavioural, in neuropsychiatric disorders where decision-making biases are prominent.SignificancePeople can make apparently irrational decisions because of underlying features in their decision circuitry. Deficits in the same neural circuits may also underlie debilitating cognitive symptoms of neuropsychiatric patients. Here, we reveal a neural circuit mechanism explaining an irrationality frequently observed in healthy humans making binary choices – the pro-variance bias. Our circuit model could be perturbed by introducing deficits in either excitatory or inhibitory neuron function. These two perturbations made specific, dissociable predictions for the types of irrational decisionmaking behaviour produced. We used the NMDA-R antagonist ketamine, an experimental model for schizophrenia, to test if these predictions were relevant to neuropsychiatric pathophysiology. The results were consistent with impaired excitatory neuron function, providing important new insights into the pathophysiology of schizophrenia.


2020 ◽  
Author(s):  
Zhaoxi Sun

Host-guest binding remains a major challenge in modern computational modelling. The newest 7<sup>th</sup> statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and predict the binding affinities in all three host-guest binding cases of the 6<sup>th</sup> SAMPL challenge. In this work, we employed the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The predicted binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations.


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