scholarly journals An Integrate-and-fire Model of Prefrontal Cortex Neuronal Activity during Performance of Goal-directed Decision Making

2005 ◽  
Vol 15 (12) ◽  
pp. 1964-1981 ◽  
Author(s):  
Randal A. Koene ◽  
Michael E. Hasselmo
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Neuronal Activity ◽  
Fire Model ◽  
Integrate And Fire

scholarly journals Dissociable roles of cortical excitation-inhibition balance during patch-leaving versus value-guided decisions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luca F. Kaiser ◽  
Theo O. J. Gruendler ◽  
Oliver Speck ◽  
Lennart Luettgau ◽  
Gerhard Jocham
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Neuronal Activity ◽  
Ventromedial Prefrontal Cortex ◽  
Anterior Cingulate ◽  
Mutual Inhibition ◽  
Dorsal Anterior Cingulate Cortex ◽  
Cortical Excitation ◽  
The Cost

AbstractIn a dynamic world, it is essential to decide when to leave an exploited resource. Such patch-leaving decisions involve balancing the cost of moving against the gain expected from the alternative patch. This contrasts with value-guided decisions that typically involve maximizing reward by selecting the current best option. Patterns of neuronal activity pertaining to patch-leaving decisions have been reported in dorsal anterior cingulate cortex (dACC), whereas competition via mutual inhibition in ventromedial prefrontal cortex (vmPFC) is thought to underlie value-guided choice. Here, we show that the balance between cortical excitation and inhibition (E/I balance), measured by the ratio of GABA and glutamate concentrations, plays a dissociable role for the two kinds of decisions. Patch-leaving decision behaviour relates to E/I balance in dACC. In contrast, value-guided decision-making relates to E/I balance in vmPFC. These results support mechanistic accounts of value-guided choice and provide evidence for a role of dACC E/I balance in patch-leaving decisions.

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 Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex

1999 ◽  
Vol 11 (4) ◽  
pp. 935-951 ◽  
Author(s):  
Shigeru Shinomoto ◽  
Yutaka Sakai ◽  
Shintaro Funahashi
Keyword(s):  
Prefrontal Cortex ◽  
Order Statistics ◽  
Cortical Neurons ◽  
Biological Data ◽  
Higher Order Statistics ◽  
Uhlenbeck Process ◽  
Fire Model ◽  
Integrate And Fire ◽  
Spike Sequences ◽  
Ornstein Uhlenbeck Process

Cortical neurons of behaving animals generate irregular spike sequences. Recently, there has been a heated discussion about the origin of this irregularity. Softky and Koch (1993) pointed out the inability of standard single-neuron models to reproduce the irregularity of the observed spike sequences when the model parameters are chosen within a certain range that they consider to be plausible. Shadlen and Newsome (1994), on the other hand, demonstrated that a standard leaky integrate-and-fire model can reproduce the irregularity if the inhibition is balanced with the excitation. Motivated by this discussion, we attempted to determine whether the Ornstein-Uhlenbeck process, which is naturally derived from the leaky integration assumption, can in fact reproduce higher-order statistics of biological data. For this purpose, we consider actual neuronal spike sequences recorded from the monkey prefrontal cortex to calculate the higher-order statistics of the interspike intervals. Consistency of the data with the model is examined on the basis of the coefficient of variation and the skewness coefficient, which are, respectively, a measure of the spiking irregularity and a measure of the asymmetry of the interval distribution. It is found that the biological data are not consistent with the model if the model time constant assumes a value within a certain range believed to cover all reasonable values. This fact suggests that the leaky integrate-and-fire model with the assumption of uncorrelated inputs is not adequate to account for the spiking in at least some cortical neurons.

scholarly journals Predicting neuronal activity with an adaptive exponential integrate-and-fire model

2007 ◽  
Vol 8 (Suppl 2) ◽  
pp. P121
Author(s):  
Nicolas Marcille ◽  
Claudia Clopath ◽  
Rajnish Ranjan ◽  
Shaul Druckmann ◽  
Felix Schuermann ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Fire Model ◽  
Integrate And Fire

scholarly journals Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments

2007 ◽  
Vol 70 (10-12) ◽  
pp. 1668-1673 ◽  
Author(s):  
Claudia Clopath ◽  
Renaud Jolivet ◽  
Alexander Rauch ◽  
Hans-Rudolf Lüscher ◽  
Wulfram Gerstner
Keyword(s):  
Neuronal Activity ◽  
Fire Model ◽  
Integrate And Fire ◽  
Simple Models

scholarly journals Dissociable roles of cortical excitation-inhibition balance during patch-leaving versus value-guided decisions

2019 ◽  
Author(s):  
Luca F. Kaiser ◽  
Theo O.J. Gruendler ◽  
Oliver Speck ◽  
Lennart Luettgau ◽  
Gerhard Jocham
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Neuronal Activity ◽  
Ventromedial Prefrontal Cortex ◽  
Anterior Cingulate ◽  
Mutual Inhibition ◽  
Dorsal Anterior Cingulate Cortex ◽  
Cortical Excitation ◽  
The Cost

AbstractIn a dynamic world, it is essential to decide when to leave an exploited resource. Such patch-leaving decisions involve balancing the cost of moving against the gain expected from the alternative patch. This is in contrast with value-guided decisions that typically involve maximizing reward by selecting the current best option. Patterns of neuronal activity pertaining to patch-leaving decisions have been reported in the dorsal anterior cingulate cortex (dACC), whereas competition via mutual inhibition in the ventromedial prefrontal cortex (vmPFC) is thought to underlie value-guided choice. Here, we show that the balance between cortical excitation and inhibition (E/I balance), measured by the ratio of GABA and glutamate concentrations, plays a dissociable role for the two kinds of decisions. Patch-leaving decision behaviour was related to E/I balance in dACC. In contrast, value-guided decision making was related to E/I balance in vmPFC. These results support previous mechanistic accounts of value-guided choice and provide novel evidence for a role of dACC E/I balance in patch-leaving decisions.

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