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.

Lipopolysaccharide effects on neuronal activity in rat basal forebrain and hypothalamus during sleep and waking

2000 ◽  
Vol 278 (3) ◽  
pp. R620-R627
Author(s):  
Xinzheng Xi ◽  
Linda A. Toth
Keyword(s):  
Neuronal Activity ◽  
Basal Forebrain ◽  
Preoptic Area ◽  
Intraperitoneal Administration ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Firing Patterns ◽  
Firing Rates ◽  
Neuronal Mechanisms ◽  
The Brain

Peripheral administration of lipopolysaccharide (LPS) is associated with alterations in sleep and the electroencephalogram. To evaluate potential neuronal mechanisms for the somnogenic effects of LPS administration, we used unanesthetized rats to survey the firing patterns of neurons in various regions of rat basal forebrain (BF) and hypothalamus during spontaneous sleep and waking and during the epochs of sleep and waking that occurred after the intraperitoneal administration of LPS. In the brain regions studied, LPS administration was associated with altered firing rates in 39% of the neurons examined. A larger proportion of LPS-responsive units showed vigilance-related alterations in firing rates compared with nonresponsive units. Approximately equal proportions of LPS-responsive neurons showed increased and decreased firing rates after LPS administration, with some units in the lateral preoptic area of the hypothalamus showing particularly robust increases. These findings are consistent with other studies showing vigilance-related changes in neuronal activity in various regions of BF and hypothalamus and further demonstrate that peripheral LPS administration alters neuronal firing rates in these structures during both sleep and waking.

Do the suprachiasmatic nuclei oscillate in old rats as they do in young ones?

1993 ◽  
Vol 265 (5) ◽  
pp. R1216-R1222 ◽  
Author(s):  
E. Satinoff ◽  
H. Li ◽  
T. K. Tcheng ◽  
C. Liu ◽  
A. J. McArthur ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Brain Slices ◽  
Neuronal Firing ◽  
Suprachiasmatic Nuclei ◽  
Firing Rates ◽  
Behavioral Rhythms ◽  
Behavioral Rhythm ◽  
Old Rats ◽  
The Brain

The basis of the decline in circadian rhythms with aging was addressed by comparing the patterns of three behavioral rhythms in young and old rats with the in vitro rhythm of neuronal activity in the suprachiasmatic nuclei (SCN), the primary circadian pacemaker. In some old rats, rhythms of body temperature, drinking, and activity retained significant 24-h periodicities in entraining light-dark cycles; in others, one or two of the rhythms became aperiodic. When these rats were 23-27.5 mo old they were killed, and single-unit firing rates in SCN brain slices were recorded continuously for 30 h. There was significant damping of mean peak neuronal firing rates in old rats compared with young. SCN neuronal activities were analyzed with reference to previous entrained behavioral rhythm patterns of individual rats as well. Neuronal activity from rats with prior aperiodic behavioral rhythms was erratic, as expected. Neuronal activity from rats that were still maintaining significant 24-h behavioral rhythmicity at the time they were killed was erratic in most cases but normally rhythmic in others. Thus there was no more congruence between the behavioral rhythms and the brain slice rhythms than there was among the behavioral rhythms alone. These results, the first to demonstrate aberrant SCN firing patterns and a decrease in amplitude in old rats, imply that aging could either disrupt coupling between SCN pacemaker cells or their output, or cause deterioration of the pacemaking properties of SCN cells.

scholarly journals Does High Firing Irregularity Enhance Learning?

2011 ◽  
Vol 23 (3) ◽  
pp. 656-663 ◽  
Author(s):  
Chris Christodoulou ◽  
Aristodemos Cleanthous
Keyword(s):  
Neural Networks ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Benchmark Problem ◽  
Integrate And Fire ◽  
Eligibility Trace ◽  
Firing Irregularity ◽  
The Brain ◽  
High Firing ◽  
Integrate And Fire Neuron

In this note, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high rates (Bugmann, Christodoulou, & Taylor, 1997 ; Christodoulou & Bugmann, 2001 ), enhances learning. More specifically, it enhances reward-modulated spike-timing-dependent plasticity with eligibility trace when used in spiking neural networks, as shown by the results when tested in the simple benchmark problem of XOR, as well as in a complex multiagent setting task.

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 Decision-Making, Errors, and Confidence in the Brain

2010 ◽  
Vol 104 (5) ◽  
pp. 2359-2374 ◽  
Author(s):  
Edmund T. Rolls ◽  
Fabian Grabenhorst ◽  
Gustavo Deco
Keyword(s):  
Decision Making ◽  
Neuronal Activity ◽  
Blood Oxygen Level Dependent ◽  
Linear Increase ◽  
Control Area ◽  
Neuronal Responses ◽  
Blood Oxygen Level ◽  
Decision Confidence ◽  
Fmri Study ◽  
Continuous Scale

To provide a fundamental basis for understanding decision-making and decision confidence, we analyze a neuronal spiking attractor-based model of decision-making. The model predicts probabilistic decision-making with larger neuronal responses and larger functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) responses on correct than on error trials because the spiking noise-influenced decision attractor state of the network is consistent with the external evidence. Moreover, the model predicts that the neuronal activity and the BOLD response will become larger on correct trials as the discriminability Δ I increases and confidence increases and will become smaller as confidence decreases on error trials as Δ I increases. Confidence is thus an emergent property of the model. In an fMRI study of an olfactory decision-making task, we confirm these predictions for cortical areas including medial prefrontal cortex and the cingulate cortex implicated in choice decision-making, showing a linear increase in the BOLD signal with Δ I on correct trials, and a linear decrease on error trials. These effects were not found in a control area, the orbitofrontal cortex, where reward value useful for the choice is represented on a continuous scale but that is not implicated in the choice itself. This provides a unifying approach to decision-making and decision confidence and to how spiking-related noise affects choice, confidence, synaptic and neuronal activity, and fMRI signals.

scholarly journals Brittleness in model selection analysis of single neuron firing rates

2018 ◽  
Author(s):  
Chandramouli Chandrasekaran ◽  
Joana Soldado-Magraner ◽  
Diogo Peixoto ◽  
William T. Newsome ◽  
Krishna V. Shenoy ◽  
...  
Keyword(s):  
Decision Making ◽  
Model Selection ◽  
Single Neuron ◽  
Heterogeneous Systems ◽  
Selection Methods ◽  
Neural Signals ◽  
Neural Data ◽  
Firing Rates ◽  
Data Matching ◽  
The Brain

AbstractModels of complex heterogeneous systems like the brain are inescapably incomplete, and thus always falsified with enough data. As neural data grow in volume and complexity, absolute measures of adequacy are being replaced by model selection methods that rank the relative accuracy of competing theories. Selection still depends on incomplete mathematical instantiations, but the implicit expectation is that ranking is robust to their details. Here we highlight a contrary finding of “brittleness,” where data matching one theory conceptually are ranked closer to an instance of another. In particular, selection between recent models of decision making is conceptually misleading when data are simulated with minor distributional mismatch, with mixed secondary signals, or with non-stationary parameters; and decision-related responses in macaque cortex show features suggesting that these effects may impact empirical results. We conclude with recommendations to mitigate such brittleness when using model selection to study neural signals.

Neuroscience: The New English Major?

2021 ◽  
pp. 107385842110039
Author(s):  
Kristin F. Phillips ◽  
Harald Sontheimer
Keyword(s):  
Decision Making ◽  
Graduate Education ◽  
Human Behavior ◽  
Undergraduate Curriculum ◽  
Virginia Tech ◽  
Bachelor's Degree ◽  
State University ◽  
Human Interactions ◽  
English Major ◽  
The Brain

Once strictly the domain of medical and graduate education, neuroscience has made its way into the undergraduate curriculum with over 230 colleges and universities now offering a bachelor’s degree in neuroscience. The disciplinary focus on the brain teaches students to apply science to the understanding of human behavior, human interactions, sensation, emotions, and decision making. In this article, we encourage new and existing undergraduate neuroscience programs to envision neuroscience as a broad discipline with the potential to develop competencies suitable for a variety of careers that reach well beyond research and medicine. This article describes our philosophy and illustrates a broad-based undergraduate degree in neuroscience implemented at a major state university, Virginia Tech. We highlight the fact that the research-centered Experimental Neuroscience major is least popular of our four distinct majors, which underscores our philosophy that undergraduate neuroscience can cater to a different audience than traditionally thought.

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