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Showing posts with the label basal ganglia

Metastability and Eye Movements: A Dynamical‑Systems Interpretation

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  1. Why metastability is a natural language for gaze behavior The idea that perception, attention, and neural activity evolve through transiently stable states has deep roots in cognitive science and neuroscience. Concepts such as attractors , basins , noise‑induced transitions , and escape times appear in work on perceptual switching, decision‑making, and neural population dynamics (Kelso, 1995; Rabinovich et al., 2008; Deco & Jirsa, 2012). Eye movements, especially the alternation between fixations and saccades ,   fit remarkably well into this metastable picture. The Freidlin–Wentzell theory of rare events (Freidlin & Wentzell, 2012) provides a rigorous mathematical language for these intuitions. 2. Fixations as metastable states  Consider the gaze position X t as the state of a stochastic dynamical system. During a fixation the  gaze remains confined to a small region, microsaccades and noise generate small fluctuations, and the gaze tends to return...
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INVOLVEMENT OF THE INFERIOR FRONTAL CORTEX IN THE LEVODOPA-INDUCED DYSKINESIAS

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Parkinson’s disease (PD) is one of the most prevailing neurodegenerative disorders affecting populations all over the world.  Almost from 4.1 to 4.6 million patients suffering from PD are counted across the  fifteen most crowded countries where  live the two thirds of the world population. The average age of the PD onset is 55. The incidence grows along with the age: from 20/100000 cases in the overall population to 120/100000 in the elder population above 70 years old.   This number is expected to reach 8.7 / 9.3 million in 2030. China is expected to increase from two million to five million by 2030. (E. R. Dorsey, et al.; Neurology 2007) Gender difference  in the rate of incidence of PD was reported in many epidemiological studies:  from 1.5 to 2 times higher in men than in women. In Japan, however, there is a higher incidence in women. Environmental factors are critical in determining the incidence rate of the disease. Some studies repo...
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A Two-Layered Diffusion Model Traces the Dynamics of Information Processing in the Valuation-and-Choice Circuit of Decision Making

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A circuit of evaluation and selection of the alternatives ( see here ) is considered a reliable model in neurobiology. In this published study , valuation and choice of a decisional process during Two-Alternative Forced-Choice ( TAFC ) task are represented as a two-layered network of computational cells, where information accrual and processing progress in nonlinear diffusion dynamics.  The evolution of the response-to-stimulus map is thus modeled by two linked diffusive modules ( 2LDM ) representing the neuronal populations involved in the valuation-and-decision circuit of decision making (Figure 1). Diffusion models are naturally appropriate for describing accumulation of evidence over the time [ see here ] . This allows the computation of the response times (RTs) in valuation and choice, under the hypothesis of ex-Wald distribution. A nonlinear transfer function integrates the activities of the two layers. The input-output map based on the infomax principle makes the 2LD...
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NEUROBIOLOGY OF DECISIONAL PROCESSES

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The exploration of the neurobiological bases of the cognitive processes that underlie the decision-making (DM) have been recently the object of many studies in neurophysiology and computational neuroscience. By tracing the neuronal circuits that are involved in the DM it is possible to get biophysically reliable models linking the dynamics of the neuronal activities to decisional behavior.  The present section recalls the major findings regarding this issue. Firstly, it is focused the role of the basal ganglia and the related model of reinforcement learning is described. Afterward, the neuronal circuits involving the dynamics of the lateral intraparietal area (LIP) and the superior colliculus (SC) are considered. They provide the theoretical bases for the development and implementation of  the models of DM that are set up as a two-fold circuitry of valuation and choice, by which the accumulation and the processing of information (evidence) take place. A mathematical reasonin...
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Diagnosing Singular Fits in Linear Mixed Models: Practical Examples, Code, and Alternatives

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  Introduction This post is the practical follow‑up to When Linear Mixed Models Don’t Converge , where I discussed the theoretical reasons why convergence issues often arise in linear mixed models. Here, we move from theory to practice: simulated examples, diagnostic tools, interpretation of singular fits, and practical modeling alternatives. If you haven’t read the first part yet, you may want to start there for the conceptual foundations. Figure. Conceptual map showing how model complexity and available information interact Legend of Figure 1. Green = identifiable models; yellow = risk of singularity; red = non‑identifiable models. A Concrete Example Using R: When a Random-Slope Model Becomes Singular Let’s simulate a simple dataset with only 8 groups and a very small random‑slope variance. This is a classic scenario where the model converges numerically but is statistically non‑identifiable. ```r set.seed(123) n_groups <- 8 n_per_group <- 20 group <- factor(rep(1:n_gro...
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Understanding Anaerobic Threshold (VT2) and VO2 Max in Endurance Training

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  Introduction: The Science Behind Ventilatory Thresholds VT1 vs. VT2: Defining Energy Transitions p (Aerobic Threshold) : The transition from lipid metawwwpp (fat-burning) to a mixed metabolism, where both carbohydrates and fats fuel muscle activity. V0T2 (Anaerobic Threshold) : The point where the  shifts to a predominantly carbohydrate-based wmetabolism , leading to rapid lactate waccumpulation and increased reliance on anaerobic energy pathways. www w In p VT1 and VT2 Wwww ewAgep & Sex Younger athletes generally exhibit higher VT2 values , while aging naturally reduces aerobicw capacity. Men tend to have a higher VO2 Max due to greater lung capacity and muscle mass, but women can achieve similar endurance levels through optimized training. Body Composition & Health Status High body fat percentage may reduce VT2 efficiency, as excess weight increases ...
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Unit Root Testing in Practice: A Tutorial on ADF–KPSS, Specification, and Diagnostics

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  Stationary Signals   A stationary signal is defined by the stability of its statistical properties over time. Specifically, this implies that the signal’s mean, variance, and autocorrelation function remain consistent, regardless of the time in which the signal is evaluated. There are two main types of stationarity: ·        Strict-sense stationarity refers to the property of a signal in which all statistical characteristics remain unchanged over time. This encompasses all moments of the distribution, indicating that the entire probability distribution remains constant throughout the observed period. ·        Wide-sense stationarity requires that the first two moments, specifically the mean and variance, remain constant over time. The autocorrelation function is dependent only on the time difference between two points, not on the exact time points themselves.   Importance in Signal...
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