Skip to main content

Uncertainty in Multiclass Classification

1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...

Synaptic Deficits In Psychiatric Disorders

Synaptic deficits play a significant role in the pathophysiology of various psychiatric disorders, contributing to the cognitive, emotional, and behavioral symptoms observed in these conditions. Here is an overview of synaptic deficits in key psychiatric disorders:


1.      Schizophrenia:

o  Synaptic Hypoconnectivity: Schizophrenia is associated with deficits in synaptic connectivity, including reduced synaptic density, altered dendritic spine morphology, and impaired synaptic plasticity in brain regions like the prefrontal cortex and hippocampus.

o Glutamatergic Dysfunction: Dysregulation of glutamatergic neurotransmission, particularly N-methyl-D-aspartate (NMDA) receptor hypofunction, contributes to synaptic deficits and disrupted neural circuitry in schizophrenia.

o  Synaptic Pruning Abnormalities: Aberrant synaptic pruning processes during neurodevelopment lead to excessive synaptic elimination, affecting neuronal connectivity and cognitive functions in individuals with schizophrenia.

2.     Depression:

o    Synaptic Atrophy: Depression is characterized by synaptic atrophy, reduced synaptic density, and impaired synaptic plasticity in regions such as the prefrontal cortex and hippocampus, impacting mood regulation and cognitive processing.

o    Neurotransmitter Imbalance: Dysregulation of monoaminergic neurotransmitters, such as serotonin and dopamine, can lead to synaptic deficits and altered synaptic transmission in depression.

oStress-Induced Changes: Chronic stress and elevated glucocorticoid levels associated with depression can disrupt synaptic structure and function, contributing to neuronal atrophy and synaptic loss.

3.     Bipolar Disorder:

o Synaptic Dysfunction: Bipolar disorder is characterized by synaptic dysfunction, including alterations in synaptic plasticity mechanisms, neurotransmitter release, and dendritic spine morphology in brain regions like the amygdala and prefrontal cortex.

o    Excitatory/Inhibitory Imbalance: Imbalance between excitatory and inhibitory synaptic transmission, involving disruptions in glutamatergic and gamma-aminobutyric acid (GABA)ergic signaling, is implicated in the pathophysiology of bipolar disorder.

o Circadian Rhythm Disruption: Dysregulation of circadian rhythms and clock genes can impact synaptic function and neuronal connectivity in individuals with bipolar disorder.

4.    Alzheimer's Disease:

o    Synaptic Loss: Alzheimer's disease is characterized by significant synaptic loss, particularly in regions crucial for memory and cognition, such as the hippocampus and neocortex.

o Amyloid and Tau Pathology: Accumulation of amyloid-beta plaques and tau tangles disrupt synaptic function, leading to synaptic degeneration and impaired neuronal communication in Alzheimer's disease.

o    Synaptic Plasticity Impairment: Disruption of synaptic plasticity mechanisms, including long-term potentiation (LTP) and long-term depression (LTD), contributes to cognitive decline and memory deficits in Alzheimer's disease.

Understanding the synaptic deficits in psychiatric disorders provides valuable insights into the underlying neurobiology of these conditions and offers potential targets for novel therapeutic interventions aimed at restoring synaptic function, improving neural connectivity, and alleviating symptoms associated with synaptic dysfunction.

 

Comments

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....

Linear Models

1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...

Predicting Probabilities

1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...

Uncertainty in Multiclass Classification

1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...

LPFC Functions

The lateral prefrontal cortex (LPFC) plays a crucial role in various cognitive functions, particularly those related to executive control, working memory, decision-making, and goal-directed behavior. Here are key functions associated with the lateral prefrontal cortex: 1.      Executive Functions : o     The LPFC is central to executive functions, which encompass higher-order cognitive processes involved in goal setting, planning, problem-solving, cognitive flexibility, and inhibitory control. o     It is responsible for coordinating and regulating other brain regions to support complex cognitive tasks, such as task switching, attentional control, and response inhibition, essential for adaptive behavior in changing environments. 2.      Working Memory : o     The LPFC is critical for working memory processes, which involve the temporary storage and manipulation of information to guide behavior and decis...