Skip to main content

Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
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 ...
Post a Comment

Functional Brain Network Alterations in Response to Blindness and Sight Restoration

Image
Dr. Rishabh Pathak

 


Functional brain network alterations in response to blindness and sight restoration involve complex changes in neural connectivity, network organization, and information processing. Here are some key points regarding functional brain network alterations in response to blindness and sight restoration based on the provided information:

 1. Effect of Blindness on Functional Connectivity:

   - Blindness, whether congenital, early-onset, or late-onset, can lead to significant alterations in functional connectivity within the brain. Studies have shown that visual deprivation can weaken connectivity within the visual cortex and between the visual cortex and other sensory, motor, and association regions.

   - Resting-state functional connectivity studies have demonstrated decreased connectivity between primary visual areas (V1 and V2) and somatosensory, auditory, motor, and association areas in individuals with blindness, reflecting the impact of visual loss on neural communication and network dynamics.

 2. Whole-Brain Functional Connectivity Analysis:

   - Research has focused on examining whole-brain functional connectivity changes in response to blindness and sight restoration. Studies have utilized ROI-ROI functional connectivity analysis and graph theory measures to investigate how visual deprivation and sight recovery interventions influence neural network connectivity and organization.

   - Functional brain network alterations following blindness may involve changes in connectivity patterns between visual areas, sensory regions, motor cortex, and higher-order association areas. These alterations reflect the brain's adaptive responses to visual deprivation and the reorganization of neural networks to compensate for the loss of vision.

 3. Impact of Sight Restoration on Brain Networks:

   - Sight restoration interventions, such as retinal prostheses or gene therapy, can induce changes in functional brain networks by reintroducing visual input and stimulating visual processing areas. Studies have shown that restoring vision can enhance functional connectivity in the visual cortex and promote adaptive neural responses to the reintroduction of visual stimuli.

   - Functional brain network alterations following sight restoration may include enhanced visual responses, improved connectivity between visual areas, and adaptive learning processes that facilitate the integration of restored visual input into existing neural circuits. These changes reflect the brain's plasticity and capacity to adapt to restored sensory modalities.

 4. Implications for Rehabilitation and Technology Development:

   - Understanding functional brain network alterations in response to blindness and sight restoration is crucial for developing effective rehabilitation strategies and optimizing vision restoration technologies. By elucidating how visual deprivation and sight recovery interventions impact neural connectivity and network dynamics, researchers can enhance the design of interventions aimed at improving visual function and quality of life in individuals with visual impairments.

 

Overall, functional brain network alterations in response to blindness and sight restoration involve dynamic changes in neural connectivity and network organization, reflecting the brain's adaptive responses to visual deprivation and the reintroduction of visual input through sight recovery interventions. Studying these alterations provides valuable insights into the neural mechanisms underlying vision loss and restoration, with implications for clinical rehabilitation and the development of innovative therapies for individuals with visual impairments.

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
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....
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
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...
Post a Comment
Read more

Predicting Probabilities

Dr. Rishabh Pathak
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 ...
Post a Comment
Read more

Kernelized Support Vector Machines

Dr. Rishabh Pathak
1. Introduction to SVMs Support Vector Machines (SVMs) are supervised learning algorithms primarily used for classification (and regression with SVR). They aim to find the optimal separating hyperplane that maximizes the margin between classes for linearly separable data. Basic (linear) SVMs operate in the original feature space, producing linear decision boundaries. 2. Limitations of Linear SVMs Linear SVMs have limited flexibility as their decision boundaries are hyperplanes. Many real-world problems require more complex, non-linear decision boundaries that linear SVM cannot provide. 3. Kernel Trick: Overcoming Non-linearity To allow non-linear decision boundaries, SVMs exploit the kernel trick . The kernel trick implicitly maps input data into a higher-dimensional feature space where linear separation might be possible, without explicitly performing the costly mapping . How the Kernel Trick Works: Instead of computing ...
Post a Comment
Read more

Supervised Learning

Dr. Rishabh Pathak
What is Supervised Learning? ·     Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. ·     Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. ·          Types: ·          Classification: Predicting categorical labels (e.g., classifying iris flowers into species). ·          Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: ·     Generalization: The ability of a model to perform well on previously unseen data, not just the training data. ·         Overfitting and Underfitting: ·    ...
Post a Comment
Read more