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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 ...
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What is Brain Network?

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Dr. Rishabh Pathak

A brain network refers to the interconnected system of neural pathways and regions in the brain that work together to support various cognitive functions and behaviors. 

1. Definition:

   - A brain network is a complex web of interconnected brain regions that communicate and collaborate to perform specific functions, such as sensory processing, motor control, memory, emotion regulation, and higher-order cognitive processes.

   - These networks consist of both structural connections (anatomical pathways) and functional connections (patterns of neural activity) that enable information processing and integration across different regions of the brain.

 

2. Functional Brain Networks:

   - Functional brain networks are identified using techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to map patterns of synchronized neural activity across different brain regions.

   - Resting-state networks (RSNs) are a key type of functional brain network that exhibit correlated activity even in the absence of a specific task, providing insights into the intrinsic organization of the brain's functional architecture.

 

3. Resting-State Networks (RSNs):

   - RSNs are distinct patterns of functional connectivity that are consistently observed during rest or passive task states, reflecting the intrinsic organization of the brain's functional architecture.

   - Common RSNs include the Default Mode Network (DMN), Frontoparietal Network (FPN), Salience Network (SAN), Limbic Network (LIM), Dorsal Attention Network (DAN), Somatomotor Network (SMN), and Visual Network (VIS).

 

4. Structural Brain Networks:

   - Structural brain networks represent the anatomical connections between different brain regions, which can be mapped using techniques like diffusion tensor imaging (DTI) to trace white matter pathways.

   - These structural connections provide the physical substrate for functional interactions within and between brain networks, supporting efficient information transmission and neural communication.

 

5. Network Dynamics:

   - Brain networks exhibit dynamic interactions and reconfigurations in response to various stimuli, tasks, and internal states, allowing for flexible and adaptive information processing.

   - Changes in network dynamics can reflect alterations in cognitive states, emotional experiences, and pathological conditions, providing valuable insights into brain function and dysfunction.

 

In summary, a brain network represents the intricate system of interconnected neural pathways and regions that collaborate to support diverse cognitive functions and behaviors. By studying the organization and dynamics of brain networks using advanced neuroimaging techniques, researchers can gain a deeper understanding of brain function, dysfunction, and the underlying mechanisms of neurological and psychiatric disorders.

 

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