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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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Functional Brain Network

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Dr. Rishabh Pathak

Functional brain networks refer to the interconnected system of brain regions that exhibit synchronized neural activity and functional connectivity during specific cognitive tasks or at rest. 

1. Definition:

   - Functional brain networks are patterns of coordinated neural activity among different brain regions that work together to support specific cognitive functions, such as attention, memory, language, and emotion regulation [T5].

   - These networks are identified using techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), which measure changes in blood flow or electrical activity to infer functional connections between brain regions.

 

2. Resting-State Networks (RSNs):

   - Resting-state networks (RSNs) are functional brain networks that exhibit synchronized activity even in the absence of a specific task, 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).

 

3. Functional Connectivity:

   - Functional connectivity refers to the statistical correlation or coherence of neural activity between different brain regions, indicating the strength of communication and interaction within a functional brain network.

   - Measures of functional connectivity can reveal how information is processed and integrated across distributed brain regions during cognitive tasks or in resting states.

 

4. Task-Related Networks:

   - Task-related functional brain networks are activated when individuals engage in specific cognitive tasks or sensory-motor activities, reflecting the dynamic coordination of brain regions to support task performance.

   - These networks can be identified by analyzing changes in neural activity patterns or connectivity during task execution, providing insights into the neural mechanisms underlying cognitive processes.

 

5. Network Dynamics:

   - Functional brain networks exhibit dynamic changes in connectivity patterns and network configurations in response to external stimuli, cognitive demands, and internal states.

   - The flexibility and adaptability of brain networks allow for efficient information processing, cognitive flexibility, and the integration of sensory, motor, and cognitive functions.

 

In summary, functional brain networks represent the coordinated activity and connectivity patterns among brain regions that underlie cognitive processes and behaviors. By studying the organization and dynamics of these networks using advanced neuroimaging techniques, researchers can unravel the complex interactions within the brain and gain insights into normal brain function, cognitive disorders, and the effects of interventions on brain connectivity.



 

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