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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 Connectome?

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Dr. Rishabh Pathak

A connectome is a comprehensive map of neural connections in the brain, representing the intricate network of structural and functional pathways that facilitate communication between different brain regions.

 

1. Definition:

   - A connectome is a detailed representation of the wiring diagram of the brain, illustrating the complex network of axonal projections, synaptic connections, and communication pathways between neurons and brain regions.

   - The connectome encompasses both the structural connectivity, which refers to the physical links between neurons and brain areas, and the functional connectivity, which reflects the patterns of neural activity and information flow within the brain.

 

2. Structural Connectome:

   - The structural connectome provides a map of the anatomical connections in the brain, showing how neurons are physically linked through axonal projections, white matter pathways, and synaptic contacts.

   - Techniques like diffusion MRI, tractography, and electron microscopy are used to trace and visualize the structural pathways of neural communication, revealing the organization of neural circuits and brain networks.

 

3. Functional Connectome:

   - The functional connectome captures the dynamic patterns of neural activity and functional interactions between brain regions during different cognitive tasks, states of consciousness, or behavioral conditions.

   - Functional imaging methods such as fMRI, EEG, and MEG are employed to measure functional connectivity and map the synchronized activity within functional brain networks, providing insights into brain function and information processing.

 

4. Mapping Techniques:

   - Mapping the connectome involves integrating data from various imaging modalities, neuroanatomical studies, and computational analyses to construct a comprehensive model of brain connectivity.

   - Advances in imaging technology, network analysis, and machine learning have enabled researchers to create detailed connectomes at different spatial scales, from the level of individual synapses to large-scale brain networks.

 

5. Applications:

   - Connectome research has broad implications for understanding brain development, neural plasticity, learning, memory, cognition, and neurological disorders.

   - By studying the connectome, researchers can uncover the organizational principles of the brain, identify biomarkers of brain function and dysfunction, and explore the neural basis of complex behaviors and cognitive processes.

 

In summary, the connectome represents a fundamental framework for studying the structural and functional connectivity of the brain, offering insights into the complex network architecture that underlies brain function, information processing, and the mechanisms of neurological and psychiatric disorders.

 

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