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Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

Dr. Rishabh Pathak
This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better. Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based functio...
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Patterns of Change in Gray Matter

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Dr. Rishabh Pathak

Gray matter undergoes dynamic changes throughout development, reflecting the maturation and specialization of neural circuits in the brain. Here are some key patterns of change in gray matter:


1.   Early Growth and Pruning: In early childhood, there is a period of rapid growth in gray matter volume, driven by increases in neuronal cell bodies, dendrites, and synapses. This phase is followed by a process of pruning, where weaker or unused connections are eliminated to optimize neural efficiency.


2.   Regional Variability: Gray matter changes exhibit regional variability, with different brain regions showing distinct patterns of growth and decline over time. For example, cortical regions involved in sensory and motor functions may mature earlier than areas responsible for higher-order cognitive processes.


3.  Inverted-U Shaped Trajectory: Many cortical regions, especially dorsal areas, exhibit an inverted-U shaped trajectory of gray matter development. This pattern involves an initial increase in gray matter volume during infancy and early childhood, followed by a gradual decrease starting in late childhood and continuing into adulthood, eventually reaching a plateau.


4.  Synaptic Density Changes: Changes in gray matter volume are closely related to synaptic density and complexity. Synaptic pruning, which involves the elimination of weaker synapses and the strengthening of important connections, contributes to the observed patterns of gray matter changes across development.


5. Hierarchical Development: Gray matter development follows a hierarchical sequence, with lower-order sensory and motor regions maturing earlier than higher-order association areas. This sequence of development reflects the phylogenetic organization of the brain and the progressive specialization of cortical functions.


6.  Age-Related Declines: While gray matter volume generally increases in childhood and peaks in early adulthood, there is a gradual decline in gray matter volume in later adulthood. Age-related declines in gray matter are associated with factors such as synaptic loss, neuronal atrophy, and changes in cortical thickness.


Understanding the patterns of change in gray matter provides insights into the structural and functional development of the brain across the lifespan. The dynamic nature of gray matter development reflects the ongoing refinement and optimization of neural circuits to support cognitive abilities, sensory processing, and motor functions.

 

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