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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

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...

Human postnatal Neuroanatomical development

Human postnatal neuroanatomical development refers to the process of structural growth and maturation of the human brain after birth, continuing through infancy, childhood, and adolescence. This period is characterized by significant changes in the size, shape, and connectivity of brain structures, which play a crucial role in shaping cognitive, motor, and perceptual abilities. Here are key points related to human postnatal neuroanatomical development :


1.  Brain Growth: From birth to teenage years, there is a fourfold increase in the volume of the human brain. This growth is not uniform, with variations in growth rates between different brain regions, such as subcortical and cortical areas.


2.  Neuronal Migration: By the time of birth, most neurons have migrated to their appropriate locations within the cortex, hippocampus, and other brain regions. However, some neurogenesis continues into adulthood, particularly in the hippocampus.


3.     Synaptogenesis: Synapse formation, the connections between neurons, undergoes rapid changes during postnatal development. Different brain regions exhibit varying rates of synapse formation, with peak densities reached at different ages.


4. Cortical Development: The cerebral cortex, responsible for higher cognitive functions, undergoes significant changes during postnatal development. The maturation of cortical regions is essential for the emergence of complex cognitive abilities.


5.     Connectivity: The development of inter- and intraregional connectivity within the brain is a critical aspect of postnatal neuroanatomical development. The establishment of neural connections influences information processing and functional specialization.


6.     Plasticity: The postnatal brain exhibits a high degree of plasticity, allowing it to adapt and reorganize in response to experiences and environmental stimuli. This plasticity plays a crucial role in shaping brain development and functional outcomes.


7.     Implications: Understanding human postnatal neuroanatomical development has implications for clinical, educational, and social policies. It can inform interventions for neurodevelopmental disorders, educational practices, and policies aimed at supporting healthy brain development in children.


In summary, human postnatal neuroanatomical development is a dynamic and complex process that involves structural changes in the brain, including neuronal migration, synaptogenesis, cortical maturation, and connectivity development. This period of growth and maturation lays the foundation for cognitive, motor, and perceptual abilities, highlighting the importance of studying brain development in understanding human behavior and function.

 

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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

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...