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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...

Gyrification begins around Mid-gestation

Gyrification, the process of forming the characteristic folds and grooves on the surface of the cerebral cortex, initiates around mid-gestation during human brain development. This crucial phase typically begins around week 23 of gestation and continues throughout prenatal and postnatal development. The timing of gyrification coincides with the period when the brain undergoes significant growth and structural organization, leading to the formation of gyri and sulci that increase the surface area of the cortex within the confines of the skull.


During mid-gestation, primary sulci start to emerge, marking the onset of gyrification. As the brain continues to develop, secondary and tertiary sulci form, creating a complex pattern of folds that characterize the convoluted surface of the cerebral cortex. The process of gyrification is intricately linked to neuronal connectivity, cortical expansion, and the establishment of functional neural circuits essential for cognitive functions.


The timing of gyrification around mid-gestation is significant as it sets the stage for the structural maturation of the brain and the organization of cortical regions. The folding of the cortex through gyrification allows for increased neuronal density, efficient communication between brain regions, and the specialization of different functional areas. Variations in the timing and extent of gyrification can influence brain structure and function, contributing to individual differences in cognitive abilities and neurological outcomes.


Understanding the timeline and mechanisms of gyrification is essential for unraveling the complexities of brain development and for studying the impact of disruptions in this process on neurodevelopmental disorders and cognitive function. By studying the initiation and progression of gyrification during mid-gestation and beyond, researchers can gain insights into the dynamic interplay of genetic, environmental, and epigenetic factors that shape the intricate folding patterns of the human brain.

 

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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...