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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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Frontal–central - Beta Activity

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Dr. Rishabh Pathak

Frontal-central beta activity in EEG recordings refers to a specific pattern of beta waves that are predominantly observed in the frontal and central regions of the brain.

Description:

o Frontal-central beta activity is characterized by increased beta waves present diffusely, with a buildup of greater beta activity specifically in the frontal-central regions.

o This pattern may be accompanied by generalized theta activity, which can be more visible when the beta activity declines.

2.     Frequency Range:

o Frontal-central beta activity typically falls within the beta frequency range, which is defined as 13 Hz or greater in EEG recordings.

o The frequency of frontal-central beta activity tends to be within the narrower range of 20 to 30 Hz, with variations in frequency observed based on age and state of consciousness.

3.     State Dependency:

o  Frontal-central beta activity is considered state-dependent, meaning it is influenced by the individual's level of consciousness and cognitive state.

o It is commonly observed during drowsiness and may continue through stage 2 of non-rapid eye movement (NREM) sleep, appearing as bursts with specific characteristics.

4.    Amplitude and Symmetry:

o Normal frontal-central beta activity is symmetric in its amplitude, with an amplitude asymmetry greater than 35% considered abnormal.

o The amplitude of frontal-central beta activity may reach a maximum of about 60 μV, with rhythmicity that can be out of phase between the two hemispheres.

5.     Development and Migration:

o Frontal-central beta activity typically first develops between the ages of 6 months and 2 years, initially appearing over the central and posterior head regions before gradually migrating anteriorly.

o During childhood, frontal-central beta activity continues to shift anteriorly and becomes frontally predominant by early adulthood, reflecting age-related changes in brain activity patterns.

Understanding the characteristics and significance of frontal-central beta activity in EEG recordings is essential for interpreting brain wave patterns, assessing cognitive states, and monitoring changes in neural activity across different regions of the brain.

 

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