Skip to main content

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...
Post a Comment

Muscles Artifacts compared to Beta Activity

Image
Dr. Rishabh Pathak

Muscle artifacts and beta activity in EEG recordings can sometimes exhibit similar characteristics, particularly in terms of frequency and location. 

1.     Frequency:

o    Muscle Artifacts: Muscle artifacts often manifest as high-frequency, fast activity on EEG recordings, resembling beta activity in terms of frequency (>25 Hz). However, muscle artifacts typically have a sharper contour and less rhythmicity compared to beta activity.

o Beta Activity: Beta activity in EEG is characterized by rhythmic oscillations in the beta frequency range (typically 13-30 Hz). Beta activity tends to have a more regular and rhythmic pattern compared to muscle artifacts.

2.   Waveform:

o    Muscle Artifacts: Muscle artifacts may have a spike-like or sharp waveform due to the rapid muscle contractions generating the artifact. The individual motor unit potentials involved in muscle contractions contribute to the waveform characteristics of muscle artifacts.

o Beta Activity: Beta activity typically exhibits a smoother and more sinusoidal waveform compared to the sharp spikes often seen in muscle artifacts.

3.   Duration:

o Muscle Artifacts: Muscle artifacts, particularly those induced by muscle contractions, may have shorter durations due to the transient nature of muscle activity. The onset and offset of muscle artifacts are often abrupt.

o Beta Activity: Beta activity can be sustained over longer periods, reflecting ongoing cortical processes associated with motor planning, movement, or cognitive tasks.

4.   Location:

o  Muscle Artifacts: Muscle artifacts are commonly observed near electrodes overlaying muscle groups generating the artifact, such as facial muscles or tongue muscles. The location of muscle artifacts can provide clues to their origin.

o  Beta Activity: Beta activity is often distributed over central and frontal regions of the brain, reflecting motor and cognitive processing areas. Co-localization of muscle artifacts with regions of maximum beta activity can occur, complicating differentiation.

5.    Inter-Interval Variation:

o Muscle Artifacts: In muscle artifacts, there may be significant variation in the interval between individual potentials, especially when these intervals become very brief, leading to the merging of potentials. This rapid activity beyond the beta frequency range is indicative of muscle artifact.

o Beta Activity: Beta activity typically exhibits more consistent inter-interval durations, contributing to its rhythmic and periodic nature within the beta frequency range.

Understanding these distinctions between muscle artifacts and beta activity is essential for accurate EEG interpretation and artifact identification. Recognizing the subtle differences in frequency, waveform, duration, and spatial distribution can help differentiate between genuine brain activity and artifact-induced signals in EEG recordings.

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....
Post a Comment
Read more

EEG Amplification

Dr. Rishabh Pathak
EEG amplification, also known as gain or sensitivity, plays a crucial role in EEG recordings by determining the magnitude of electrical signals detected by the electrodes placed on the scalp. Here is a detailed explanation of EEG amplification: 1. Amplification Settings : EEG machines allow for adjustment of the amplification settings, typically measured in microvolts per millimeter (μV/mm). Common sensitivity settings range from 5 to 10 μV/mm, but a wider range of settings may be used depending on the specific requirements of the EEG recording. 2. High-Amplitude Activity : When high-amplitude signals are present in the EEG, such as during epileptiform discharges or artifacts, it may be necessary to compress the vertical display to visualize the full range of each channel within the available space. This compression helps prevent saturation of the signal and ensures that all amplitude levels are visible. 3. Vertical Compression : Increasing the sensitivity value (e.g., from 10 μV/mm to...
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...
Post a Comment
Read more

Different Methods for recoding the Brain Signals of the Brain?

Dr. Rishabh Pathak
The various methods for recording brain signals in detail, focusing on both non-invasive and invasive techniques.  1. Electroencephalography (EEG) Type : Non-invasive Description : EEG involves placing electrodes on the scalp to capture electrical activity generated by neurons. It records voltage fluctuations resulting from ionic current flows within the neurons of the brain. This method provides high temporal resolution (millisecond scale), allowing for the monitoring of rapid changes in brain activity. Advantages : Relatively low cost and easy to set up. Portable, making it suitable for various applications, including clinical and research settings. Disadvantages : Lacks spatial resolution; it cannot precisely locate where the brain activity originates, often leading to ambiguous results. Signals may be contaminated by artifacts like muscle activity and electrical noise. Developments : ...
Post a Comment
Read more

Computational Model

Dr. Rishabh Pathak
A computational model in the context of brain development refers to a mathematical and numerical representation of the processes involved in the growth and morphogenesis of the brain. Here are the key aspects of a computational model in the study of brain development: 1.    Numerical Simulation : A computational model allows researchers to simulate and analyze the complex processes of brain development using numerical methods. By translating biological principles and mechanical behaviors into mathematical equations, researchers can simulate the growth and deformation of brain structures over time. 2.    Finite Element Analysis : Computational models often utilize finite element analysis, a numerical technique for solving partial differential equations, to simulate the mechanical behavior of brain tissue during growth. This method enables researchers to predict how the brain's structure changes in response to growth-induced stresses and strains. 3.    Parame...
Post a Comment
Read more