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

Direct Waves (DW)

Image
Dr. Rishabh Pathak

It seems there might be a misunderstanding in the term "Direct Waves (DW)" as it is not a standard term in the context of Transcranial Magnetic Stimulation (TMS) or neurophysiology. However, based on the provided excerpts, it appears that "Direct Waves" could be referring to "D-waves" in the context of TMS. Here is an explanation of D-waves in TMS:


1.      D-Waves in Transcranial Magnetic Stimulation (TMS):

o Definition: D-waves are direct motor responses evoked by TMS, specifically reflecting the direct activation of corticospinal neurons in the primary motor cortex (M1). These waves are part of the neurophysiological responses observed during TMS experiments.

o   Generation: When a TMS pulse is applied to the motor cortex, it can directly activate the corticospinal tract, leading to the generation of D-waves. D-waves are typically observed in electromyography (EMG) recordings of muscles innervated by the stimulated cortical area.

o  Characteristics: D-waves are characterized by their short latency and monophasic waveform. They represent the most direct pathway of neural activation in response to TMS, involving the excitation of pyramidal neurons in layer V of the motor cortex.

o Physiological Significance: D-waves provide insights into the excitability and integrity of the corticospinal pathway. Changes in D-wave amplitude or latency can indicate alterations in motor cortex function, corticospinal conductivity, or synaptic transmission efficiency.

2.     Relationship with I-Waves:

o   In addition to D-waves, TMS can also evoke indirect waves (I-waves) that reflect more complex neural activation patterns involving interneuronal circuits within the cortex. I-waves are generated through indirect pathways and contribute to the overall motor response observed during TMS.

o The interplay between D-waves and I-waves provides a comprehensive understanding of how TMS influences neural circuits in the motor cortex and modulates motor output. Different types of waves (e.g., I1-wave, I2-wave) represent distinct neural pathways and mechanisms of cortical activation.

3.     Clinical and Research Applications:

o  D-wave analysis in TMS studies is crucial for assessing motor cortex excitability, mapping corticospinal projections, and investigating motor system function in health and disease.

o  Researchers and clinicians use D-wave measurements to study motor recovery after stroke, evaluate corticospinal integrity in neurological disorders, and optimize TMS protocols for therapeutic interventions targeting motor dysfunction.

In summary, D-waves in TMS represent direct motor responses elicited by cortical stimulation and play a significant role in understanding motor cortex excitability and corticospinal pathway function. By studying D-waves along with other TMS-evoked responses, researchers gain valuable insights into neural activation patterns, motor system connectivity, and the effects of TMS on brain physiology.

 

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

Uncertainty Estimates from Classifiers

Dr. Rishabh Pathak
1. Overview of Uncertainty Estimates Many classifiers do more than just output a predicted class label; they also provide a measure of confidence or uncertainty in their predictions. These uncertainty estimates help understand how sure the model is about its decision , which is crucial in real-world applications where different types of errors have different consequences (e.g., medical diagnosis). 2. Why Uncertainty Matters Predictions are often thresholded to produce class labels, but this process discards the underlying probability or decision value. Knowing how confident a classifier is can: Improve decision-making by allowing deferral in uncertain cases. Aid in calibrating models. Help in evaluating the risk associated with predictions. Example: In medical testing, a false negative (missing a disease) can be worse than a false positive (extra test). 3. Methods to Obtain Uncertainty from Classifiers 3.1 ...
Post a Comment
Read more