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

Epigenetics and Histone Deacetylases in Neurodegenerative Disease, Aging, and CNS Repair

Image
Dr. Rishabh Pathak

Epigenetic modifications, including histone acetylation, play a critical role in gene expression regulation, cellular differentiation, and various physiological processes in the central nervous system (CNS). Histone deacetylases (HDACs) are enzymes that modulate histone acetylation levels, thereby influencing chromatin structure and gene transcription. Here is an overview of the involvement of epigenetics and HDACs in neurodegenerative diseases, aging, and CNS repair:


1.      Epigenetic Regulation in Neurodegenerative Diseases:

o    Alzheimer's Disease (AD):

§Epigenetic alterations, including changes in histone acetylation patterns, have been implicated in AD pathogenesis.

§Dysregulation of HDAC activity can lead to aberrant gene expression associated with AD pathology, such as amyloid beta accumulation and tau hyperphosphorylation.

o    Parkinson's Disease (PD):

§Epigenetic modifications, including histone acetylation changes, have been linked to PD pathophysiology.

§HDAC inhibitors have shown neuroprotective effects in preclinical models of PD by modulating gene expression and promoting neuronal survival.

o    Huntington's Disease (HD):

§  Altered histone acetylation levels and HDAC dysregulation have been observed in HD, contributing to transcriptional dysregulation and neuronal dysfunction.

§ Targeting HDACs with specific inhibitors has shown therapeutic potential in ameliorating HD-related phenotypes in experimental models.

2.     Epigenetic Changes in Aging:

o    Aging-Related Epigenetic Modifications:

§Aging is associated with global changes in epigenetic marks, including histone modifications, that impact gene expression patterns and cellular functions.

§Dysregulation of HDACs and histone acetylation dynamics during aging can contribute to age-related cognitive decline and neurodegenerative processes.

o    Role of HDACs in Aging:

§HDACs play a role in regulating longevity pathways, cellular senescence, and age-related gene expression changes in the CNS.

§Modulating HDAC activity through pharmacological interventions or genetic manipulation has been explored as a potential strategy to counteract age-related epigenetic alterations.

3.     Epigenetic Regulation in CNS Repair:

o    Neuroregeneration and Plasticity:

§Epigenetic mechanisms, including histone acetylation, are involved in regulating neurogenesis, synaptic plasticity, and axonal regeneration in the CNS.

§ HDAC inhibitors have been investigated for their potential to enhance CNS repair processes by promoting neuronal growth, synaptic connectivity, and functional recovery following injury or neurodegenerative insults.

Understanding the role of epigenetics and HDACs in neurodegenerative diseases, aging, and CNS repair provides insights into the molecular mechanisms underlying these processes and identifies potential therapeutic targets for intervention. Further research on the specific epigenetic modifications, HDAC isoforms, and regulatory pathways involved in these contexts may lead to the development of novel epigenetic-based therapies for neurological disorders and age-related CNS conditions.

 

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

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

Predicting Probabilities

Dr. Rishabh Pathak
1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...
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

Ensembles of Decision Trees

Dr. Rishabh Pathak
1. What are Ensembles? Ensemble methods combine multiple machine learning models to create more powerful and robust models. By aggregating the predictions of many models, ensembles typically achieve better generalization performance than any single model. In the context of decision trees, ensembles combine multiple trees to overcome limitations of single trees such as overfitting and instability. 2. Why Ensemble Decision Trees? Single decision trees: Are easy to interpret but tend to overfit training data, leading to poor generalization,. Can be unstable because small variations in data can change the structure of the tree significantly. Ensemble methods exploit the idea that many weak learners (trees that individually overfit or only capture partial patterns) can be combined to form a strong learner by reducing variance and sometimes bias. 3. Two Main Types of Tree Ensembles (a) Random Forests Random forests are ensembles con...
Post a Comment
Read more