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

RB/E2F pathway regulates neurogenesis by modulating the composition of Neural Precursor population

Image
Dr. Rishabh Pathak

The Retinoblastoma (Rb)/E2F pathway plays a crucial role in regulating neurogenesis by modulating the composition of the neural precursor population. Here are key points regarding how the Rb/E2F pathway influences neurogenesis:


1.      Neural Precursor Cell Fate:

o    Regulation of Cell Cycle Exit: The Rb/E2F pathway controls the transition of neural precursor cells from proliferation to differentiation by promoting cell cycle exit. Activation of the Rb protein leads to the repression of E2F transcription factors, which are essential for driving cell cycle progression. By inhibiting E2F activity, Rb facilitates the exit of neural precursor cells from the cell cycle, allowing them to undergo differentiation.

o    Maintenance of Terminal Differentiation: Proper functioning of the Rb/E2F pathway is essential for maintaining terminal differentiation of neural precursor cells. Disruption of Rb-mediated regulation can result in defects in neuronal maturation and migration, leading to abnormalities in the composition of the neural precursor population.

2.     DLX Transcription Factors:

o    Regulation of DLX Genes: The Rb/E2F pathway modulates the expression of DLX homeodomain genes, particularly Dlx2, which are critical for ventral telencephalic development and the generation of specific interneuron subtypes. Rb interacts with regulatory regions of the Dlx1/Dlx2 locus, including enhancers and promoters, to control DLX gene expression. E2F functional sites act as repressor elements in these regions, influencing the transcriptional activity of DLX genes.

o  Role in Neuronal Differentiation: By directly regulating DLX gene expression, the Rb/E2F pathway contributes to the differentiation and specification of neural precursor cells into distinct neuronal subtypes. Dysregulation of DLX genes due to Rb pathway dysfunction can impact the diversity and maturation of the neural precursor population.

3.     Cell Cycle Dynamics:

o Coordination of Proliferation and Differentiation: The Rb/E2F pathway coordinates the balance between proliferation and differentiation in neural precursor cells. By controlling the expression of key transcription factors and cell cycle regulators, Rb ensures that neural precursors appropriately exit the cell cycle and commit to neuronal differentiation pathways.

o Temporal Progression of Neurodevelopment: Through its interactions with downstream targets such as Znf238, the Rb/E2F pathway orchestrates the temporal progression of neurodevelopment. Negative feedback loops mediated by Rb/E2F-regulated factors help consolidate cell cycle exit and regulate the migration and differentiation of newborn cortical neurons.

In summary, the Rb/E2F pathway plays a pivotal role in regulating neurogenesis by modulating the composition of the neural precursor population. By controlling cell cycle exit, maintaining terminal differentiation, regulating DLX transcription factors, and coordinating proliferation and differentiation processes, the Rb/E2F pathway influences the generation and maturation of neurons during brain development. Understanding the mechanisms by which the Rb/E2F pathway shapes the neural precursor population provides insights into neurodevelopmental processes and potential therapeutic targets for neurodevelopmental disorders.

 

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

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

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

The Decision Functions

Dr. Rishabh Pathak
1. What is the Decision Function? The decision_function method is provided by many classifiers in scikit-learn. It returns a continuous score for each sample, representing the classifier’s confidence or margin. This score reflects how strongly the model favors one class over another in binary classification, or a more complex set of scores in multiclass classification. 2. Shape and Output of decision_function For binary classification , the output shape is (n_samples,). Each value is a floating-point number indicating the degree to which the sample belongs to the positive class. Positive values indicate a preference for the positive class; negative values indicate a preference for the negative class. For multiclass classification , the output is usually a 2D array of shape (n_samples, n_classes), providing scores for each class. 3. Interpretation of decision_function Scores The sign of the value (positive or...
Post a Comment
Read more