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

Brain Development in the Postnatal Period

Image
Dr. Rishabh Pathak

Brain development in the postnatal period involves a series of dynamic processes that continue after birth, contributing to the maturation and refinement of the nervous system. Here are key points regarding brain development in the postnatal period:


1.     Proliferation and Migration of Glial Progenitors:

§  While neuron production and migration are primarily prenatal events, the postnatal period is characterized by the continued proliferation and migration of glial progenitor cells, such as oligodendrocyte progenitor cells.

§  Glial progenitors play essential roles in the development of myelin, the insulation around nerve fibers that enhances signal transmission in the brain, and their proliferation and differentiation contribute to ongoing brain maturation throughout childhood.

2.     Differentiation and Maturation of Glial Cells:

§  The differentiation and maturation of glial cells, including oligodendrocytes and astrocytes, continue postnatally and play critical roles in supporting neuronal function, synaptic transmission, and overall brain health.

§  Glial cells provide structural support, regulate the extracellular environment, modulate synaptic activity, and participate in processes such as myelination, synaptic pruning, and neurotransmitter recycling, influencing neural circuit function.

3.     Late Maturation of Glial Populations:

§  Ongoing research suggests that the late maturation of glial populations has widespread functional implications beyond their traditional support roles, indicating complex interactions between neurons, oligodendrocytes, and astrocytes in shaping neural dynamics.

§  The maturation of glial populations in the postnatal period likely influences neural circuit function, synaptic plasticity, and information processing in the brain, highlighting the importance of glial cells in brain development and function.

4.     Cell Death in Glial Populations:

§  In the postnatal period, regressive events such as cell death also occur in glial populations, particularly in excess oligodendrocytes that undergo apoptosis after differentiating, a process influenced by signals from nearby axons.

§  The elimination of surplus glial cells through apoptosis is essential for matching the number of surviving oligodendrocytes with the local axonal surface area, ensuring proper myelination and functional connectivity in the developing brain.

5.     Continued Brain Growth and Maturation:

§  Postnatally, the brain undergoes significant growth and maturation, with the brain size increasing by four-fold during the preschool period and reaching approximately 90% of adult volume by age 6, reflecting ongoing structural and functional development.

§  The postnatal period is characterized by continued refinement of neural circuits, synaptic connections, and myelination processes, contributing to the maturation of cognitive abilities, motor skills, and sensory processing in children.

In summary, brain development in the postnatal period involves the proliferation, differentiation, and maturation of glial cells, ongoing refinement of neural circuits, and regressive events such as cell death in glial populations. The interactions between neurons and glial cells, along with the processes of myelination and synaptic pruning, contribute to the maturation and functional organization of the developing brain beyond the prenatal period, shaping neural dynamics and supporting cognitive and behavioral development in children.

 

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

Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
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