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

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...

Cortical Folding is a Mechanical Instability Driven by Differential Growth

Cortical folding is a complex phenomenon in brain development that is driven by differential growth processes. This mechanical instability arises from the differential growth rates between the cortical layers, leading to the formation of the characteristic gyri and sulci on the surface of the cerebral cortex. Here is an overview of how cortical folding is a mechanical instability driven by differential growth:


1.     Differential Growth: The process of cortical folding is fundamentally linked to the concept of differential growth, where different regions of the developing brain expand at varying rates. This uneven growth results in mechanical stresses within the cortical tissue, as certain areas experience more growth than others. The differential growth between the outer cortical layers and the underlying structures, such as the white matter, plays a key role in initiating cortical folding.


2. Physics-Based Approach: A physics-based approach has been increasingly utilized to understand cortical folding as a mechanical instability phenomenon. This perspective considers the mechanical forces generated by differential growth and how they influence the morphological changes in the brain. By modeling the cortical tissue as a multi-layered system undergoing constrained growth, researchers can simulate the patterns of cortical folding observed in the developing brain.


3.     Constrained Differential Growth: The theory of cortical folding as a constrained differential growth process suggests that the early radial expansion of the cortical plate is relatively uniform across its thickness and does not lead to folding. However, the later tangential expansion, particularly in the superficial cortical layers, is constrained by the inner layers and the underlying structures, promoting the formation of gyri and sulci. This differential growth pattern creates mechanical instabilities that drive the folding of the cortex.


4.     Role of Neuronal Connectivity: While the differential growth is a primary driver of cortical folding, other factors such as neuronal connectivity also play a significant role in shaping the folding patterns. The establishment of neural circuits and synaptic connections influences the distribution of mechanical forces within the cortex, further contributing to the folding process. Changes in synaptic pruning, myelination, and neuronal migration also impact the mechanical properties of the developing brain and influence cortical folding during different stages of development.


5. Implications for Developmental Disorders: Disruptions in the mechanisms underlying cortical folding and differential growth can lead to cortical malformations and neurodevelopmental disorders. Conditions such as lissencephaly, characterized by a smooth brain surface due to disrupted neuronal migration, highlight the importance of proper mechanical interactions in cortical development. Understanding the interplay between differential growth, mechanical forces, and neuronal processes is crucial for elucidating the origins of cortical malformations and associated neurological conditions.


In summary, cortical folding represents a dynamic interplay between differential growth processes and mechanical instabilities in the developing brain. By considering the physical principles that govern cortical morphogenesis, researchers can gain insights into the mechanisms driving the formation of gyri and sulci, as well as the implications of disrupted cortical folding for brain structure and function.

 

Comments

Popular posts from this blog

Relation of Model Complexity to Dataset Size

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....

Linear Models

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...

Predicting Probabilities

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 ...

Uncertainty in Multiclass Classification

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 ...

Conducting a Qualitative Analysis

Conducting a qualitative analysis in biomechanics involves a systematic process of collecting, analyzing, and interpreting non-numerical data to gain insights into human movement patterns, behaviors, and interactions. Here are the key steps involved in conducting a qualitative analysis in biomechanics: 1.     Data Collection : o     Use appropriate data collection methods such as video recordings, observational notes, interviews, or focus groups to capture qualitative information about human movement. o     Ensure that data collection is conducted in a systematic and consistent manner to gather rich and detailed insights. 2.     Data Organization : o     Organize the collected qualitative data systematically, such as transcribing interviews, categorizing observational notes, or indexing video recordings for easy reference during analysis. o     Use qualitative data management tools or software to f...