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

The Cytoplasmic FMRP Interacting Protein 1 CYFIP1 Links Fragile X Syndrome to Other Neurodevelopmental and Psychiatric Disorders

Image
Dr. Rishabh Pathak

The Cytoplasmic FMRP Interacting Protein 1 (CYFIP1) has emerged as a critical link between Fragile X Syndrome (FXS) and other neurodevelopmental and psychiatric disorders. Here is an overview of the role of CYFIP1 in connecting FXS to broader neurobiological contexts:


1.      Association with Fragile X Syndrome (FXS):

o    FMRP Interactor: CYFIP1 is a known interactor of Fragile X Mental Retardation Protein (FMRP), the protein encoded by the FMR1 gene. Mutations in the FMR1 gene lead to the absence or dysfunction of FMRP, resulting in FXS, a genetic disorder characterized by intellectual disability and autism spectrum features [T26].

o    Regulation of Protein Synthesis: CYFIP1 plays a crucial role in regulating protein synthesis at synapses by interacting with FMRP and the mRNA translation machinery. Dysregulation of protein synthesis due to CYFIP1-FMRP interactions contributes to synaptic dysfunction and cognitive impairments in individuals with FXS [T27].

2.Implications for Neurodevelopmental and Psychiatric Disorders:

o    Neurodevelopmental Disorders: CYFIP1 has been implicated in a broader spectrum of neurodevelopmental disorders beyond FXS. Dysfunctions in CYFIP1-mediated protein synthesis and synaptic plasticity have been associated with conditions such as autism spectrum disorders, intellectual disabilities, and developmental delay [T28].

o    Psychiatric Disorders: CYFIP1 has also been linked to psychiatric disorders, including schizophrenia and bipolar disorder. Aberrant CYFIP1 expression or function may disrupt neural connectivity, synaptic transmission, and neuronal signaling pathways implicated in the pathogenesis of these psychiatric conditions [T29].

3.     Molecular Mechanisms and Pathophysiology:

o    CYFIP1 Complexes: CYFIP1 is a component of the WAVE regulatory complex (WRC), which regulates actin cytoskeleton dynamics and dendritic spine morphology in neurons. Dysregulation of CYFIP1-WRC interactions can impact synaptic structure, neuronal connectivity, and plasticity, contributing to neurodevelopmental and psychiatric phenotypes [T30].

o    Synaptic Function: CYFIP1 is involved in modulating synaptic function, including neurotransmitter release, receptor trafficking, and dendritic spine formation. Altered CYFIP1 activity can disrupt synaptic homeostasis, impair neural circuitry, and affect cognitive and behavioral functions associated with neurodevelopmental and psychiatric disorders [T31].

4.    Therapeutic Implications:

o    Targeting CYFIP1 Interactions: Strategies aimed at modulating CYFIP1 interactions with FMRP, WRC components, or other synaptic proteins may offer therapeutic opportunities for treating FXS and related neurodevelopmental and psychiatric disorders. By restoring normal protein synthesis and synaptic function, these interventions could potentially alleviate cognitive deficits and behavioral symptoms in affected individuals [T32].

o    Precision Medicine Approaches: Precision medicine approaches that consider individual genetic variations, including CYFIP1-related mutations or dysregulation, could help tailor treatment strategies for patients with FXS and associated neurodevelopmental and psychiatric conditions. Personalized interventions targeting CYFIP1 pathways may enhance treatment efficacy and outcomes in affected individuals [T33].

In conclusion, CYFIP1 serves as a critical molecular link connecting Fragile X Syndrome to a broader spectrum of neurodevelopmental and psychiatric disorders. Understanding the role of CYFIP1 in regulating protein synthesis, synaptic function, and neural connectivity is essential for unraveling the pathophysiological mechanisms underlying these conditions and developing targeted therapeutic interventions to address the shared molecular pathways implicated in FXS and related 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

Prerequisite Knowledge for a Quantitative Analysis

Dr. Rishabh Pathak
To conduct a quantitative analysis in biomechanics, researchers and practitioners require a solid foundation in various key areas. Here are some prerequisite knowledge areas essential for performing quantitative analysis in biomechanics: 1.     Anatomy and Physiology : o     Understanding the structure and function of the human body, including bones, muscles, joints, and organs, is crucial for biomechanical analysis. o     Knowledge of anatomical terminology, muscle actions, joint movements, and physiological processes provides the basis for analyzing human movement. 2.     Physics : o     Knowledge of classical mechanics, including concepts of force, motion, energy, and momentum, is fundamental for understanding the principles underlying biomechanical analysis. o     Understanding Newton's laws of motion, principles of equilibrium, and concepts of work, energy, and power is essential for quantifyi...
1 comment
Read more