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...

Molecular Properties And Transport Mechanism Of Vesicular Nucleotide Transporter (VNUT)

The Vesicular Nucleotide Transporter (VNUT), also known as SLC17A9, is a transmembrane protein responsible for packaging nucleotides, particularly ATP, into synaptic vesicles for release as neurotransmitters. Here is an overview of the molecular properties and transport mechanism of VNUT:


1.      Molecular Properties:

o    Gene and Protein Structure: The VNUT gene, SLC17A9, encodes the VNUT protein, a member of the SLC17 transporter family. VNUT is a transmembrane protein with 12 transmembrane domains and cytoplasmic N- and C-termini.

o    Subcellular Localization: VNUT is primarily localized to synaptic vesicles in neurons and secretory vesicles in other cell types, where it facilitates the packaging of nucleotides for vesicular release.

2.     Transport Mechanism:

o Substrate Specificity: VNUT is selective for nucleotides, with a preference for ATP as the primary substrate for vesicular packaging. It can also transport other nucleotides like ADP and UTP.

oProton Coupling: VNUT operates through a proton-coupled transport mechanism, where the uptake of nucleotides into vesicles is coupled to the electrochemical gradient of protons across the vesicular membrane.

o Vesicular Acidification: The acidic pH inside synaptic vesicles created by the vesicular H+-ATPase is essential for the transport activity of VNUT, as it drives the nucleotide uptake process.

3.     Regulation:

o pH Sensitivity: VNUT activity is sensitive to changes in vesicular pH, with optimal transport efficiency observed under acidic conditions typical of synaptic vesicles.

o Modulation by Cations: Cations like calcium (Ca2+) and zinc (Zn2+) can modulate VNUT activity, potentially influencing nucleotide loading and synaptic vesicle release.

4.    Physiological Functions:

o Neurotransmission: VNUT plays a crucial role in purinergic neurotransmission by packaging ATP into synaptic vesicles for release as a neurotransmitter or a co-transmitter with classical neurotransmitters like glutamate.

o Synaptic Plasticity: ATP release via VNUT-mediated vesicular exocytosis can modulate synaptic transmission, plasticity, and neuronal excitability, contributing to various physiological processes in the nervous system.

5.     Pathophysiological Implications:

o Neurological Disorders: Dysregulation of VNUT function and purinergic signaling has been implicated in neurological disorders such as chronic pain, epilepsy, and neurodegenerative diseases, highlighting VNUT as a potential therapeutic target.

o  Immune Responses: Extracellular ATP released through VNUT-mediated vesicular exocytosis can also modulate immune responses, inflammation, and the activation of immune cells in the brain and periphery.

Understanding the molecular properties and transport mechanism of VNUT provides insights into the fundamental processes of nucleotide packaging and release in synaptic vesicles, with implications for neurotransmission, synaptic function, and the pathophysiology of neurological and immune-related disorders. Further research on VNUT regulation and its role in health and disease may uncover novel therapeutic strategies targeting purinergic signaling pathways.

 

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....

EEG Amplification

EEG amplification, also known as gain or sensitivity, plays a crucial role in EEG recordings by determining the magnitude of electrical signals detected by the electrodes placed on the scalp. Here is a detailed explanation of EEG amplification: 1. Amplification Settings : EEG machines allow for adjustment of the amplification settings, typically measured in microvolts per millimeter (μV/mm). Common sensitivity settings range from 5 to 10 μV/mm, but a wider range of settings may be used depending on the specific requirements of the EEG recording. 2. High-Amplitude Activity : When high-amplitude signals are present in the EEG, such as during epileptiform discharges or artifacts, it may be necessary to compress the vertical display to visualize the full range of each channel within the available space. This compression helps prevent saturation of the signal and ensures that all amplitude levels are visible. 3. Vertical Compression : Increasing the sensitivity value (e.g., from 10 μV/mm to...

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...

Different Methods for recoding the Brain Signals of the Brain?

The various methods for recording brain signals in detail, focusing on both non-invasive and invasive techniques.  1. Electroencephalography (EEG) Type : Non-invasive Description : EEG involves placing electrodes on the scalp to capture electrical activity generated by neurons. It records voltage fluctuations resulting from ionic current flows within the neurons of the brain. This method provides high temporal resolution (millisecond scale), allowing for the monitoring of rapid changes in brain activity. Advantages : Relatively low cost and easy to set up. Portable, making it suitable for various applications, including clinical and research settings. Disadvantages : Lacks spatial resolution; it cannot precisely locate where the brain activity originates, often leading to ambiguous results. Signals may be contaminated by artifacts like muscle activity and electrical noise. Developments : ...

Uncertainty Estimates from Classifiers

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 ...