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

Epigenetics and Histone Deacetylases in Neurodegenerative Disease, Aging, and CNS Repair

Image
Dr. Rishabh Pathak

Epigenetic modifications, including histone acetylation, play a critical role in gene expression regulation, cellular differentiation, and various physiological processes in the central nervous system (CNS). Histone deacetylases (HDACs) are enzymes that modulate histone acetylation levels, thereby influencing chromatin structure and gene transcription. Here is an overview of the involvement of epigenetics and HDACs in neurodegenerative diseases, aging, and CNS repair:


1.      Epigenetic Regulation in Neurodegenerative Diseases:

o    Alzheimer's Disease (AD):

§Epigenetic alterations, including changes in histone acetylation patterns, have been implicated in AD pathogenesis.

§Dysregulation of HDAC activity can lead to aberrant gene expression associated with AD pathology, such as amyloid beta accumulation and tau hyperphosphorylation.

o    Parkinson's Disease (PD):

§Epigenetic modifications, including histone acetylation changes, have been linked to PD pathophysiology.

§HDAC inhibitors have shown neuroprotective effects in preclinical models of PD by modulating gene expression and promoting neuronal survival.

o    Huntington's Disease (HD):

§  Altered histone acetylation levels and HDAC dysregulation have been observed in HD, contributing to transcriptional dysregulation and neuronal dysfunction.

§ Targeting HDACs with specific inhibitors has shown therapeutic potential in ameliorating HD-related phenotypes in experimental models.

2.     Epigenetic Changes in Aging:

o    Aging-Related Epigenetic Modifications:

§Aging is associated with global changes in epigenetic marks, including histone modifications, that impact gene expression patterns and cellular functions.

§Dysregulation of HDACs and histone acetylation dynamics during aging can contribute to age-related cognitive decline and neurodegenerative processes.

o    Role of HDACs in Aging:

§HDACs play a role in regulating longevity pathways, cellular senescence, and age-related gene expression changes in the CNS.

§Modulating HDAC activity through pharmacological interventions or genetic manipulation has been explored as a potential strategy to counteract age-related epigenetic alterations.

3.     Epigenetic Regulation in CNS Repair:

o    Neuroregeneration and Plasticity:

§Epigenetic mechanisms, including histone acetylation, are involved in regulating neurogenesis, synaptic plasticity, and axonal regeneration in the CNS.

§ HDAC inhibitors have been investigated for their potential to enhance CNS repair processes by promoting neuronal growth, synaptic connectivity, and functional recovery following injury or neurodegenerative insults.

Understanding the role of epigenetics and HDACs in neurodegenerative diseases, aging, and CNS repair provides insights into the molecular mechanisms underlying these processes and identifies potential therapeutic targets for intervention. Further research on the specific epigenetic modifications, HDAC isoforms, and regulatory pathways involved in these contexts may lead to the development of novel epigenetic-based therapies for neurological disorders and age-related CNS conditions.

 

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

Mesencephalic Locomotor Region (MLR)

Dr. Rishabh Pathak
The Mesencephalic Locomotor Region (MLR) is a region in the midbrain that plays a crucial role in the control of locomotion and rhythmic movements. Here is an overview of the MLR and its significance in neuroscience research and motor control: 1.       Location : o The MLR is located in the mesencephalon, specifically in the midbrain tegmentum, near the aqueduct of Sylvius. o   It encompasses a group of neurons that are involved in coordinating and modulating locomotor activity. 2.      Function : o   Control of Locomotion : The MLR is considered a key center for initiating and regulating locomotor movements, including walking, running, and other rhythmic activities. o Rhythmic Movements : Neurons in the MLR are involved in generating and coordinating rhythmic patterns of muscle activity essential for locomotion. o Integration of Sensory Information : The MLR receives inputs from various sensory modalities and higher brain regions t...
Post a Comment
Read more

Seizures

Dr. Rishabh Pathak
Seizures are episodes of abnormal electrical activity in the brain that can lead to a wide range of symptoms, from subtle changes in awareness to convulsions and loss of consciousness. Understanding seizures and their manifestations is crucial for accurate diagnosis and management. Here is a detailed overview of seizures: 1.       Definition : o A seizure is a transient occurrence of signs and/or symptoms due to abnormal, excessive, or synchronous neuronal activity in the brain. o Seizures can present in various forms, including focal (partial) seizures that originate in a specific area of the brain and generalized seizures that involve both hemispheres of the brain simultaneously. 2.      Classification : o Seizures are classified into different types based on their clinical presentation and EEG findings. Common seizure types include focal seizures, generalized seizures, and seizures of unknown onset. o The classification of seizures is esse...
Post a Comment
Read more

Mu Rhythms compared to Ciganek Rhythms

Dr. Rishabh Pathak
The Mu rhythm and Cigánek rhythm are two distinct EEG patterns with unique characteristics that can be compared based on various features.  1.      Location : o     Mu Rhythm : § The Mu rhythm is maximal at the C3 or C4 electrode, with occasional involvement of the Cz electrode. § It is predominantly observed in the central and precentral regions of the brain. o     Cigánek Rhythm : § The Cigánek rhythm is typically located in the central parasagittal region of the brain. § It is more symmetrically distributed compared to the Mu rhythm. 2.    Frequency : o     Mu Rhythm : §   The Mu rhythm typically exhibits a frequency similar to the alpha rhythm, around 10 Hz. §   Frequencies within the range of 7 to 11 Hz are considered normal for the Mu rhythm. o     Cigánek Rhythm : §   The Cigánek rhythm is slower than the Mu rhythm and is typically outside the alpha frequency range. 3. ...
Post a Comment
Read more