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

Positive Occipital Sharp Transients of Sleep compared to Cone waves

Image
Dr. Rishabh Pathak

Positive Occipital Sharp Transients of Sleep (POSTS) and cone waves are both EEG patterns observed during sleep, but they have distinct characteristics, clinical significance, and implications. 

Positive Occipital Sharp Transients of Sleep (POSTS)

1.      Definition:

§  POSTS are sharp waveforms that occur predominantly in the occipital region during sleep, particularly in non-rapid eye movement (NREM) sleep.

2.     Waveform Characteristics:

§  They typically exhibit a triangular shape and can be monophasic or diphasic. The positive peak is prominent, followed by a negative potential of lower amplitude.

3.     Location:

§  Recorded primarily from the occipital leads (O1 and O2) of the EEG. They may also show phase reversals at these electrodes.

4.    Duration and Frequency:

§  Each transient lasts about 80 to 200 milliseconds and can occur as individual events or in trains of up to six per second. The trains usually last about 1 to 2 seconds.

5.     Clinical Significance:

§  Generally considered a normal variant in healthy individuals, especially in children and adolescents. They are not associated with any pathological conditions and are common in the EEGs of healthy adults.

6.    Age-Related Variability:

§  More prevalent in younger populations and become less common with age. Rarely observed in individuals over 70 years old.

Cone Waves

7.     Definition:

§  Cone waves are a type of EEG pattern that can be observed during sleep, characterized by a specific morphology that resembles a cone or a pointed shape.

8.    Waveform Characteristics:

§  Cone waves typically have a more pronounced peak and a rapid return to the baseline, creating a sharp, pointed appearance. They may not have the same triangular shape as POSTS.

9.    Location:

§  Cone waves can be recorded from various regions of the scalp, not limited to the occipital area. Their distribution may vary depending on the underlying condition or the specific context in which they are observed.

10.                        Duration and Frequency:

§  The duration and frequency of cone waves can vary, and they may not follow the same patterns of occurrence as POSTS. They can appear in bursts or as isolated events.

11.  Clinical Significance:

§  Cone waves may be associated with specific neurological conditions or sleep disorders, and their presence can indicate underlying pathology. Unlike POSTS, they may not be considered a normal variant and could warrant further investigation.

12. Age-Related Variability:

§  The occurrence of cone waves may not have the same age-related patterns as POSTS, and their clinical significance can vary widely based on the context in which they are observed.

Summary

In summary, while both Positive Occipital Sharp Transients of Sleep and cone waves are EEG patterns observed during sleep, they differ significantly in their characteristics, clinical implications, and associations with neurological conditions. POSTS are generally benign and common in healthy individuals, while cone waves may indicate underlying pathology and require careful interpretation in the context of the patient's clinical picture.

 

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

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

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