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

What are the direct connection and indirect connection performance of BCI systems over 50 years?

Image
Dr. Rishabh Pathak

The performance of Brain-Computer Interface (BCI) systems has significantly evolved over the past 50 years, distinguishing between direct and indirect connection methods.

Direct Connection Performance:

1.      Definition: Direct connection BCIs involve the real-time measurement of electrical activity directly from the brain, typically using techniques such as:

  • Electroencephalography (EEG): Non-invasive, measuring electrical activity through electrodes on the scalp.
  • Invasive Techniques: Such as implanted electrodes, which provide higher signal fidelity and resolution.

2.     Historical Development:

  • Early Research: The journey began in the 1970s with initial experiments at UCLA aimed at establishing direct communication pathways between the brain and devices. Research in this period focused primarily on animal subjects and theoretical frameworks.
  • Technological Advancements: As technology advanced, particularly in the areas of signal processing and machine learning, the direct connection methods began to yield more accurate and reliable interpretations of brain signals for practical applications like controlling devices.

3.     Current Performance:

  • Complex Command Recognition: Improved algorithms now allow for high accuracy in interpreting user intentions, enabling the control of complex devices (e.g., robotic prosthetics) .
  • Calibration: While intrusive methods such as implanted electrodes offer better signals, they require more extensive calibration and carry risks.

Indirect Connection Performance:

1.      Definition: Indirect connection BCIs acquire brain-related metrics that do not directly measure the electrical activity of neurons. Methods include:

  • Functional Magnetic Resonance Imaging (fMRI): Measures brain activity by detecting changes associated with blood flow.
  • Functional Near-Infrared Spectroscopy (fNIRS): Involves measuring hemodynamic responses associated with neural activity.

2.     Historical Development:

  • Emergence of Non-invasive Techniques: The non-invasive BCI systems became more prevalent in the 1990s, leveraging indirect methods to allow users to interact with computers without the need for invasive procedures 
  • Application Expansion: This evolution led to applications beyond medical, including gaming and consumer products 

3.     Current Performance:

  • User Comfort and Accessibility: Indirect BCIs, such as fNIRS and fMRI, offer a user-friendly environment without the risks associated with invasive methods, making them more widely acceptable for use in various applications.
  • Real-time Analysis: Although providing less temporal resolution than direct methods, advances in imaging technologies have enhanced the real-time analysis capabilities of indirect BCIs for practical tasks.

Summary of Performance:

In summary, the direct connection BCIs have made strides in accuracy and capability through improved electrode technology and sophisticated algorithms, particularly beneficial in medical applications. Indirect connection BCIs, while generally less invasive, have developed to become user-friendly alternatives, particularly suited for research, entertainment, and rehabilitation. Overall, both approaches have expanded significantly over the last fifty years, leading to a diverse array of applications that enhance human-computer interaction, especially for individuals with disabilities.

 


Kawala-Sterniuk, A., Browarska, N., Al-Bakri, A., Pelc, M., Zygarlicki, J., Sidikova, M., Martinek, R., & Gorzelanczyk, E. J. (2021). Summary of over fifty years with brain-computer interfaces—A review. Brain Sciences, 11(43). https://doi.org/10.3390/brainsci11010043

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

EEG Amplification

Dr. Rishabh Pathak
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...
Post a Comment
Read more

Different Methods for recoding the Brain Signals of the Brain?

Dr. Rishabh Pathak
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 : ...
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

Plastic Changes are age dependent

Dr. Rishabh Pathak
Plastic changes in the brain are indeed age-dependent, with different developmental stages and life phases influencing the extent, nature, and outcomes of neural plasticity. Here are some key aspects of the age-dependent nature of plastic changes in the brain: 1.      Developmental Plasticity : The developing brain exhibits heightened plasticity during critical periods of growth and maturation. Early in life, neural circuits undergo significant structural and functional changes in response to sensory inputs, learning experiences, and environmental stimuli, shaping the foundation of cognitive development. 2.      Sensitive Periods : Sensitive periods in development represent windows of heightened plasticity during which the brain is particularly receptive to specific types of experiences. These critical phases play a crucial role in establishing neural connections, refining circuitry, and optimizing brain function for learning and adaptation. 3. ...
Post a Comment
Read more