Skip to main content

Energy Manifold Natural Gradient Descent: From Riemannian Optimization to Modern Neuroscience, NeuroAI and Quantum Physics

Dr. Rishabh Pathak
When Geometry, Energy, Artificial Intelligence and Neuroscience Converge Modern Artificial Intelligence is rapidly moving beyond the idea that learning simply means minimizing an error function. Increasingly, researchers are asking a deeper question: what is the structure of the space in which learning takes place? This question becomes particularly important when the system being modelled is constrained, nonlinear, dynamic, or governed by physical principles. A recent work titled “Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers” , by Zhangyong Liang and Huanhuan Gao, introduces Energy Manifold Natural Gradient Descent (EMNGD) , a mathematical framework that extends energy-based natural-gradient optimization from unconstrained Euclidean parameter spaces to constrained Riemannian parameter manifolds . At its core, the framework proposes a simple but powerful principle: An optimization algorithm should not only determine how to reduce error; it sh...
Post a Comment

Functional Brain Network

Image
Dr. Rishabh Pathak

Functional brain networks refer to the interconnected system of brain regions that exhibit synchronized neural activity and functional connectivity during specific cognitive tasks or at rest. 

1. Definition:

   - Functional brain networks are patterns of coordinated neural activity among different brain regions that work together to support specific cognitive functions, such as attention, memory, language, and emotion regulation [T5].

   - These networks are identified using techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), which measure changes in blood flow or electrical activity to infer functional connections between brain regions.

 

2. Resting-State Networks (RSNs):

   - Resting-state networks (RSNs) are functional brain networks that exhibit synchronized activity even in the absence of a specific task, reflecting the intrinsic organization of the brain's functional architecture.

   - Common RSNs include the Default Mode Network (DMN), Frontoparietal Network (FPN), Salience Network (SAN), Limbic Network (LIM), Dorsal Attention Network (DAN), Somatomotor Network (SMN), and Visual Network (VIS).

 

3. Functional Connectivity:

   - Functional connectivity refers to the statistical correlation or coherence of neural activity between different brain regions, indicating the strength of communication and interaction within a functional brain network.

   - Measures of functional connectivity can reveal how information is processed and integrated across distributed brain regions during cognitive tasks or in resting states.

 

4. Task-Related Networks:

   - Task-related functional brain networks are activated when individuals engage in specific cognitive tasks or sensory-motor activities, reflecting the dynamic coordination of brain regions to support task performance.

   - These networks can be identified by analyzing changes in neural activity patterns or connectivity during task execution, providing insights into the neural mechanisms underlying cognitive processes.

 

5. Network Dynamics:

   - Functional brain networks exhibit dynamic changes in connectivity patterns and network configurations in response to external stimuli, cognitive demands, and internal states.

   - The flexibility and adaptability of brain networks allow for efficient information processing, cognitive flexibility, and the integration of sensory, motor, and cognitive functions.

 

In summary, functional brain networks represent the coordinated activity and connectivity patterns among brain regions that underlie cognitive processes and behaviors. By studying the organization and dynamics of these networks using advanced neuroimaging techniques, researchers can unravel the complex interactions within the brain and gain insights into normal brain function, cognitive disorders, and the effects of interventions on brain connectivity.



 

Categories:

Comments

Post a Comment

Popular posts from this blog

Amphiarthrodial or Cartilaginous Joints

Dr. Rishabh Pathak
Amphiarthrodial joints, also known as cartilaginous joints, are joints where the adjacent bones are connected by cartilage. These joints allow for limited movement and provide both stability and flexibility to the skeletal system. Here is an overview of amphiarthrodial or cartilaginous joints: Amphiarthrodial or Cartilaginous Joints: 1.     Structure : o     Cartilage : §   Amphiarthrodial joints are characterized by the presence of cartilage between the articulating surfaces of the bones. §   The cartilage can be hyaline cartilage or fibrocartilage, depending on the specific joint and its function. o     Lack of Joint Cavity : §   Similar to fibrous joints, cartilaginous joints do not have a synovial cavity, and the bones are held together by the cartilaginous tissue. 2.     Types : o     Synchondroses : §   Synchondroses are cartilaginous joints where the connecting material is hyaline carti...
Post a Comment
Read more

Cell Maturation (Dendrite and Axon Growth)

Dr. Rishabh Pathak
Cell maturation, encompassing dendrite and axon growth, is a crucial stage of brain development where neurons undergo structural changes to establish connections and form functional neural circuits. Here is an overview of cell maturation in the context of dendrite and axon growth: 1.      Dendrite Growth : o     Definition : Dendrites are branched extensions of a neuron that receive signals from other neurons and transmit these signals to the cell body. o     Dendritic Arborization : During maturation, neurons extend and elaborate their dendritic arbors, increasing the surface area available for synaptic connections. o     Synaptic Integration : Dendritic growth is essential for forming synapses with other neurons, allowing for the integration of incoming signals and information processing. o     Activity-Dependent Plasticity : Dendritic growth can be influenced by neural activity and sensory experiences, sh...
Post a Comment
Read more

Distinguishing Features of Electrode Artifacts

Dr. Rishabh Pathak
Electrode artifacts in EEG recordings can present with distinct features that differentiate them from genuine brain activity.  1.      Types of Electrode Artifacts : o Variety : Electrode artifacts encompass several types, including electrode pop, electrode contact, electrode/lead movement, perspiration artifacts, salt bridge artifacts, and movement artifacts. o Characteristics : Each type of electrode artifact exhibits specific waveform patterns and spatial distributions that aid in their identification and differentiation from true EEG signals. 2.    Electrode Pop : o Description : Electrode pop artifacts are characterized by paroxysmal, sharply contoured transients that interrupt the background EEG activity. o Localization : These artifacts typically involve only one electrode and lack a field indicating a gradual decrease in potential amplitude across the scalp. o Waveform : Electrode pop waveforms have a rapid rise and a slower fall compared to in...
Post a Comment
Read more

Unrestricted Sampling

Dr. Rishabh Pathak
Unrestricted sampling, also known as simple random sampling, is a fundamental sampling technique where each element in the population has an equal and independent chance of being selected for the sample. In unrestricted sampling: 1.     Equal Probability of Selection : §   In simple random sampling, every element in the population has an equal probability of being chosen for the sample. This ensures that each unit is selected independently of other units, without any bias towards specific elements. 2.     Random Selection : §   The selection of sample elements is done randomly, without any systematic pattern or predetermined order. This randomness is essential to ensure that the sample is representative of the population and to minimize selection bias. 3.     Independence of Selection : §   Each selection is made independently of previous selections, meaning that the inclusion or exclusion of one element does not influence the ...
Post a Comment
Read more

Beta Activity compared to Muscles Artifacts

Dr. Rishabh Pathak
Beta activity in EEG recordings can sometimes be confused with muscle artifacts due to their overlapping frequency components. Frequency Components : o   Muscle artifacts often have frequency components of 25 Hz and greater, which can overlap with the frequency range of beta activity. o   Beta activity in EEG recordings typically falls within the beta frequency range of 13-30 Hz, with variations based on specific brain states and cognitive processes. 2.      Waveform Characteristics : o   Electromyographic (EMG) artifacts, which represent muscle activity, have distinct waveform characteristics that can help differentiate them from beta activity. o   EMG artifacts may exhibit a sharper contour with less rhythmicity, especially when the high-frequency filter is set at 70 Hz or higher, compared to the smoother contour and rhythmicity of beta activity. 3.      High-Frequency Filter Settings : o   Adjusting the high-frequency f...
Post a Comment
Read more