Skip to main content

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

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...

Manifestation of blindness-induced Neuroplasticity at different scales


 Blindness-induced neuroplasticity manifests at different scales within the brain, reflecting the adaptive changes that occur in response to the loss of vision. Here are some manifestations of blindness-induced neuroplasticity at different scales:

1. Neurotransmitter Level: At the neurotransmitter level, blindness can lead to alterations in the balance between inhibitory and excitatory neurotransmitters in the brain. These changes in neurotransmitter activity can influence the overall excitability and functioning of neural circuits, contributing to adaptive responses to vision loss.

2. Cortical Reorganization: Blindness can result in cortical reorganization, where areas of the brain that were originally dedicated to processing visual information undergo functional changes to accommodate non-visual functions. For example, the visual cortex may be repurposed for processing tactile or auditory information, reflecting the brain's ability to adapt to the absence of visual input.

3. Structural Changes: Blindness-induced neuroplasticity can also lead to structural changes in the brain, such as alterations in gray matter volume or cortical thickness. Studies have shown that the visual pathway and cortical areas may exhibit differences in structural organization in response to vision loss, with late blindness potentially inducing less structural changes compared to early blindness.

4. Cross-Modal Plasticity: One of the key manifestations of blindness-induced neuroplasticity is cross-modal plasticity, where the brain integrates information from different sensory modalities to compensate for the loss of vision. This adaptive reorganization can occur at the level of the primary sensory cortex (V1) and lead to enhanced processing of non-visual sensory inputs, such as tactile or auditory information.

5. Functional Connectivity: Changes in resting-state functional connectivity have been observed in blind individuals, reflecting alterations in how different brain regions communicate in the absence of vision. Studies have shown weakened connectivity within the visual cortex and between visual and other sensory regions following vision loss, with potential restoration of connectivity patterns after sight recovery interventions.

6. Experience-Dependent Plasticity: The manifestation of blindness-induced neuroplasticity can also be experience-dependent, with factors such as early exposure to tactile stimuli influencing the degree of cortical reorganization and sensory processing enhancements in blind individuals. For example, learning Braille at an early age has been associated with higher tactile-induced visual responses, highlighting the role of experience in shaping neuroplastic changes.

 

By examining blindness-induced neuroplasticity at different scales, researchers can gain insights into the adaptive mechanisms that underlie the brain's ability to reorganize and compensate for the loss of vision. Understanding these manifestations is essential for developing targeted interventions and rehabilitation strategies to optimize sensory processing and functional outcomes in individuals with visual impairments.

Comments

Popular posts from this blog

Ocular Artifacts

Ocular artifacts in EEG recordings are commonly caused by movements of the eyes, such as blinking, eye flutter, and eye movements associated with eye opening and closure. These artifacts can mimic epileptiform discharges and other EEG patterns, making them important to recognize and differentiate.  Nature of Ocular Artifacts : o    Ocular artifacts are primarily due to the movement of the electrical dipoles created by the eyes, particularly during activities like eye opening, closure, and directed gaze. o    The movement of these dipoles generates changing electrical fields that appear as EEG activity, with characteristics such as slow waves and spikes. o    Ocular artifacts are typically limited to the frontal region and do not extend into the central region of the brain. 2.      Differentiation from Epileptiform Discharges : o   Ocular artifacts can sometimes appear as bifrontal spike and slow wave complexes, resembling inter...

Unrestricted Sampling

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 ...

Amphiarthrodial or Cartilaginous Joints

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...

Cell Maturation (Dendrite and Axon Growth)

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...

Distinguishing Features Ictal Epileptiform Patterns

The distinguishing features of ictal epileptiform patterns are critical for differentiating them from other EEG activities and for accurate seizure diagnosis. Here are the key distinguishing features outlined in the document: 1.      Stereotyped Nature : Ictal patterns are often stereotyped across seizures for the individual patient. This means that the same pattern tends to recur in different seizures, which aids in identification. 2.    Evolution of Activity : A hallmark of ictal patterns is their evolution, which can manifest as changes in frequency, amplitude, distribution, and waveform. This evolution is a key feature that helps differentiate ictal patterns from other types of EEG activity, such as normal rhythms or artifacts. 3.   Behavioral Changes : Ictal patterns are typically associated with stereotyped behavioral changes. While some seizures may not exhibit obvious movements, the presence of behavioral changes is a significant indicator of s...