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

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...

Translocation, Retention and Potential Neurological Lesion in The Brain and Following Nanoparticle Exposure

Translocation, retention, and potential neurological lesions in the brain following nanoparticle exposure are important considerations in nanotoxicology and neurotoxicology research. Here are some key points regarding the impact of nanoparticle exposure on the brain: 1.       Translocation to the Brain : o Nanoparticles can enter the brain through various routes, including systemic circulation, olfactory nerve pathways, and disrupted blood-brain barrier (BBB) integrity. o Factors such as nanoparticle size, surface properties, shape, and surface modifications influence their ability to cross biological barriers and reach the brain parenchyma. 2.      Retention in the Brain : o Once nanoparticles translocate to the brain, they may exhibit different retention times depending on their physicochemical properties and interactions with brain cells. o Nanoparticles can accumulate in specific brain regions, such as the olfactory bulb, hippocampus, and...

Distinguishing Features of Electrode Artifacts

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...

Elements Selection Techniques

Element selection techniques play a crucial role in determining how individual elements or units are chosen from the population to form a sample. Here are some common element selection techniques used in sampling: 1.     Unrestricted Sampling : §   In unrestricted sampling, each element in the population has an equal chance of being selected for the sample. This approach is commonly used in simple random sampling, where every element is selected independently of other elements. 2.     Restricted Sampling : §   Restricted sampling involves imposing certain restrictions or conditions on the selection of sample elements. This can include stratification, clustering, or other criteria that guide the selection process. Restricted sampling techniques include: §   Stratified Sampling: The population is divided into homogeneous subgroups (strata), and samples are selected from each stratum to ensure representation of different characteristics. § ...

Beta Activity compared to Muscles Artifacts

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...