In What Time Period Does Neural Architecture Remodeling Take Place?

Neuronal architecture varies greatly among species and structures within the same nervous system. Understanding how neural circuit formation is orchestrated in the developing CNS and how regenerative capacities were lost over evolution can help. Hilton et al. explore neuron maturation processes that limit axon regeneration, including changes in gene expression, cytoskeletal dynamics, and mechanistic challenges in assembly formation and remodeling upon learning.

New technologies have rapidly advanced our understanding of how neural circuits are wired during development and how they are. Neural Logic Circuits (NLC) is an evolutionary, weightless, and learnable neural architecture loosely inspired by the neuroplasticity of the brain. Animal and human research has increasingly detailed the brain’s capacity for reorganization of neural network architecture to adapt to environmental conditions.

Neural networks are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. The most accepted time for the origin of the centralized nervous system is during the Ediacaran Period, when signs of burrowing substrates in the cortical plate grow at a rate of about 4 per week until approximately GA weeks 26-28.

Artificial neural networks (ANNs) are models created using machine learning to perform various tasks, inspired by neural circuitry. Schrodi introduces a unifying search space design framework based on context-free grammars that can naturally and compactly generate expressive hierarchical search spaces.


📹 Neural Network Architectures & Deep Learning

This video describes the variety of neural network architectures available to solve various problems in science ad engineering.


At what age does synaptic pruning in the visual cortex finish?

Brain development begins a few weeks after conception and is thought to be complete by early adulthood. The basic structure of the brain is laid down during the prenatal period and early childhood, with the formation and refinement of neural networks continuing over the long term. The brain’s many functions do not develop at the same time or follow the same developmental pattern.

Synapse pruning is a process that involves the overproduction of synapses and subsequent reduction of unused and overabundance of synapses. This process is largely experience-driven and occurs in areas of the brain involved in visual and auditory perception, such as the cortex, between the 4th and 6th year of life. In contrast, pruning in areas involved in higher cognitive functions (such as inhibitory control and emotion regulation) continues through adolescence.

The processes of overproduction and subsequent synaptic reduction are essential for the flexibility required for the adaptive capabilities of the developing mind, allowing individuals to respond to their unique environment. Myelination is the final process involved in brain development, where axons are wrapped in fatty cells, facilitating neuronal activity and communication. The timing of myelination depends on the region of the brain in which it occurs.

In summary, brain development is a complex process that begins a few weeks after conception and is thought to be complete by early adulthood.

Does synaptogenesis continue throughout life?

Synaptogenesis is a continuous process that begins at the moment of conception and continues throughout the lifespan, with a notable decline in frequency with age.

What is neuronal architecture?

A neural network is a complex structure consisting of neurons, layers, and weights, which are organized into layers to adjust the impact of inputs and biases. It is widely used in pattern detection applications, such as image and speech recognition, natural language processing, customer segmentation, and fraud detection. However, neural networks have limitations, such as requiring large amounts of labeled data and being opaque, making it difficult to understand why a network made a particular decision, known as the ‘black box’ problem.

At what age does synapse development peak?

Synapse production in the visual cortex peak at 8 months of age, while in the prefrontal cortex, peak levels occur in the first year. Synaptic pruning occurs rapidly between ages 2 and 10, eliminating about 50% of extra synapses. This pruning continues in the visual cortex until 6 years of age. Adolescence continues synaptic pruning, but not as fast, and the total number of synapses stabilizes. This process is crucial for complex behaviors like planning and personality.

How long does it take for neuroplasticity to occur?

Rehabilitation activities can aid in neuroplasticity, which is a process that occurs not only during therapy but also during daily activities like walking, speaking, and hand exercises. This process helps the brain create new connections, enabling recovery. While not everyone can fully recover from a stroke, many individuals can progress towards their individual goals, such as becoming stronger, more mobile, or more independent. With proper support, individuals can gain confidence and find new ways of doing things.

At what stage of development do negative experiences influence the architecture of the brain?
(Image Source: Pixabay.com)

At what stage of development do negative experiences influence the architecture of the brain?

Early experiences significantly impact the developing brain, determining its architecture and the impact of external influences on its development. During sensitive periods, healthy emotional and cognitive development is influenced by responsive interaction with adults, while chronic or extreme adversity can disrupt normal brain development. Children placed into orphanages with severe neglect show decreased brain activity compared to those without institutionalization.

To cope with adversity, children need to learn to cope with physiological responses, such as increased heart rate, blood pressure, and stress hormones like cortisol. Supportive relationships with adults can help a child learn to cope with everyday challenges, leading to positive stress. Tolerable stress occurs when serious difficulties are buffered by caring adults, mitigating the damaging effects of abnormal stress hormones.

Significant early adversity can lead to lifelong problems, with toxic stress experienced early in life and common precipitants of toxic stress, such as poverty, abuse, parental substance abuse, and violence, having a cumulative toll on an individual’s physical and mental health. The more adverse experiences in childhood, the greater the likelihood of developmental delays and other problems. Adults with more adverse experiences in early childhood are also more likely to have health problems, including alcoholism, depression, heart disease, and diabetes.

What age does neural pruning occur?

Synaptic pruning is a natural process where the brain removes unnecessary neurons and synapses, typically occurring between ages 2-10. The brain, which contains millions of neurons that communicate using electrical and chemical signals, eliminates extra synapses to ensure proper function. Synaptic pruning is a natural process that helps the brain maintain a healthy balance of neurons and synapses.

How long does it take to build new neural pathways?

The wiring of brain cells renders novel behaviors increasingly routine and less effortful over time. The formation of a new neural pathway and the mastery of a new behavioral pattern necessitate approximately 10, 000 repetitions or three months of practice, given the inherent variability among individuals in terms of brain structure and function.

What is neural remodeling?
(Image Source: Pixabay.com)

What is neural remodeling?

Developmental neuronal remodeling is a crucial process in shaping the connectivity of the mature nervous system, involving pruning exuberant neural connections and regrowth of adult-specific ones. Errors in remodeling are associated with neurodevelopmental disorders like schizophrenia and autism. However, our understanding of the mechanisms governing neuronal remodeling is far from complete, particularly how precise spatiotemporal control of remodeling and rewiring is achieved. Recently, cell adhesion molecules (CAMs) and other cell surface and secreted proteins have been implicated in processes of neurite pruning and wiring specificity during circuit reassembly.

The fruit fly Drosophila is emerging as a powerful model in the field due to its extensive, well-characterized, and stereotypic remodeling events occurring throughout its nervous system during metamorphosis. The wide and constantly growing toolkit to identify CAM binding and resulting cellular interactions in vivo has led to recent advances in uncovering spatiotemporal aspects of regulation.

Defects in the normal progression of remodeling have been implicated in various neurodevelopmental and neuropsychiatric conditions, such as schizophrenia, autism spectrum disorder, and Alzheimer’s disease. Despite constant progress, the molecular mechanisms underlying remodeling, specifically its spatiotemporal control, remain poorly understood.

In recent years, it is becoming increasingly evident that neuronal remodeling is not solely governed by intrinsic genetic programs and cell-autonomous mechanisms but is also highly dependent on interactions with the environment, including other neurons, non-neuronal cells, or the extracellular matrix. Recent studies have highlighted the importance of orchestrated circuit remodeling, where different neuronal types in a given network simultaneously remodel in an interdependent manner.

Cell adhesion molecules (CAMs) are prime candidates to mediate cell–cell interactions during coordinated circuit assembly and remodeling. However, much less is known about the function of CAMs in regulating the spatiotemporal precision of developmental remodeling. Circuit reassembly during remodeling, occurring at late developmental stages, in larger neurons, and for specific neuronal components, provides an excellent opportunity to deduce similar mechanisms of initial circuit formation.

What age does neural development occur?
(Image Source: Pixabay.com)

What age does neural development occur?

At birth, the average baby’s brain is about a quarter of the size of an adult brain, but it doubles in size in the first year and continues to grow until it reaches 80 of adult size by age 3 and 90 by age 5. The brain is the command center of the human body, and its connections enable us to move, think, communicate, and do various tasks. The early childhood years are crucial for making these connections, with at least one million new neural connections made every second.

Different areas of the brain are responsible for different abilities and develop at different rates. Brain development builds on itself, as connections link in more complex ways, enabling the child to move, speak, and think in more complex ways.

What are the stages of neural regeneration?
(Image Source: Pixabay.com)

What are the stages of neural regeneration?

Neuroregeneration is the process of regrowing or repairing nervous tissues, cells, or cell products. It involves the generation of new neurons, glia, axons, myelin, or synapses. Neuroregeneration differs between the peripheral nervous system (PNS) and the central nervous system (CNS) by the functional mechanisms involved, especially in the extent and speed of repair. Neuronal system injuries affect over 90, 000 people annually, with spinal cord injuries affecting an estimated 10, 000 people.

The nervous system is divided into the central nervous system (brain and spinal cord) and the peripheral nervous system (cranial and spinal nerves and associated ganglia). While the peripheral nervous system has an intrinsic ability for repair and regeneration, the central nervous system is mostly incapable of self-repair and regeneration. There is currently no treatment for recovering human nerve function after injury to the central nervous system. Multiple attempts at nerve re-growth across the PNS-CNS transition have not been successful due to insufficient knowledge about regeneration in the central nervous system.

Neuroregeneration is important clinically, as it is part of the pathogenesis of many diseases, including multiple sclerosis. Neural stem cells grafting, tissue regrowth, peripheral surgery, and prognosis are all part of the growing field of nerve regeneration and repair.


📹 Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn

This video on What is a Neural Networkdelivers an entertaining and exciting introduction to the concepts of Neural Network.


In What Time Period Does Neural Architecture Remodeling Take Place?
(Image Source: Pixabay.com)

Rafaela Priori Gutler

Hi, I’m Rafaela Priori Gutler, a passionate interior designer and DIY enthusiast. I love transforming spaces into beautiful, functional havens through creative decor and practical advice. Whether it’s a small DIY project or a full home makeover, I’m here to share my tips, tricks, and inspiration to help you design the space of your dreams. Let’s make your home as unique as you are!

Email: [email protected], [email protected]

About me

24 comments

Your email address will not be published. Required fields are marked *

  • Steve, you are the first person I have ever seen describe an overview of neural networks without paralyzing the consciousness of the average person. I look forward to more of your lectures, focused in depth on particular aspects of deep learning. It is not hard to get an AI toolkit for experimentation. It is hard to get a toolkit and know what to do with it. My personal interest is in NLR (natural language recognition) and NLP (natural language programming) as applied to formal language sources such as dictionaries and encyclopedias. I look forward to lectures covering extant NLP AI toolkits. Sincerely, John

  • steve brunton idk who u r before perusal this. but this presentation style of a glass whiteboard w/ image superimposed is the best way ive ever seen someone teach tbh. thank u at least for that. but more importantly this actually helped me understand the beast of neural nets a little more and hopefully be more prepared when our new ai overlords enslave us at least we will know how they think

  • He Steve, thank you a lot for all your brilliant articles! One request on the topic, could you please cover how all this works with shift/rotation/scale of the image? Nobody on youtube covers this tricky part of the neuron networks used for image recognition. I keep fingers crossed that you the one who could clarify this.

  • I really really really like the way you present- could you help me understand your set up? There’s a see-through glass that you draw on, there’s a projector (i think) that’s allowing you to see which part of the presentation you’re in. Plus the dark shirt enables me to just focus on your face, and your hands. It’s a very intuitive interface for learning. Your hand gestures easily capture my eyes’ attention. Do please elaborate. Thanks!

  • Very nice. I like the autoencoders. That is basically just understanding. Intelligence is basically just a compression algorithm. The more you understand the less data you have to save. You can extract information from your understanding. That’s basically what the autoencoder is about. For instance, if you want to save an image of a circle you can store all the pixels in the image, or store the radius, position and color of it. Which one takes up more space? Well, storing the pixels. We can use our understanding of the image containing a circle in order to compress it. Our understanding IS the compression. The compression IS the understanding. It’s the same.

  • Important note about the function operating on a node. If the functions of two adjacent layers are linear, then they can be equivalently represented as a single layer (compositions of linear transforms is itself a linear transformation and thus could just be its own layer). So, nonlinear transformations are necessary for deep networks (not just neural networks). That isn’t to say you can’t have a composition of linear transformations to compose an overall linear transformation, if there’s nonlinear constraints for each operator.

  • I guess neurones can be thought of a functions that call other functions if a certain variable has a sufficient value. And the main difference between an ANN and our biological neural network is that ANN has a fixed set of functions with fixed connections, only changing the conditions triggering the next callback, whereas brains can grow new neurones and even disconnect and rewire connections. The question then becomes: Can we write a function that writes a new function? Or a function that modifies the content of an existing function so as to change its callback to call a different function? If this holds true, we could get even closer to natural neural networks. I’m also debating myself when to use “artificial” vs “synthetic”. I guess an (A)NN can’t rewire/reprogram itself, whereas a real one can? In which case if we produce a neural network that indeed can change its own inner structure, we could promote it from “artificial” to “synthetic”? Great article. Definitely earned yourself a subscriber. 🙂

  • I started to learn NNs in good old early 2000-s. No internet, no collegues, nor even friends to share my excitement about NNs. But even then it was obvious that the future lies with them, though I had to concentrate on more essential skills for my living. And only now, after so many years have passed, I tend to come back to NNs, cause I’m still very excited about them and it is much-much-much easier now at least ot play with them (much more powerful computers, extensive online knowlegde base, community, whatever), not speaking about career opportunities. I’m glad YT somehow guessed I’m interested in NNs, though I haven’t yet searched for it AFAIR. It gives me another impetus to start learning them again. Thanks for the article! Liked and sub-ed.

  • One of the most effective and useful introductory lectures on neural networks you can attend. It provides basic terminology and enables a good foundation for other lectures. HIGHLY RECOMMENDED. It would be helpful, Mr. Bunton, to say a little bit more about Neurons. Is a neuron strictly a LOGICAL function point in a process (my simple excel cell doing a logical function qualifies as a neuron with your definition), is it a PHYSICAL function point like a server, or is it both? Was there a reason you did not mention restricted Boltzmann motors? Thank you again, Sir, for the quality of this lecture.

  • In the name of fairness, I must say that YouTube AI recommender system did only 50% of the job bringing you, to the thumbnail of this article… The other 50%? Well… YOU PLAYED IT . Humans will remain humans in every generation… AI will have their gradients reduced on every iteration 🥶🤖💀… ( Insert Ghanaian Coffin Guys Meme here )

  • Steve: nice talk,… many questions come up, I’ll ask a few 1)Do you distinguish planar vs non-planar networks? 2)Do RNN(s) become unstable? They look like control system time dependent processes. 3)Has anyone applied Monte Carlo toward selection of topology of a NN, or toward the activation function selection,…? Fascinating area to study.

  • Hi! I am medical doctor with little background on computing studies or mathematics but great interest in data and its use for medical research and patient’s care. I am now drafting a booklet on Machine Learning for health care workers with no previous coding background and found this article extremely clear and helpful. Would you allow me to add a link to this article in the booklet?

  • trying to create neural network through manual arrangements is fool’s errand. What is needed is Genetic Algorithm which can generate a best neural net configuration that can learn the task best. usually the process goes like this data -> some neural net -> model. but the better approach is… data -> genetic algo -> neural net data -> neural net -> model. in essence you are not just adjusting weights, but also topology with data, surely changes in topology need to slower. what factors can we control in a neuron – input power for it turn on (threshold, usually the only thing that’s controlled) – time it stays on (active period) – time it needs to recharge (cool off period) – time it takes from on trigger to output on (delay period) another flaw in topology is it’s always forward facing, real brain is more like a graph than layers..

  • :face-purple-wide-eyes::face-purple-wide-eyes::face-blue-wide-eyes::face-blue-wide-eyes::face-orange-biting-nails::face-orange-biting-nails::face-blue-smiling::face-blue-smiling::face-orange-raised-eyebrow::face-orange-raised-eyebrow::eyes-purple-crying::eyes-purple-crying: Can I use your mathematical apparatus, to investigate the physical processes of Metaphysics?? I am looking for a mathematical apparatus capable of working with metaphysical phenomena, i.e. metamathematics!!

  • Liked that the approach was direct and simplistic; and of course you can write your code in this manner too. So that you’re not overwhelmed. Say four or five layers being coded, then you have outboard functions that handle the input and out put arrays. This last might take up most of the landscape of a program. Isn’t this fellow clever? Dang. He’s gotta be a Professor somewhere. Many thanks. The computer training that I had gotten was very rudimentary, first in the 60s and then another drop in the mid 90s. Luckily there’s YT where you can catch up. And after a while the ‘training’ starts to remind you of subliminal sorts of stuff. Maybe?

  • I’d submit that your architecture diagrams are missing a box for the process acting upon the network. It’s great to show the data, but the process should also be shown as well. For example, what if you have two processes acting upon the same neural network graph simultaneously? Where would those processes be depicted?

  • Hey i know it’s been a few years and it’s an otherwise great article but you just seemed to imply “tanh” as an alternative name for sigmoid, which is a bit incorrect. Only stating this since it’s valuable information for learners to know how these two are similar (in shape) but very different functions. Sigmoid goes from 0 to 1 but hyperbolic tangent goes from -1 to 1 which might not seem much of a difference but it’s substantially different when you have architectures like LSTMs. Referring to 1:10

  • Thank you for your article! Seeing your example for principal values decomposition made neural networks much clearer to me than anything else I had seen till now. It allowed me to connect this to SVD-based linear modeling I used almost 10 years ago to create simplified models of visual features seen in fluid dynamics. I did not expect how much easier this suddenly seemed when it connected to what I already knew.

  • excuse me i have a question not about neural network but about the article itself, how did you shoot this article ?! i don’t think you added this images and presentation in post, i think you can see it and where it is in the screen, i think that there is a glass in front of you and the presentation displayed on it, how you made this ?!, thanks

  • these are all 2d, design something isotropic which ceases when each neuron has 255 connections. the core neuron should be superpositional so input and output, but rather than starting from 2, you need to have only one local connection to the core neuron within the ann, because when it is formed into a mega ann (mann) it will connect to its neighbour by way of an additional connection to each core. a pair of mann, which are superpositional at the core and also an inverse transposition of eachother, is – in my belief the minimum required to form a sentient ai. ram address locations should be the same as neuronal path variations with somehow superpositionally plus or minus one address location in active memory, this is how we get to subjective / spontaneus decision making within symmetrical input inquiry. also keep in mind the pattern for the mega ann needs to have symmetry on all 3 spatial axis after the second neuron outwards, this will enable the single local link from neuron 1 to 2 to be precisely inverted in the other mega ann, this leaves an additional symmetry across the pair which can represent time. all self definitions and heuristic processing comes by way of this pair of linked core neurons. just some shit i pulled from the ether. Liam

  • Hi Steve, thanks for all the amazing material that you post on different topics. They are simple and easy to understand. I was curious if it is possible to access some of the images (cartoon diagrams) that you show here. I would love to use those for some of my presentations (where I use deep learning, transfer learning for my graduate research), if that is alright. Thanks very much!

  • Computers and AI might not be harmful but the programmers can be. If user x = browsing edu/academic then search all user x input and forward copy to professor y + delete all user x input if user x = files complain then file report and forward to local police if user x = banking/finance online find user x bank/finance login sites….

  • Dear Prof. Brunten, Thanks for your nice lecture. My field of study is mathematics. You mentioned that there are different types of activation functions. I saw in some lecture people said the aim of using activation function is nonlinear classification. I don’t know why these are called activation functions? i mean which property do they have? Can we create an arbitrary activation function or not?

  • 👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏 I can’t able to type, but he deserves more than this👏

Pin It on Pinterest

We use cookies in order to give you the best possible experience on our website. By continuing to use this site, you agree to our use of cookies.
Accept
Privacy Policy