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10

The thing you were reading about is known as the action potential. It is a mechanism that governs how information flows within a neuron. It works like this: Neurons have an electrical potential, which is a voltage difference inside and outside the cell. They also have a default resting potential, and an activation potential. The neuron tends to move towards ...


9

How Artificial Neural Networks (ANNs) are different from the Biological Neural Networks (BNNs) depends on what you are looking for. We all know that the ANNs are inspired by the Biological ones. Structural differences: In general, a neural network consists of four components: neurons topology: the connectivity path between neurons weights learning ...


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The release of Adenosine, Dopamine, Endorphin, Endocannabinoids, GABA, Glutamate, Norepinephrine, Oxytocin, Serotonin, and many others into specific regions of the brain are very likely an essential part of both activation tuning of single neurons and neuroplasticity, two essential aspects of organic learning researchers have been and will continue to work ...


5

One probable hardware limiting factor is internal bandwidth. A human brain has 10^15 synapses. Even if each is only exchanging a few bits of information per second, that's on the order of 10^15 bytes/sec internal bandwidth. A fast GPU (like those used to train neural networks) might approach 10^11 bytes/sec of internal bandwidth. You could network 10,000 ...


4

They are not close, not anymore! [Artificial] Neural Nets vaguely inspired by the connections we previously observed between the neurons of a brain. Initially, there probably was an intention to develop ANN to approximate biological brains. However, the modern working ANNs that we see their applications in various tasks are not designed to provide us a ...


4

State of Rosehip Research The Rosehip neuron is an important discovery, with vast implications to AI and its relationship to the dominant intelligence on earth for at least the last 50,000 years. The paper that has spawned other articles is Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type, Buldog et. al.,...


4

It looks like you really have two questions here. I'll try to answer the first one, and you should think about making a separate question for the second. There is research into using simulated models of biologically realistic neurons. While there are large projects like the Human Brain Project aimed at simulating human brains, there is also a lot of lower-...


3

Yes, this was an active area of research in a number of different AI fields. Probably the most directly related work is Bongard, Zykov & Lipson's self-repairing robots from the early 2000's. There's some more recent work from Mark Yim that you can see here too. There are lots of different ways to do this, but Bongard et al's approach was probably the ...


3

It is true that the current Machine learning is based on treating neurons as a component in the whole complexity , mesh of neurons. The focus is more on the architecture rather than understanding or imitating the basic block of it more clearly , i.e. the neurons. Anirban Bandhopadhyay is a biologist and Neurologist who has studied how the harmony changes ...


3

I think a big problem with intelligent robots is that the world is very dynamic and always changing and our techniques right now are still quite static and not really flexible. I myself come from a Computer Vision background and in this field I often see some limitations of one of the most promising approaches for AI right now (Deep Learning). For example ...


3

The common statement that Artificial Neural Networks are inspired by the neural structure of brains is only partially true. It is true that Norbert Wiener, Claude Shannon, John von Neuman, and others began the path toward practical AI by developing what they then called the electronic brain. It is also true Artificial networks have functions called ...


3

Human intelligence is very general / broad in its scope. This is self-evident, and whatever AI ends up to be, we'd like it to be a general problem solver as well (cf. Simon and Newell). Taking liberal interpretations of your question... Why AI in a computer? Computers, to the extent that we can frame problems in general as a solvable computational ...


3

First, it is not possible to fully define or duplicate the actions of a conscious agent using only AI theory derived from Church-Turing. In other words, the human brain is not a type of Turing Machine. This can be proven. Are all this artificially intelligent agents/programs being created or will be created in near future, just a set of rules and ...


2

This has been my field of research. I've seen the previous answers that suggest that we don't have sufficient computational power, but this is not entirely true. The computational estimate for the human brain ranges from 10 petaFLOPS (1 x 10^16) to 1 exaFLOPS (1 x 10^18). Let's use the most conservative number. The TaihuLight can do 90 petaFLOPS which is 9 ...


2

Just a little bit of a glimpse. We are in this age of artificial narrow intelligence,where by many various applications are in phase of development, based on the case scenarios given in the question.ie computing power is out but not to the full requirement of artificial intelligent agent nor robot. According to the Microsoft co-founder said in MIT ...


2

I am not a professional but I have been thinking a lot about AI and neural nets, so I thought I might add my 2 ct. "Acting intelligently" or "deriving goal directed action from sensory input" is actually a lot more complex than what computer processors have been doing so far. I think we are on a good path right now, but it will still be quite a while before ...


2

The brains of mammals do not use an activation function. Only machine learning designs based on the perceptron multiply the vector of outputs from a prior layer by a parameter matrix and pass the result statelessly into a mathematical function. Although the spike aggregation behavior has been partly modeled, and in far more detail than the 1952 Hodgkin and ...


2

The Title Question Is there any paper, article or book that analyzes the feasibility of achieving AGI through brain-simulation? Yes. There are various analyses that have been published. We have some early work like Some Philosophical Problems From the Standpoint of Artificial Intelligence, John McCarthy and Patrick J. Hayes, Stanford University, 1969. ...


2

The cell of a perceptron was based on an oversimplified conception of a neuron. At the time, neural plasticity, timing factors in relation to activation, neurochemical pathways, and energy transit complexities in axons were unknown. The mapping of pulse transmission to basic algebra seemed unrealistic, so timing was ignored. Plasticity, timing, and regional ...


2

Understanding a human brain fully, how hippocampus and neocortex works would surely help AI enthusiasts to make better AI algorithms with superior memory, ability to learn and finding what precisely intelligence, feelings and conciousness are by itself. The AI looks at psysiological and anatomical data as a source of suggestions regarding possible mechanisms ...


2

I don’t think AI is simulating the brain functions and not even close. Do you know how the nervous system work? How the neutrons transmit signals with action potential? Pathway analysis? Splicing junctions? AI is not about simulating the brain at all. We don’t simulate the biology pathway, we don’t simulate alternative splicing, we don’t have proteins in ...


1

Good question. It is related to the genetic algorithm concept, automated bug detection, and continuous integration. Early Genetically Inspired Algorithms Some of the Cambridge LISP code in the 1990s worked deliberately toward self-improvement, which is not the same as self-repair, but the two are conceptual siblings. Some of those early LISP algorithms ...


1

The question and the example are a few contradictory. The example is about a physical brain damage. Computer systems with the ability to self-repair exists from 1970's. They can repair a damaged disk (RAID), replace a CPU by an idle one (active/passive), mark faulty memory blocks, redirect network traffic from broken links to available ones, ... nowadays ...


1

What are the top contributions from neuroscience to artificial intelligence and vice versa? Here is a glimpse of one of my favorite companies doing it big in artificial intelligence field,inline with its contribution to neuroscience;otherwise. DeepMind It's goal is to build a general AI systems with the ability to think,reasoning and learn flexibility ...


1

The answer is We do not know. Odds are, we will not know for quite a while. The reason for this is we cannot understand the "code" of the human brain, nor can we simply feed it values and get results. This limits us to measuring currents of the input and output on test subjects, and we have had few such test subjects that are human. Thus, we know almost ...


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For what its worth (and having done a bit of study on this and being really interested in the topic): the answer seems to go back to the beginnings of AI and even earlier (Turing's 1936 paper in which he introduces what's now called the Turing machine). John McCarthy's filer for the 1956 Dartmouth College summer workshop on "Artificial Intelligence" (...


1

I think a worthwhile extension of this line of thought is "why not both?" I do not believe there is anything preventing approaching the problem from both sides at once. There is a great deal of research on both sides (biological research and computational research), but considerably less on the integration of the two (although there certainly is some, such ...


1

There are a number of reasons why a simulated brain might be better than creating a real brain. One reason is computers can live indefinitely (kind of). Brains may not be able to live forever and there might not be a way to transfer information from one brain to another. One of the principle advantages of a computer then is that it could have more experience ...


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