Hot answers tagged

14

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 ...


5

Vernor Vinge said that if we can scan a human brain and then simulate it: We can run it at 1000 times the speed. The brain will be able to do 1000 years of thinking in 1 year ect. At this stage in history we have the computer power. The trouble lies in cutting a brain up and scanning the 100 billion neurons and 12 million kilometres of axons and 100000 ...


5

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 ...


4

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 ...


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-...


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.,...


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

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

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

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

Neuromorphic engineering offers various of ways of reproducing the brain’s processing ability. The recent technology can include IBM's multi-artificial-neuron computer, the world's first artificial nanoscale stochastic phase-change neuronsarticle. Check the: Stochastic phase-change neurons study. Other can include Neurogrid, built by Brains in Silicon at ...


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 ...


2

Theoretically, there shouldn't be a problem copying either of the artificial brains in any state. Difficulty in measuring a state doesn't seem to really be a problem until you get down to the quantum level, where the means of measurement affect the state. The configuration of the artificial brains, including pathway structures and states, should be ...


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

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 thought the answer might be no. In this 2020 ICLR paper: The Curious Case of Neural Text Degeneration, researchers found that beam search text is less surprising compared to human natural language. And they proposed a nucleus sampling method which generates more human like text.


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. And ...


2

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

Yes, CNNs are inspired by the human brain [1, 2, 3]. More specifically, their operations, the convolution and pooling, are inspired by the human brain. However, note that, nowadays, CNNs are mainly trained with gradient descent (GD) and back-propagation (BP), which seems not to be a biologically plausible way of learning, but, given the success of GD and BP, ...


1

In the eye, the retinal ganglion cells have a receptive field that is equivalent to some types of convolution filters, most of them edge detectors. The brain is a big unknown, nobody knows how it does to organize, memorize, create concepts, learns the language, ... . Thus, it is not possible to establish a parallelism. In particular, brain has a capacity of ...


1

It looks like everything you want is available with the Deep Learning Toolkit (DLTK) for Medical Imaging There is also a blog: An Introduction to Biomedical Image Analysis with TensorFlow and DLTK There is a DataCamp course that walks you through most of the process but instead of a classifier they use deep learning to reconstruct brain images. They ...


1

There have been studies in University of Oregon and Kyoto University to be able to visualise thoughts and dreams on a screen using voxel values of an FMRI scan as input and an estimation of an image of the thoughts as the output. Instead of linking you to these studies and papers - you could just watch this episode of mind field where both these studies are ...


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

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 ...


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 ...


1

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 ...


Only top voted, non community-wiki answers of a minimum length are eligible