17
votes
When will the number of neurons in AI systems equal the human brain?
Some back of the envelope calculations :
number of neurons in AI systems
The number of neurons in AI systems is a little tricky to calculate, Neural Networks and Deep Learning are 2 current AI ...
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10
votes
Accepted
Do neurons of a neural network model a linear relationship?
In a neural network (NN), a neuron can act as a linear operator, but it usually acts as a non-linear one. The usual equation of a neuron $i$ in layer $l$ of an NN is
$$o_i^l = \sigma(\mathbf{x}_i^l \...
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7
votes
Accepted
Are biological neurons organized in consecutive layers as well?
Really short answer: yes
Slightly longer answer: kinda
Long answer:
Convolutional neural networks (CNNs), which are now a standard in image processing models, were inspired from work done by Hubel and ...
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7
votes
When will the number of neurons in AI systems equal the human brain?
Soon enough but that doesn't mean anything at all. In machine learning the word neuron represents a calculation whereas in brain the word neuron represent a specific type of cell which is a ...
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6
votes
Accepted
What is the calcium equivalent role in neural networks
Neural networks don't model biological neurons.
They are at best inspired by biological neurons, in that they get excited by certain inputs and fire once the excitation crosses a threshold. And this ...
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5
votes
Accepted
What determines the values of weights in a neural network?
Typically, weights are randomly initialized. Then, as the model is optimized for its given task, those weights are steadily made "better" as determined by the network's loss function. This is also ...
- 2,018
5
votes
Accepted
What makes the animal brain so special?
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^{...
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5
votes
Do neurons of a neural network model a linear relationship?
Almost never. The sum of linear functions is another linear function, so if neurons were only linear transformations there would be basically no point to having more than one neuron per layer. Instead,...
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4
votes
Is there research that employs realistic models of neurons?
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 ...
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4
votes
Can a neuron have both a bias and a threshold?
I assume you're talking about a perceptron threshold function. One definition of it with an explicit threshold is
$$f(\textbf{x})=
\begin{cases}
1& \text{if } \textbf{w}\cdot\textbf{x} > t\\
0&...
- 2,018
4
votes
Accepted
How do biological neurons weights get initialized?
In short
I mentioned in another post, how the Artificial Neural Network (ANN) weights are a relatively crude abstraction of connections between neurons in the brain. Similarly, the random weight ...
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4
votes
Accepted
Is there research that employs realistic models of neurons?
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. ...
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4
votes
How do layers in an artificial neural network transform inputs to outputs?
The basic calculation for a single neuron is of the form
$$\sigma\left(\sum_{i} x_i w_i \right),$$
where $x_i$ is the input to the neuron $w_i$ are the neuron-specific weights for every single ...
3
votes
Is there research that employs realistic models of neurons?
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 ...
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3
votes
Are Modular Neural Networks more effective than large, monolithic networks at any tasks?
There is indeed an investigation in progress, regarding this topic. A first publication from last march noted that modularity has been done, although not explicitly, since some time ago, but somehow ...
- 511
3
votes
Accepted
Are AI algorithms capable of self-repair?
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 ...
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3
votes
Are AI algorithms capable of self-repair?
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.
...
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3
votes
How to model inhibitory synapses in the artificial neuron?
Principles of Computational Modelling in Neuroscience by David Sterratt, Bruce Graham, Andrew Gillies and David Willshaw discuss it in Chapter 7 (The synapse) and also in Chapter 8 (Simplified models ...
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3
votes
When will the number of neurons in AI systems equal the human brain?
The answers so far haven't answered the question numerically, so here is my attempt to steer them in the direction I was seeking:
The freely available Deep Learning Book has the following figure on ...
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3
votes
How many nodes/hidden layers are required to solve a classification problem where the boundary is a sinusoidal function?
It depends on the accuracy you want. If you had 1 neuron, it could discern things across a line, if you have 2, you could solve things across 2 lines, etc. As you increase the number of neurons, you ...
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3
votes
When will the number of neurons in AI systems equal the human brain?
While interesting, this is all rendered somewhat moot if you think about what will happen once we understand how the brain works. After all, once we understood flight, we didn't start making birds. ...
- 131
3
votes
Do neurons of a neural network model a linear relationship?
Taking the question from comments on nbro's answer.
Am I wrong to see a clear relationship between how we are currently training networks and the classic function that defines a line?
You are ...
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3
votes
Why some neural network models in the 1980s shown as circuit models
In the early days of neural networks the theorists and practitioners were educated in mathematics, psychology, neurophysiology, electrical engineering, and neurobiology. Computer science was still in ...
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3
votes
What are (all) the differences between a neuron and a perceptron?
In addition to those mentioned differences, a perceptron can be thought of as a standalone model (which is trained with a specific algorithm, the perceptron algorithm), while the artificial neuron (...
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2
votes
Comments on my proposed "Jitter" neuron
Well, adding gaussian noise is a very common regularisation method.
Maybe this paper is interesting to you. They also have very small datasets.
In the end there is only so much you can get out of a ...
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2
votes
How to model inhibitory synapses in the artificial neuron?
In biology, when the presynaptic releases a neurotransmitter (a positive amount of them, obviously), this neurotransmitter reaches the postsynaptic vesicles causing an excitatory (depolarization) or ...
- 1,282
2
votes
How to model inhibitory synapses in the artificial neuron?
The Degree to Which Inhibition is in Common Use
What could loosely be considered inhibitory effect occurs in MLPs (multilayer perceptrons) as they are normally designed and implemented already.
The ...
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2
votes
What makes the animal brain so special?
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 ...
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2
votes
Accepted
Is the neuron adequately comprehended?
No, here is why. No approach can simulate the mind with 100% accuracy. a major notion that AI theorist refuse to note is that you cant take an orange and by virtue of technology turn it into an apple ...
- 194
2
votes
Accepted
CNNs: What happens from one neuron volume to the next?
Short answer: One to many
Long answer:
The point is that you use a 3D convolution in a CNN. Each kernel has the size of n*m*C (C is the number of feature maps) and every feature map has its own kernel(...
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