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15

There is no direct way to find the optimal number of them: people empirically try and see (e.g., using cross-validation). The most common search techniques are random, manual, and grid searches. There exist more advanced techniques such as Gaussian processes, e.g. Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act ...


11

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 systems as you call them, specifics are hard to come by (If someone has them please share), but data on parameters do exist, parameters are more analogous to ...


8

In reverse order to how you asked: all units in a layer become equal since initially the errors due to all of them are the same and thus we train them to be equal This actually happens if you initialise the weights equally (e.g. all zero). Gradients in that case are the same to each neuron in the same layer, and everything changes in lockstep. A neural ...


7

For a more intelligent approach than random or exhaustive searches, you could try a genetic algorithm such as NEAT http://nn.cs.utexas.edu/?neat. However, this has no guarantee to find a global optima, it is simply an optimization algorithm based on performance and is therefore vulnerable to getting stuck in a local optima.


7

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 biochemical system.


6

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 second point even holds only approximately because the backpropagation algorithm needs smoothed out steps to learn by gradient descent. And backpropagation is ...


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

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 lets apply the same logic here. neurons are temporary things in our brains, daily we are trimming our brains and growing our brains, in order to "Engineer" a ...


4

Paper Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[J]. arXiv preprint arXiv:1512.00567, 2015. gives some general design principles: Avoid representational bottlenecks, especially early in the network; Balance the width and depth of the network. Optimal performance of the network can be reached ...


4

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 initialization step in ANNs is a simple procedure that abstracts the complexity of central nervous system development and synaptogenesis. A bit more detail (with ...


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.,...


4

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 referred to as "training" the neural network. By far the most popular way of updating weights in a neural net is the backpropagation algorithm, most simply with ...


4

I assume you're talking about a perceptron threshold function. One definition of it with an explicit threshold is $$f(\textbf{x})= \begin{cases} 1& \text{if } \textbf{w}\cdot\textbf{x} > t\\ 0& \text{otherwise} \end{cases}.$$ Another form with a bias is $$f(\textbf{x})= \begin{cases} 1& \text{if } \textbf{w}\cdot\textbf{x} + b > 0\\ 0&...


4

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 input and $\sigma$ is the pre-specified activation function. In your terms, and disregarding the activation function, the calculation would turn out to be $$c\,a_c ...


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

"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 of neurons). Especially in chapter 8, they discuss how to add excitatory or inhibitory sysnapses into integrate and fire neuron. There are various ways to ...


3

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 page 27: I question the blue fit line, as it seems that data points may be better described by a parabolic or exponential function. In any case, based upon ...


3

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 given dataset.


3

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 are increasing the number of discernible areas. As you increase the number of lines you can use to break up the input space, the lines can be placed to ...


3

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. The same goes for AI. Here are just a few ways in which human brains and digital brains can't be compared. The digital brain won't have to worry about food 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

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 inhibitory (hyperpolarization) effect, depending on the kind of postsynaptic vesicle in next cell dendrites. If the total amount of depolarization (all dendrites)...


2

Yes, for many sensory inputs there is indeed something similar to normalization. But its not rally the same as in classical data analytics compared to what eg min/max normalization does or other technics. Lets look on some examples and considerations: mammals don't perceive heat or loudness in a linear way. This is because already many sensory receptors ...


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

The most generic approach is to input all pixels as you have suggested. A CNN would be the best architecture for that. To provide information like speed or velocity, you can feed more than one frame to the CNN (e.g. the last 5 frames or whatever provides enough information). The CNN can learn movement information by comparing those images. If you want to ...


2

I recommend preprocessing images and feeding pixel values of several combined images. Some ideas: Preprocess all images to grayscale if possible. It’ll reduce the number of input neurons. (As long as this step doesn’t introduce large overhead) Select some $\gamma$ value such that 0 < $\gamma$ < 1. Generate (ie. Select from your game) $n$ sequential ...


2

Simon Krannig's answer provides the math notation behined exactly what is going on, but since you still seem a bit confused, I've made a visual representation of a neural network using only weights with no activation function. See below: So I'm fairly sure it as you suspected: At each neuron, you take the sum of the inputs of the previous layer multiplied ...


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