Questions tagged [artificial-neuron]

For questions about what constitutes an artificial neuron and how artificial neurons can be utilized as part of a neural network.

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33
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4answers
1k views

How to find the optimal number of neurons per layer?

When you're writing your algorithm, how do you know how many neurons you need per single layer? Are there any methods for finding the optimal number of them, or is it a rule of thumb?
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2answers
414 views

Are biological neurons organized in consecutive layers as well?

I'm now reading a book titled Hands-On Machine Learning with Scikit-Learn and TensorFlow and in the Chapter 10 of the book, the author writes the following: The architecture of biological neural ...
8
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3answers
169 views

Is there research that employs realistic models of neurons?

Is there research that employs realistic models of neurons? Usually, the model of a neuron for a neural network is quite simple as opposed to the realistic neuron, which involves hundreds of proteins ...
8
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4answers
257 views

Which artificial neural network can mimic biological neurons the most?

On the Wikipedia page we can read the basic structure of an artificial neuron (a model of biological neurons) which consist: Dendrites - acts as the input vector, Soma - acts as the summation ...
8
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2answers
321 views

Back-of-the-envelope machine learning (specifically neural networks) calculations

There is a popular story regarding the back-of-the-envelope calculation performed by a British physicist named G. I. Taylor. He used dimensional analysis to estimate the power released by the ...
7
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3answers
539 views

How to model inhibitory synapses in the artificial neuron?

In the brain, some synapses are stimulating and some inhibiting. In the case of artificial neural networks, ReLU erases that property, since in the brain inhibition doesn't correspond to a 0 output, ...
6
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1answer
333 views

How many nodes/hidden layers are required to solve a classification problem where the boundary is a sinusoidal function?

A single neuron is capable of forming a decision boundary between linearly seperable data. Is there any intuition as to how many, and in what configuration, would be necessary to correctly approximate ...
5
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1answer
71 views

Are neurons in layer $l$ only affected by neurons in the previous layer?

Are artificial neurons in layer $l$ only affected by those in layer $l-1$ (providing inputs) or are they also affected by neurons in layer $l$ (and maybe by neurons in other layers)?
5
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1answer
267 views

Is the neuron adequately comprehended?

It is possible that the signal handling of a neuron is outside the engineering comprehension of the most astute of human brains, even after the relationships of inputs to outputs are statistically ...
5
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2answers
366 views

What do neural connection weights represent 'conceptually'?

I understand how Neural Networks work and have studied its theory well. My question is at the intricacies of Deep Neural networks and perhaps is a bit beyond common understanding (as I have been told (...
4
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1answer
1k views

Do we know what the units of neural networks will do before we train them?

I was learning about back-propagation and, looking at the algorithm, there is no particular 'partiality' given to any unit. What I mean by partiality there is that you have no particular ...
4
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2answers
200 views

Can neurons in MLP and filters in CNN be compared?

I know they are not the same in working, but an input layer sends the input to $n$ neurons with a set of weights, based on these weights and the activation layer, it produces an output that can be fed ...
4
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1answer
159 views

What effect does a negative output of a neuron have on neighbouring neurons?

Artificial neural networks are composed of multiple neurons that are connected to each other. When the output of an artificial neuron is zero, it does not have any effect on neighboring neurons. When ...
3
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2answers
167 views

How do layers in an artificial neural network transform inputs to outputs?

To me, most ANN/RNN related articles don't tell me actually how the network is implemented. I know that in the ANN you'll have multiple neurons, activation function, weights, etc. But, how do you, ...
3
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2answers
170 views

How do biological neurons weights get initialized?

When trying to map artificial neuronal models to biological facts it was not possible to find an answer regarding the biological justification of randomly initializing the weights. Perhaps this is ...
3
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4answers
132 views

Could a large number of interconnected tiny turing-complete computer chips be patterned across a wafer to simulate a programmable neural network?

The Intel 8080 had 4500 transistors and ran at 2-3.125 MHz. By comparison, the 18-core Xeon Haswell-E5 han 5,560,000,000 transistors and can run at 2 GHz. Would it be possible or prudent to simulate a ...
2
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3answers
234 views

Where does the so-called 'loss' / 'loss function' fit into the idea of a perceptron / artificial neuron (as presented in the figure)?

I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.3 Choice of Activation and Loss Functions presents the following figure: $\overline{X}$ is ...
2
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1answer
48 views

How can I train a neural network for another input set, without losing the learning of the previous input set?

I read this tutorial about backpropagation. So using this backpropagation we are training the neural network repeatedly for one input set, say [2,4], until we reach 100% accuracy of getting 1 as ...
2
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1answer
45 views

In a neural network, can colors be used for neurons in place of floating points and would there be any benefit in doing so?

Firstly, some context. I have been reading and watching videos on the subject for around 3 years, but I am still very much a beginner in machine learning and artificial intelligence. That said, I ...
2
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1answer
64 views

Is input normalization built-in into mammals sensory neurons?

The spectrum of human sensory inputs seems to fall within certain ranges suggesting normalization is built-in into biological NNs? It also adapts to circumstantial conditions, e.g. people living in a ...
2
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1answer
474 views

Planning a Neural Network

I'm trying to understand how to effectively plan and write a Neural Network but running into problems with understanding how they should be written. I'm working with classification with writing a ...
2
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1answer
922 views

Is “dataset size” and “model size” same thing?

I mean what is determine my model size, connection amount between layers and neurons, or size of my dataset?
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1answer
43 views

Applying Artificial neural network into kaggle's house prices data set gave bad predicted values

I am trying to solve the kaggle's house prices using neural network. I've already made it with ensembling several models (XGBoost, GradientBooster and Ridge) and I've got a great score ranking me ...
1
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1answer
182 views

Method to compute the sum when the activation is a continuous function?

Background My understanding is the input neurons seem to seem to compute a weighted sum moving from one layer to another. $$ \sum_i a_i w_i = a'_{k} $$ But to compute this weighted sum the sum ...
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2answers
75 views

Is understanding value for different features next step for object recognition?

Once the artificially intelligent machines are able to identify objects, we might want to teach them how to value different things differently based on their utility, demand, life, etc. How can we ...
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2answers
254 views

Why do we need weights when training a perceptron as an OR gate?

Without using any of Matlab's neural network tools, I'm writing a program to simulate an OR gate with a perceptron. I have seen many tutorials, but I still can't understand why we need weights to ...
1
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1answer
35 views

Why one unit in the layers of neural network is not enough?

In a deep connected network, when every unit gets all the input features(X) so it has one parameter for every feature and every unit tweaks its parameters for loss optimization. What if we use only ...
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2answers
183 views

Can AI help summarize article or abstract sentence keyword?

I'm wondering if AI now can help us abstract summary or general idea of long article, for example novel or historical stories, or abstract most important keyword from sentence; Would you please tell ...
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1answer
46 views

Is it a great misconception that the softmax is an activation function?

An activation function is a function from $R \rightarrow R$. It takes as input the inner products of weights and activations in the previous layer. It outputs the activation. A softmax however, is a ...
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1answer
46 views

In practice, are perceptrons typically implemented as objects?

I'm fairly new to ANNs. I know the general structure, the math, and the algorithms behind them. I figured the logical next step on my journey to fully understanding them would to be implement one ...
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0answers
34 views

What is the output of neuron $y_{2}$ at time step $t$?

In Fundamentals of Neural Networks: Architectures, Algorithms And Applications by Laurene V. Fausett on $\text{Page:32}$ it describes Hot and Cold perception modeling with McCulloch-Pitts Net the ...
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1answer
66 views

What is the exact structure within the nodes of a hidden layer? [closed]

I've been reading on neural networks, but for me, seems like the easiest way for me to learn is seeing some code. I am curious about what is the exact structure within a node of a hidden layer and ...
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1answer
43 views

What is the equation of the separation line for this neuron with identity activation?

I have a single neuron with 2 inputs, and identity activation, where f is activation function and u is output: $u = f(w_1x_1 + ...
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0answers
15 views

Regional specialization in neural networks (especially for language processing)?

What is the status of the research on regional specialization of the artificial neural networks? Biology knows that such specialization exists in the brain and it is very important for the functioning ...
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0answers
30 views

Does a varying ANN model accuracy mean underfitting or overfitting?

Background: This is for a simulated robot with four legs, walking on a flat terrain. The ANN (an MLP) is given inputs as the robot's body angle, positions and angle of each leg with respect to the ...
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0answers
170 views

Is it possible to create intelligence with metaprogramming?

The computer is made up of NAND conditional statements. And "conditional statements that read and write conditional statements" must exist. For example "Modify conditional statement 2 if conditional ...
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0answers
26 views

Since there are different types of neurons in adjacent positions in the brain's arrays, should heterogeneous layers be developed?

Below is a taxonomy of neurons. Some of these types occur in different locations in the brain, but there are adjacent neurons of varying types with clearly functional type diversity in many parts of ...
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0answers
32 views

neuralnetworksanddeeplearning.com chapter 5 problems

For http://neuralnetworksanddeeplearning.com/chap5.html , could anyone suggest: 1) how to approach the derivation of expression (123) ? 2) what constitutes value ~ 0.45 ? 3) why the need of taylor ...
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0answers
47 views

How does the degree of neuronal realism affect computing in a deep learning scenario?

Neurons can be simulated using different models that vary in the degree of biophysical realism. When designing an artificial neuronal network, I am interested in the consequences of choosing a degree ...
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0answers
216 views

Feature set out of grayscale Images for training a neural network?

Previously I had trained a Neural Networkupon 20,000 character images. This Neural Net generally works well, it uses ...
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0answers
32 views

The ANN is based on cognitrons

I'm trying to understand how to build the ANN on cognitrons, so I have read theory for that topic and found the scheme: As I got neurons are subdivided in two classes: the exciting and the inhibitory....
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1answer
76 views

Text Capturing on the Images

I want to capture text and letters on images (png, jpeg, etc.). Is it Possible Which algorithm/software can I use? Right now I am using R with the ...
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1answer
82 views

debugging perceptron for digital AND circuit

I was trying to code a single layer perceptron to understand binary AND: 1 1 1 0 1 0 1 0 0 0 0 0 I made up this code ...
0
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2answers
111 views

Building an AI that generates text by itself

Now I know this might break some StackExchange rules and I am definitely open for taking the thread down if it does! I am trying to build an AI that can write it's own book and I have no idea where to ...
0
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1answer
57 views

Decide Number of input Parameters and Output Parameters - ANN

I have to create a Neural Network for regression purpose. Basically, I created a Model which predict next 5 values when we give past 6 values. I want to make a change in this neural network. For ...
0
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1answer
101 views

What is the concept of training a neural network?

I was trying to build an OCR system and heard about ANNs. I am weak at mathematics and statistics and couldn't stick up to reading those massive mathematical documents (research papers or ANN related ...
0
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1answer
57 views

Basic Functions and Results

If the number of input neurons and output neurons doesn't change, what will change if I have one hidden layer, but first with 1 neuron, then with 4 neurons? Taking into consideration the fact that ...
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0answers
14 views

Why doesn't a neuron activation depend on number of input (presynaptic) neurons?

In an artificial neural network, we usually use the same activation function for all neurons, independently of the number of input (presynaptic) neurons. However, usually, the number of input neurons ...
0
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0answers
27 views

Why is the sigmoid function interpreted as a saturating firing rate of a neuron?

I've seen several people say that sigmoids are like a saturating firing rate of a neuron but I don't see how or why they interpret it as such. I especially don't see the relationship between a "...
0
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1answer
42 views

If neurons performed the operation of an entire layer, would that make the neural network more effective?

(I have a very primitive understanding of neural networks, so please forgive the lack of technicality here.) I am used to seeing a neuron in a neural network as something that- Takes the inputs and ...