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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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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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)?
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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 ...
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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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53 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 ...
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1answer
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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 ...
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24 views

Is it still called linear separation with a layer of more than 1 neuron

A single neuron will be able to do linear separation. For example, XOR simulator network: ...
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1answer
30 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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Not able to properly tune Neural Network via Back Propagation properly

I have a custom code Neural Network(not using keras or any package...Trying to learn the essence of Neural Network from scratch)... Code can be found here I have the per iteration training output(<...
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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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2answers
145 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, ...
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30 views

The Nature of Model Weights for Targeted Dropout

I am trying to figure out how to target certain model weights withtin my 1000x 1000 feed forward network in keras ...
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35 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 ...
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1answer
45 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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What are neural networks and how do they relate to AI?

Artificial neural networks (ANN) are computing systems vaguely inspired by the biological neural networks that constitute animal brains, how do they relate to AI?
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1answer
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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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4answers
133 views

How to decide which model have to use? Simple vs Complex

I have come across the question simple model vs complex model. How to decide which one have to use? and one more question connect to this. How to decide which is a ...
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1answer
58 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 ...
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128 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 ...
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1answer
154 views

Would this formula be relevant to the field of A.I?

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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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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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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1answer
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Neurological interpretation of LSTMs

I am searching for an Interpretation of LSTMs and recurrent neural Networks within Cognitive Neuroscience. Is there a mechanism in the human brain, that works analog to LSTMs? How does Long-term ...
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1answer
52 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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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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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 ...
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337 views

How to model inhibitory synapses in the artificial neuron? [Biological inspiration]

In the brain some synapses are stimulating and some inhibiting. ReLu erases that property to only stimulating once, since in the brain inhibition doesn't mean 0 output, but more precisely - negative ...
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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 (...
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1answer
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Meaning of evaluation metrics in Tensorflow

I am pretty much a beginner in Tensorflow and simply follow a tutorial. There is no problem with my code, but I have a question regarding the output ...
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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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1answer
189 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 ...
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2answers
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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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1answer
309 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 ...
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4answers
117 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 ...
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26 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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70 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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225 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 ...
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239 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 ...
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85 views

Different Suggestion for Estimating Number of layers in Neural Network

In this note Justin Domke says that In practice, neural networks seem to usually find a reasonable solution when the number of layers is not too large, but find poor solutions when using more than, ...
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1answer
77 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 ...
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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 ...
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1answer
95 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 ...
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1answer
42 views

Hardware immutability and sentience

Hardware comes in two forms, basically: immutable, such as RAM, and mutable, such as FPGAs. In animals, neurological connections gain in strength by changing the physical structure of the brain. This ...
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1answer
86 views

Backpropagation in Decoupled Neural Interfaces

I am attempting to create a fully decoupled feed-forward neural network by using decoupled neural interfaces as explained in the paper (https://arxiv.org/abs/1608.05343). As in the paper, the DNI is ...
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318 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 ...
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191 views

Which ANN 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 ...