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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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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0answers
42 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 ...
3
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2answers
82 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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1answer
26 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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1answer
32 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 ...
1
vote
1answer
33 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 ...
2
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1answer
56 views
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?
1
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1answer
40 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 ...
2
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4answers
84 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 ...
2
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1answer
57 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 ...
3
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2answers
107 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 ...
1
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1answer
141 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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0answers
16 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
25 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 ...
1
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1answer
38 views
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 ...
1
vote
0answers
43 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 ...
8
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3answers
121 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 ...
6
votes
3answers
269 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 ...
5
votes
2answers
188 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 (...
8
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1answer
1k views
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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0answers
42 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 ...
4
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1answer
152 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 ...
0
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2answers
65 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 ...
0
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1answer
252 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 ...
3
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4answers
113 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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0answers
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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1answer
68 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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2answers
210 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 ...
5
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1answer
234 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 ...
3
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2answers
82 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, ...
1
vote
1answer
73 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
...
9
votes
2answers
312 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 ...
-1
votes
1answer
93 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 ...
2
votes
1answer
39 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 ...
2
votes
1answer
73 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 ...
6
votes
1answer
312 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 ...
6
votes
4answers
187 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 ...
