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.

Filter by
Sorted by
Tagged with
0 votes
0 answers
62 views

What are the advantages and disadvantages of higher order neuron activation functions?

I've been reading about different types of neurons that the traditional linear one. One example I came across is the Sigma-Pi neuron, where the activation function includes higher order terms, such as ...
user avatar
0 votes
1 answer
41 views

Why do we use a weighted sum in an artificial neuron instead of another more complex function?

I have just started learning about NN and DL and I wanted to know if there is a theoretical reason we use a weighted sum for all the inputs in an artificial neuron? So for example if we have a neuron ...
user avatar
  • 1
1 vote
1 answer
163 views

Why do neural network weights have to be between 0 and 1?

I've been reading about neural networks for a long time, and I saw that in each one, the weights are always between 0 and 1. Why is this? I tried programming one, but the sigmoid function just seemed ...
user avatar
0 votes
1 answer
19 views

Multi-class classification but a single feature sometimes boils it down to a binary-classification

I have a three-class classification problem for a large dataset. Classes are 0, 1, and 2. There's a categorical variable in my feature vectors such that when a sample point has this variable positive, ...
user avatar
  • 1
0 votes
0 answers
7 views

Are there any connectionist parametric models with non-neuron building blocks?

Parametric models allows learning by converging to the desired parameters, which are randomly initialized initially. Among the parametric models, especially in connectionist AI, neural networks are ...
user avatar
  • 3,109
0 votes
0 answers
15 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 ...
user avatar
2 votes
3 answers
260 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 ...
user avatar
2 votes
1 answer
53 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 ...
user avatar
1 vote
0 answers
63 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 ...
user avatar
0 votes
0 answers
53 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 "...
user avatar
  • 509
0 votes
1 answer
59 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 ...
user avatar
  • 111
1 vote
1 answer
43 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 ...
user avatar
5 votes
2 answers
612 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 ...
user avatar
1 vote
1 answer
62 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 + ...
user avatar
  • 1,183
1 vote
1 answer
73 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 ...
user avatar
1 vote
1 answer
58 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 ...
user avatar
  • 131
1 vote
0 answers
16 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 ...
user avatar
  • 773
5 votes
1 answer
81 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)?
user avatar
4 votes
1 answer
248 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 ...
user avatar
1 vote
0 answers
34 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 ...
user avatar
0 votes
2 answers
183 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 ...
user avatar
  • 151
2 votes
1 answer
51 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 ...
user avatar
0 votes
2 answers
69 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: ...
user avatar
  • 1,183
1 vote
1 answer
2k views

Is "dataset size" and "model size" same thing? [closed]

I mean what is determine my model size, connection amount between layers and neurons, or size of my dataset?
user avatar
  • 21
4 votes
2 answers
146 views

In a neural network, by how much does the number of neurons typically vary from layer to layer?

In a neural network, by how much does the number of neurons typically vary from layer to layer? Note that I am NOT asking how to find the optimal number of neurons per layer. As a hardware design ...
user avatar
3 votes
2 answers
179 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, ...
user avatar
0 votes
1 answer
75 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 ...
user avatar
  • 183
1 vote
2 answers
286 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 ...
user avatar
1 vote
1 answer
47 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 ...
user avatar
2 votes
1 answer
66 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 ...
user avatar
3 votes
2 answers
199 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 ...
user avatar
1 vote
1 answer
190 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 ...
user avatar
1 vote
0 answers
33 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 ...
user avatar
  • 111
0 votes
1 answer
58 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 ...
user avatar
  • 21
1 vote
0 answers
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 ...
user avatar
  • 127
7 votes
3 answers
186 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 ...
user avatar
  • 773
11 votes
2 answers
341 views

What does it mean for a neuron in a neural network to be activated?

I just stumbled upon the concept of neuron coverage, which is the ratio of activated neurons and total neurons in a neural network. But what does it mean for a neuron to be "activated"? I know what ...
user avatar
  • 163
8 votes
3 answers
660 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, ...
user avatar
  • 223
4 votes
1 answer
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 ...
user avatar
  • 43
1 vote
0 answers
246 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 ...
user avatar
9 votes
2 answers
371 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 ...
user avatar
  • 281
1 vote
1 answer
360 views

What is the minimum number of neurons and hidden layers needed to learn a Boolean function that maps $N$ bits to $1$ bit?

Suppose I have a Boolean function that maps $N$ bits to $1$ bit. If I understand correctly, this function will have $2^{2^N}$ possible configurations of its truth table. What is the minimum number of ...
user avatar
  • 119
1 vote
2 answers
79 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 ...
user avatar
2 votes
3 answers
143 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 ...
user avatar
1 vote
0 answers
35 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....
user avatar
1 vote
1 answer
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 ...
user avatar
1 vote
2 answers
269 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 ...
user avatar
  • 311
4 votes
1 answer
278 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 ...
user avatar
0 votes
1 answer
94 views

debugging perceptron for digital AND circuit [closed]

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 ...
user avatar
  • 29
6 votes
1 answer
347 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 ...
user avatar