AI News, Artificial Intelligence/Neural Networks/Natural Neural Networks
- On Sunday, September 30, 2018
- By Read More
Artificial Intelligence/Neural Networks/Natural Neural Networks
The primary difference between a natural neural network and a Distributed Processing analog of a Neural Network, is the attempt to capture the function of a real neuron and real natural arrangements of neurons in the model.
From the Hebbian model used in early perceptrons to modern neural models that attempt to capture the biochemical threads that implement different forms of memory within the same cell, The idea has all along been to find a reasonable model for the neuron, and learn from implementations of that model knowledge of how natural neural systems might work.
It has been a long hard road, and while neural networks have gained and lost prominence in A.I., Neuro-scientists have been forced to go back to the Neural Network model, time and time again, as the most ethical approach to learning about natural networks of neurons.
Unlike other forms of Neuroscience, Neural models do not kill animals, in order to get their neurons, they do not torture animals in order to see how they will react, they do not even involve real animals, instead they torture recyclable electrons by making them flow through computer circuits.
To add to this rule however we must take into account the fact that new models of neural systems, incorporate learning threads that operate in parallel and implement short term, long term and perhaps even medium term memories.
In fact small networks of neurons connect to larger networks of neurons, forming a network throughout the whole body, with centers that process specific types of information in a number of centers of the body.
If we are going to deal with a system of the complexity of the brain we need new neural network models that can capture the variety in the structure of neurons, can explain the functions of Groups of different types of neurons, and explain why some similar neurons act as if they were a single solid group, where only one or two neurons fire for the whole group.
- On Friday, February 21, 2020
10.4: Neural Networks: Multilayer Perceptron Part 1 - The Nature of Code
In this video, I move beyond the Simple Perceptron and discuss what happens when you build multiple layers of interconnected perceptrons ("fully-connected ...
Neural network tutorial: The back-propagation algorithm (Part 1)
In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but ...
Soft Computing Lecture Adaline Neural Network
Soft Computing Lecture Adaline Neural Network Adaline is when unit with linear activation function are called linear units a network with a single linear unit is ...
12a: Neural Nets
NOTE: These videos were recorded in Fall 2015 to update the Neural Nets portion of the class. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete ...
Artificial Neural Networks Explained | Why Error Optimization is Used (Part 1)
Link to the 2nd part of this video : Error Optimization is one of the most important part of ..
Neural Networks Demystified [Part 4: Backpropagation]
Backpropagation as simple as possible, but no simpler. Perhaps the most misunderstood part of neural networks, Backpropagation of errors is the key step that ...
Using Artificial Neural Networks to Model Complex Processes in MATLAB
In this video lecture, we use MATLAB's Neural Network Toolbox to show how a feedforward Three Layer Perceptron (Neural Network) can be used to model ...
002 Simple neural network logical AND table
Also SUBSCRIBE to my new Channel: Best deals on SmartPhone OnePlus 3T (Midnight ..
Lecture 3.1 — Learning the weights of a linear neuron [Neural Networks for Machine Learning]
For cool updates on AI research, follow me at Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey ..
Neural Networks 4: McCulloch & Pitts neuron