http://groupkos.com/dev/index.php?action=history&feed=atom&title=MindSpeak.NeuralNet.Biological 2026-04-15T18:42:20Z Revision history for this page on the wiki MediaWiki 1.39.3 http://groupkos.com/dev/index.php?title=MindSpeak.NeuralNet.Biological&diff=4542&oldid=prev 2024-11-07T01:52:12Z

<p>Created page with &quot;{{menuMindSpeak}} == <a href="/dev/index.php/MindSpeak.NeuralNet.Biological" title="MindSpeak.NeuralNet.Biological">MindSpeak.NeuralNet.Biological</a> - Biological Brain == A NeuralNet component representing a biological neural network, specifically designed to model and interact with human brains or other organic neurological systems. It inherits the properties and behaviors of its parent type <a href="/dev/index.php/NeuralNet" title="NeuralNet">Neural Network</a>. === Properties === * &lt;big&gt;&#039;&#039;&#039;patterns&#039;&#039;&#039;&lt;/big&gt;: An array of floating-point numbers representing neuron activation levels in the biologica...&quot;</p> <p><b>New page</b></p><div>{{menuMindSpeak}}<br /> <br /> == [[MindSpeak.NeuralNet.Biological]] - Biological Brain ==<br /> A NeuralNet component representing a biological neural network, specifically designed to model and interact with human brains or other organic neurological systems. It inherits the properties and behaviors of its parent type [[NeuralNet|Neural Network]].<br /> <br /> <br /> === Properties ===<br /> <br /> * &lt;big&gt;&#039;&#039;&#039;patterns&#039;&#039;&#039;&lt;/big&gt;: An array of floating-point numbers representing neuron activation levels in the biological brain.&lt;br/&gt;<br /> * &lt;big&gt;&#039;&#039;&#039;size&#039;&#039;&#039;&lt;/big&gt;: The number of neurons in the biological brain (derived from patterns length).&lt;br/&gt;<br /> * &lt;big&gt;&#039;&#039;&#039;weightMap&#039;&#039;&#039;&lt;/big&gt;: A 2D matrix storing synaptic weights, connecting each pair of neurons. Defaults to random values between 0 and 1.&lt;br/&gt;<br /> <br /> === Methods ===<br /> <br /> * .activate(pattern): Activates a specific neuron(s) based on input pattern.<br /> * .learn(learningRate = 0.1): Updates weightMap using Hebbian learning rule to improve synaptic connections.<br /> * .remember(inputPattern, outputPattern): Associates an input pattern with an output pattern by modifying weightMap.</div>

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