The journey of a neuron | McCulloch Pitts Neuron

💡🎯 In this video, we introduce the biological neuron and its important components, the we explain how it inspired the first artificial neuron popularly known as the McCulloch-Pitts Neuron.📊
🧠 Introduction to Neurons:
A neuron is a specialized cell in the nervous system that processes and transmits information. It has several key components: the soma (cell body), dendrites (receiving inputs), axon (transmitting output), and synapses (connections to other neurons).
⚙️ McCulloch-Pitts Neuron (1943):
In 1943, Warren McCulloch and Walter Pitts proposed the first artificial neuron model. It mimicked the basic functions of a biological neuron, including receiving inputs, aggregating them, and producing an output.
🔄 Input-Output Processing:
The McCulloch-Pitts neuron's input-output processing is simple yet effective. It aggregates the inputs, applies a decision function, and outputs a 1 if the aggregated inputs are greater than or equal to a threshold (theta), otherwise 0.
⛔️ Limitations of McCulloch-Pitts Neuron:
A major limitation was that it could only handle binary inputs and outputs (0 or 1), limiting its ability to process real-valued data effectively.
Another limitation was its lack of assigning relative weights to inputs and the arbitrary choice of the threshold (theta).
💡 Decision Making Example: Shark Tank
Imagine a shark on "Shark Tank" making investment decisions. The shark considers certain factors (inputs) and decides to invest (output 1) or not (output 0), similar to how the McCulloch-Pitts neuron makes binary decisions.
🎯 Conclusion:
The McCulloch-Pitts neuron was a significant step in the development of artificial neural networks, but it had limitations. Future models, like the perceptron, addressed some of these limitations by introducing real-valued inputs and weights.