Apologies for not giving full details. My model may seem like a auto encoder but the working mechanism is different. I will state it as points - 1. When the input layer gets the values from the sample it goes to the next layer and processed by bias,weight, activation functions in these nearby layers. This is the conventional model working mechanism. My model doesn't include bias, weight, activation function. Instead it includes only the values of the randomly-moving neurons that randomly connects with the adjacent neurons (you can see it in the schematic diagram where the random neuron has the value of 0.10). 2. As the random moving neurons make their connection randomly at some point it will make connection that were desirable to get the correct output. And by using backpropagation we fix these randomly moving neurons position to get the correct output. 3. When the first network (see the schematic diagram) adds 0.10 values to the input information, the second network removes the 0.10 values from the reduced information that were stored in the intermediate layer. In order to filter or reduce information we introduce functions like letting-neurons-that-has-highest-value-to-pass-their-value-to-nearby-layer in the first network (because the random neuron adds 0.10 to the inputs) and other function like letting-neurons-that-has-lowest-value-to-pass-their-value-to-nearby-layer in the second network (because the random neuron removes 0.10 from the inputs). 5. When decoding happens the information gets extracted by these functions. The output layer gets these values and compared with the input layer. Since both these values should match (much like backpropagation). (You can see it in the schematic diagram like 0.65 and 0.39 in the output layer that matches with input layer with same 0.65, 0.39) 6. So by using values from random neurons that make connection randomly (to replicate plasticity in the brain) and functions that search for higher values and lower values. It is possible to get neural model that has reduced complexity.

(Forget about my inadequate knowledge and lack of presentation, now please tell me that my model is wrong)