@BryanKrause I see, i thought there might be tangible answer for example, "fear causes incoherent/abnormal synaptic actuation patterns, which is anomaly and we sense anomaly as negative" just a speculation. Well this also makes me hypothesize that signals firing that are coherent could be sensed as positivity/reward, we associate beauty with say symmetry face so signals are quite coherent when you experience symmetric face and we associate that to positive/reward. Again just a far fetched speculation.

@user438383 but I am looking for associative answer, so psychology will only provide higher lever behavioral response instead of lower level response based on synaptic aggregation

@PolypipeWrangler Good point. But perhaps when we plant a seed, we might not have a visual evidence from the plant itself so a visual inspection of soil itself might be required. I am looking to see if algorithms for computer vision could be extended to assist and encourage planting.

was richard nixon frozen in time?

@BryanKrause acknowledge. Thanks for the advice.

@BryanKrause Do we know what exactly are these so-called things that brains optimize? Is there any model of such object. All I learned is that these synapse aggregate in similar fashion as neural nets, you are right that distance in hardware in silicon doesnt matter but we do utilize the notion of distance (more precisely inner product /norm) in the computational model which simulates the nearness of the entities/object being learn just like in the brain...so functionality is mimicked by computational model itself.

@BryanKrause makes so much sense. Is there any mathematical model or even a general model of this. I want to see how exactly "pattern of activity" simulate a function and why our NN computational models are so bad and less robust against computational model of brain.

@John alright i understood your point after reading the answer below :) so overtime memory shapes the 'function' that gives rises to emotion and it changes. So this function itself is plastic in nature. I somehow wanted to separate these so-called learned functions from 'data' and determine how much is function and how much is data so a function would be the brains 'capability' to recognize, and data is the specific object itself. So function is more or less genetically motivated "someone likes blue color while someone else likes red...". I wish I could have framed my question properly.

So for an analogy we can use evalutionary algorithms to shape hidden layers of artificial neural network, once we reach a satisfactory state, we get a function that although has been shaped by evolutionary algorithm (which relies on memory), is still a function and not memory, when this function is later used to train on some data, say image recognition, we acquire weights and biases which can be considered as memory as weights and biases may differ if one has to recognize certain person or a cat.

This is the answer I was looking for but I still need some clarification. I am interested in the reference for the codification of the visual stimuli that are going to be transmitted to the brain is done by a network wiring that is somehow shaped by previous activity Now I really want to separate out the evolutionary part from functionality part, the functionality of our brain is indeed encoded by external stimuli over the course of the evolution but "given the current state" of the encoded brain, is there anyway to know how much of the brain is memory and how much function?

@John its quite hard for me to reason that how is cognition and emotion are memories. Cognition is related to attention and learning endavour, which utilizes memories to keep track of the learned stuff but how come they themselves are memories?