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https://github.com/maael/hedonistichebbianneuralnetwork

A Hedonistic Hebbian Neural Network in python.
https://github.com/maael/hedonistichebbianneuralnetwork

artificial-life artificial-neural-networks hebbian-learning life neural-network neurons python

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A Hedonistic Hebbian Neural Network in python.

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# Hedonistic Hebbian Neural Network
[![Build Status](https://img.shields.io/travis/maael/HedonisticHebbianNeuralNetwork.svg?style=flat-square)](https://travis-ci.org/maael/HedonisticHebbianNeuralNetwork)

A Hedonistic Hebbian Neural Network in python.

[Neurons Just Want To Have Fun.](https://youtu.be/PIb6AZdTr-A)

Hedonistic aspects inspired by [Brain Function and Adaptive Systems A Heterostatic Theory A. HARRY KLOPF](https://scholar.google.co.uk/scholar?cluster=4995640381579273145&hl=en&as_sdt=0,5&sciodt=0,5).

Klopf proposes that life (fundamentally neurons, which then floats up to mean life in general as neurons control living things actions), rather than trying to achieve a homeostatic state and staying alive, instead primarily looks to maximise some variable to achieve heterostatic state, and it just so happens that the homeostatic state of survival is a step towards such a heterostatic state. In short, Klopf suggests our neurons, and so us, are just trying to maximise pleasure and minimise pain.

Hebbian Learning is inspired by [Donald Hebb - The Organisation of Behaviour 1949](https://books.google.co.uk/books?hl=en&lr=&id=uyV5AgAAQBAJ&oi=fnd&pg=PP1&dq=Donald+Hebb+-+The+organisation+of+behaviour+1949&ots=mItXytHOYn&sig=CWt900aQhilFQg6dnG7el7URsGc#v=onepage&q=Donald%20Hebb%20-%20The%20organisation%20of%20behaviour%201949&f=false).

The three core postulates that Hebb proposes are nicely summarised in this [youtube video by Soma Barsen and Dr. Steven J. Barnes](https://youtu.be/xKYMQ_o5Xik). One of the key ideas used from Hebb's postulates is a basic idea from his second postulate, the Hebbian Cell Assembly, paraphrased as -

> Cells that fire together, wire together.

## Install
```
python install.py
```

## Test
```
pytest
```