Let's delve deeper into the AGILib! Artificial General Intelligence library (AGILib) is a modular neural network library in Python, dedicated to developing a platform for general AI. By offering unique out-of-the-box solutions, AGILib encourages research, exploration, and collaboration in AI creation. The project emphasizes transparency, using clear and understandable Python code to avoid the "black box" paradigm, thus promoting research and a deeper understanding of the underlying algorithms.
Simultaneously, AGILib focuses on developing solutions that are optimized for modern hardware, such as graphics accelerators, and compatible with cutting-edge libraries. This ensures that the platform is suitable for real and complex projects while fostering an environment that promotes innovation in general artificial intelligence.
AGILib's main goal is to develop a versatile platform that enables the creation of artificial general intelligence through a set of modular solutions and libraries.
At the core of this library is the idea that neuroplasticity and predictability can co-exist in harmony. By integrating these two seemingly opposing traits, we can achieve an efficient system that is both stable and adaptable. The efficiency of the system, stability, adaptability, and security through the management and Full Control of plasticity and the learning process are essential attributes that are carefully considered in this library.
This library represents more than a merely ambitious project; it signifies a bold stride towards the realization of practical, artificial general intelligence. By cultivating a profound understanding of neural systems and offering sophisticated tools for their precise simulation, the library sets its sights on securely integrating potent artificial intelligence into our global society.
Research and Exploration* The project emphasizes understanding neural network processes and fosters an environment for innovation by offering a powerful research platform for experimentation with various components and architectures. AGILib encourages not only the improvement of existing principles but also the creation of new ones, paving the way for advancements in the field of artificial intelligence.
Researchers, developers, and enthusiasts are invited to participate in the creation of a versatile and dynamic AI platform, contributing to the advancement of AI systems that are both comprehensible and optimized for performance.
🤖😃⚙️Lib.
#1 STRUCTURE:
AGILib/
├── snn/
├── neurotransmitters.py
├── class BaseTransmitter
├── class TransmitterX(BaseTransmitter)
└── ...
├── synapses.py
├── class PreSynapse
├── class PostSynapse
└── ...
├── dendrites.py
├── class BaseDendrit
├── class DendritX(BaseDendrit)
└── ...
├── membranes.py
├── class BaseMembrane
├── class MembraneX(BaseMembrane)
└── ...
├── cores.py
├── class BaseCore
│ ├── self.nucleus
│ └── ...
├── class CoreX(BaseCore)
└── ...
├── axons.py
├── class BaseAxon
├── class AxonX(BaseAxon)
│ └── ...
└── ...
├── neurons.py
├── class BaseNeuron
├── class NeuronLIF(BaseNeuron)
└── ...
├── layers.py
├── class BaseLayer
│ └──def feedforward()
├── class Layer1(BaseLayer)
└── ...
└── networks.py
├── class BaseNet
├── class NetVisualCortex(BaseNet)
└── ...
├── learning_rules/
└── ...
├── utils/
└── ...
├── tests/
└── ...
├── examples/
└── ...
├── LICENSE
└── README.md
#2 EXPLANATION
The Spiking Neural Network Architecture (snn/) Within the snn/ directory, we delve into the hierarchical design of the Spiking Neural Network, where each component serves a specific role and seamlessly integrates into the larger system. The Python files within this directory act as building blocks, nesting within one another to constitute the complete network.
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neurotransmitters.py:
The BaseTransmitter class in this file represents our neurotransmitters. These are our neural network's messengers, each having unique impacts on neurons. They influence aspects like leakage rate, excitatory and inhibitory actions, delays, action potential threshold, long-term potentiation and depression, reactivity, neuro cycle frequency, and learning level. This system is crucial for maintaining stability and preventing over-excitation or attenuation of the neural network.
Within this file, different neurotransmitters are defined: Acetylcholine (generally excitatory, crucial for memory and learning), Dopamine (involved in reward and pleasure systems), Serotonin (impacts mood, hunger, sleep), and GABA (primary inhibitory neurotransmitter).
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synapses.py:
The synapses.py file manages the crucial process of signal exchange and the release of neurotransmitters within the neural environment. It is within this file that we define the behavior of synapses, which are pivotal in the functionality of the spiking neural network.
BaseSynaps Class: This foundational class encapsulates the essential attributes and operations of a synapse. One of its key features is the ability to adjust its response to frequent signals, effectively acting like a 'fatigue' function. This response is regulated by the organism's genome and influenced by learning processes, making each synapse dynamic in its performance. It also mimics the natural cycle of neurotransmitter depletion and recovery, thereby affecting signal strength. It's worth noting that these functionalities can be influenced by external environmental factors, adding an extra layer of realism to the simulation.
It's a powerful module that adds depth and complexity to our spiking neural network by accurately simulating the dynamic nature of synapses.
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dendrites.py:
The dendrites.py file supervises the connections within the neural network. It emulates the behavior of dendrites, which are the structures in a neuron responsible for receiving signals from other neurons.
BaseDendrite Class: The central class in this module, BaseDendrite, encapsulates the fundamental characteristics and operations of a dendrite. It maintains the "channel width" of signal reception and also has the ability to self-regulate. When the weight of its connections continuously drops to zero, it can discontinue itself, simulating the process of synaptic pruning observed in biological neural systems. In terms of its effect on the neural membrane, it can either be inhibitory, reducing the likelihood of neuron firing, or excitatory, increasing the neuron's firing potential. (---ax---) The capabilities and behaviors modeled within the dendrites.py file further the spiking neural network's ability to reflect the dynamic, ever-changing nature of biological neural networks.
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membranes.py:
The membranes.py file plays an essential role in our network model by simulating the functionality of neuronal membranes. This responsibility is encapsulated within the BaseMembrane class.
The neuronal membrane's primary role in our model is to aggregate incoming signals into a potential and simulate the natural leakage of electrical charge. In an actual neuron, the cell membrane serves as a barrier that selectively allows ions to pass in and out, creating an electrical charge across the membrane. Over time, this charge can naturally dissipate, or "leak", a process our model simulates. This leakage serves as a critical stabilizing factor, preventing the over-excitation of the network and mirroring a biological neuron's inherent mechanism to avoid continuous auto firing.
The BaseMembrane class also demonstrates dynamic sensitivity and responsiveness to neurotransmitters. These properties, directly influenced by the neurotransmitters themselves, help modulate the neuron's reaction to stimuli of varying intensities.
Furthermore, this class can adapt its response to different neurotransmitters over time, including the ability to grow more dendrites. This adaptability is an essential feature mirroring the biological process of neuroplasticity, allowing neurons in the network to learn and decode different transmitter codes.
When a new neuron is created, its initial characteristics, including sensitivity, response speed, and the ability to learn, are established by the cell nucleus's genome. This reflects the genetic influence on biological neuron development.
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cores.py:
The cores.py file is fundamental as it holds the BaseCore class, serving as the 'command center' of our neurons. It encompasses the nucleus and the peripheral controls, thereby orchestrating key functions.
Inside the nucleus, the genome, a central component of the BaseCore class, is stored. The genome plays a pivotal role in setting various parameters when a new neuron is birthed. These parameters include the neuron's lifespan, the speed at which potential leaks, delay timings, the number and type of dendrites, the method for forming dendrite connections, and the settings for synapses.
Furthermore, the genome manages the action potential, a neuron's core function. This responsibility includes logging the learning process for future genome editing in subsequent epochs.
In a striking resemblance to biological neurons, the genome within the nucleus of our model neuron is also capable of mutations. It has the ability to alter its genetic code during the neuron's lifetime, promoting adaptability and learning in the neural network.
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axons.py:
The BaseAxon class, described in the axons.py file, has the responsibility of managing signal transfer and overseeing growth for optimized interaction with other neurons.
This class goes beyond what is typically found in traditional neural networks due to its multiple synaptic connections, each potentially featuring different neurotransmitters. It's also capable of synthesizing various neurotransmitters within a single synapse, enabling a gradual transition from one neurotransmitter to another as the network learns and adapts.
An additional notable feature of the BaseAxon class is its strategic growth. As it expands, the axon autonomously searches for an optimal location within the address space of the neural network. This strategic positioning ensures effective interaction with other neurons and contributes to the overall productivity of the network. This dynamic, adaptive behavior reflects the ongoing growth and evolution seen in biological neural networks.
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neurons.py:
The neurons.py file in our system is where we define a variety of neuron types for use in our Spiking Neural Network. This is where the fundamental BaseNeuron class and the specific LIFNeuron (Leaky Integrate-and-Fire) class are defined, among others.
One of the key strengths of our system is its modularity and versatility. By employing the previous components such as the nucleus, membrane, dendrites, and axon modules, we can construct unique and highly customized neurons. The flexibility of our system allows for these varied neuron types to be integrated within a single network, even within a single layer.
The sophisticated control mechanisms in place for the nucleus, membrane, dendrites, and axon, all the way down to the granular tuning of synapses, provide a robust interface for deep tuning of individual neurons. Whether you're developing unique neurons or designing nuanced synaptic interactions, our system provides the tools to create a truly tailored network model.
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layers.py:
In the layers.py file, different types of neurons can be housed within a single layer, mirroring the diverse and complex nature of biological neural systems. This ability to integrate varied neuron types within one layer paves the way for intricate and heterogenous neural environments. Each layer, being self-contained, can independently interact and communicate with other layers, leading to dynamic and complex network behaviors.
The BaseLayer class also introduces the feedforward() method. This function is rooted in the principles of feedforward propagation, providing a mechanism for efficient transmission of signals from one layer to the next in a forward direction. This process ensures a consistent flow of information, mirroring the sequential signal transmission observed in biological neural networks.
Moreover, the modular design adopted in this file encourages architectural flexibility, a critical aspect when attempting to model biological neural networks known for their structural complexity and diversity. By organizing neurons into layers, more extensive and complicated neural systems, like separate areas of the brain, can be efficiently modeled.
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networks.py:
Building upon the key concept of neuroplasticity at the heart of advanced artificial intelligence systems, the networks.py module leverages the intricate design of neurons, synapses, and layers to form an extensive and interconnected neural network model. This module not only represents the culmination of the hierarchical design of the Spiking Neural Network (SNN) but also the cornerstone of a library for building artificial general intelligence.
At the core of this library is the idea that neuroplasticity and predictability can co-exist in harmony. By integrating these two seemingly opposing traits, we can achieve an efficient system that is both stable and adaptable. The efficiency of the system, stability, adaptability, and security through the management of plasticity and the learning process are essential attributes that are carefully considered in this library.
The BaseNet class, along with other specific network models like NetVisualCortex, facilitate efficient network communication and learning by allowing neurons to form new connections, refine existing ones, and prune ineffective links. This mirrors the continuous growth and evolution seen in biological neural networks. By doing so, the library embodies the key aspects of neuroplasticity - the ability to learn, adapt, and change.
A fundamental tenet of the library is its focus on safety and efficiency. By enabling the network to dynamically adjust its learning rate, the library provides a balance between stability and adaptability. This means that the neural system can adapt to new data or maintain predictable behavior, depending on the task at hand. This dual capacity contributes to the safety of the system by providing mechanisms to manage the learning process and control the extent of neuroplasticity.
This library represents more than a merely ambitious project; it signifies a bold stride towards the realization of practical, artificial general intelligence. By cultivating a profound understanding of neural systems and offering sophisticated tools for their precise simulation, the library sets its sights on securely integrating potent artificial intelligence into our global society.
Every Python file is complemented with visual aids to help understand the concepts better.
The 'Brain Trainers' (learning_rules/) The learning_rules/ directory hosts our 'brain trainers'. It includes definitions for learning rules like Spike-Timing Dependent Plasticity (STDP), enabling our neural network to learn and adapt.
Handy Helpers (utils/) The utils/ directory is a treasure chest of utility tools. It contains various functions and classes that assist in tasks such as generating input data like spike trains.
Quality Assurance (tests/) The tests/ directory is our quality control center, ensuring everything is functioning as expected. It contains unit tests for the library, verifying every function and feature.
Show and Tell (examples/) The examples/ directory is our 'show and tell' stage. It hosts scripts showcasing the library's usage in real-world scenarios, giving you a practical sense of what the library can achieve.
Legal and User Guide Finally, we have the LICENSE and README
#3 Guidelines for Developing an Intuitive and User-Friendly AGI Library.
* Consistent API: Aim to provide a consistent and intuitive API across all modules of the library. Each component should have predictable behavior, making the library easy to learn and use.
* Comprehensive Documentation: The documentation should be comprehensive, covering all functions and classes in detail.It should clarify the purpose, inputs, and outputs of each function, supplemented with usage examples where possible.
* Practical Examples: Develop practical examples demonstrating how to use the library effectively. These examples should showcase the capabilities of the library and serve as a starting point for users.
* Modular Design: Design the library with modularity in mind. Separate modules should be created for different aspects like neuron types, network layers, learning rules, etc. This way, users can import and use only the components they need.
* Error Handling & Testing: Incorporate clear and informative error messages to facilitate debugging. Implement a comprehensive suite of tests to ensure the functionality of your library, improving its reliability.