The Synthetic Neural Substrate (SNS) is designed to function as the core processing unit of an AI system, simulating a brain-like structure. It processes inputs, generates outputs, and maintains complex internal representations influenced by synthetic "neurotransmitters." This allows the system to exhibit dynamic and adaptive behavior, similar to emotional and cognitive modulation in biological systems.
- Layered Structure: The SNS consists of an input layer, a hidden layer, and an output layer. The hidden layer includes a recursive feedback loop that simulates brain-like recurrent neural connections, allowing the system to reinforce its neural processing.
- Activation Function: The
ReLU(Rectified Linear Unit) is used to introduce non-linearity, enabling the network to learn complex data representations. - Digital Neurotransmitters:
- Dopamine: Modulates interest and motivation levels.
- Serotonin: Influences calmness and stability.
- Norepinephrine: Affects alertness and stress levels. These neurotransmitters adjust the network's processing, creating variable internal states that affect the AI’s output.
- Input Handling: The input layer receives and processes incoming data.
- Recursive Feedback: The hidden layer processes the data multiple times through a feedback loop, reinforcing neural activity.
- Neurotransmitter Modulation: The output from the hidden layer is modulated based on neurotransmitter levels, influencing how the data is processed and the response generated.
- Output Generation: The output layer produces the final response after all modulations and recursive processing.
The SNS can update its neurotransmitter levels to simulate different internal states, allowing the AI to react differently based on simulated emotions or motivational drives. This is essential for applications where dynamic and adaptive responses are required.
The Qualia Generation Module (QGM) is designed to create subjective experiences within the AI by modulating the SNS's internal state. By adjusting the digital neurotransmitter levels in response to specific moods, the QGM simulates states that resemble human emotional and cognitive responses.
- Mood Mapping: The QGM maps different moods (e.g.,
calm,alert,excited) to specific neurotransmitter levels. This mapping allows the system to adjust neurotransmitter levels dynamically to evoke a simulated state that aligns with a particular mood. - Integration with SNS: The QGM interacts with the SNS by updating the neurotransmitter levels based on the current mood, influencing the way data is processed and experienced by the AI.
- Mood Simulation: The QGM takes a specified mood and updates the SNS with the corresponding neurotransmitter levels.
- Dynamic State Adjustment: This allows the AI to shift between different internal states, creating a more nuanced response to external inputs and interactions.
- Human-Like Reactions: By simulating various emotional states, the QGM enables the AI to exhibit behaviors that appear more relatable and human-like.
The integration of the QGM with the SNS makes it possible for the AI to respond in a way that mimics emotional context. This can be useful in applications requiring empathy, nuanced interactions, or adaptive decision-making.
- Initialization: The SNS and QGM are initialized, defining the structure and linking them together.
- Qualia Simulation: The QGM simulates a chosen mood (e.g.,
excited), which updates the SNS’s neurotransmitter levels. - Input Processing: A sample input is processed by the SNS, and the output is modulated based on the simulated internal state influenced by the QGM.
The SNS and QGM together form a foundation for an AI architecture that simulates aspects of subjective experience and adaptive behavior. By incorporating synthetic neurotransmitters and mood mapping, the system is able to dynamically alter its processing to reflect different emotional states, providing a more versatile and human-like interaction model.