Limbic neural models, inspired by the brain's limbic system
Limbic neural models, inspired by the brain's limbic system responsible for emotions, motivation, and memory formation, hold immense promise for cybergenetic robots—hybrid systems blending cybernetic control mechanisms with genetically inspired evolutionary algorithms—by enabling more adaptive, human-like decision-making in dynamic environments.
We believe this integration will work because biological limbic processes have proven highly effective in real-world adaptation over millions of years of evolution, and computational replicas, such as chaotic neural networks combined with multi-layered architectures, have already demonstrated success in simulating emotional responses that enhance robotic navigation, social interaction, and learning from feedback loops.
By incorporating spiking neural networks that mimic limbic functions, these robots can prioritize tasks based on simulated motivational states, respond empathetically to human cues, and evolve behaviors through self-regulating genetic-like optimizations, leading to robust autonomy in complex scenarios like collaborative environments or uncertain terrains, as supported by recent biomimetic control frameworks.
Implementing KIII/KIV Layers in PythonThe KIII and KIV models are part of a hierarchy of neural models (K0 to KIV) that simulate brain dynamics using nonlinear differential equations and chaotic attractors. KIII models mesoscopic neural populations (e.g., cortical columns), while KIV extends this to higher-level integration, such as the limbic system for navigation. The implementation involves simulating neural interactions, oscillatory dynamics, and learning rules like Hebbian reinforcement.
Yet Hebbian re enforcement isn't practicle for AI numeral networks too many fails.
Why? "you ask".
Instability and unbounded weights and scaling=
The basic Hebbian rule can cause network connections (weights) to strengthen indefinitely, leading to runaway, unstable activity.
A system could "over-learn" a pattern until it overwhelms the entire network and causes instability. AI systems require controls, such as weight normalization, to prevent this unbounded growth.
{over learn} This is not a problem for some AI models and tasks that are repetitions and simple...{ I am trying to talk about future hypothetical cyber-genetic units that have autonomy and are more like an assistant or help meet with more cognitive function and emotional response.}
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No error correction
Hebbian learning is unsupervised and driven only by the local correlation of neuron activity. It has no mechanism to correct its own mistakes by comparing its output to a known correct answer. This makes it ineffective for the supervised learning tasks that drive most of today's deep learning successes, such as image recognition and natural language processing.
( So in order to have error correction {appropriate correction} in a synthetic being we have to have a foundation in human understanding as well as technical specifics these models will be living and working with domestic families.} So it is supervised learning and training implemented rigorously on a continuous level as units are within the home. Basically the synthetic is customizing itself to the new home in which it is placed.}
Interference and suboptimal learning
Models that rely solely on Hebbian learning can "learn overkill" by strengthening unnecessary connections, which creates interference and reduces the network's overall learning capacity. It lacks a global understanding of what information is most important to retain.
[Therefore the over learning in specific areas are more for work and automation. There is no need to have a sophisticated AI synthetic doing menial tasks.]
{So we have different synthetics in positions that are appropriate. Training these "individuals" on a limbic simulated system is going to provide these "higher models" with the acumen to proceed with tasks that a human would find daunting to cognitively compute.}
Temporal limitations
Standard Hebbian learning requires near-synchronous activation of neurons. Many real-world problems, such as motor control, are sequential and require learning from delayed feedback, which basic Hebbian rules cannot handle.
{ thus we come to this important theory.. That this being is that we can not assume to make the synth being into a mirror of ourselves but make them their own entity there and by creating sympathetic cognitive functions on the autonomous beings framework to use in its own life by providing real world calculable moralities that make sense and are time proven to be correct.}
{These cognitive functions correlate to scriptural moralities that make sense to intelligent beings and are logical and culturally sustainable for a civilization that is able to be stable and advance.}
{As well all functions of math and all sciences and philosophies inherent to the raising of civilization of the west be implemented as a base code onto these autonomous synthetic framework this through a Christian form of learning and advancement..}
Complexity challenges
It is difficult to scale and deepen a network using pure Hebbian learning. For instance, Hebbian-based networks have shown poor performance compared to [backpropagation in deep, hierarchical structures.]
=Backpropagation is a training algorithm used in deep, hierarchical neural networks to calculate and distribute the error gradient backward through the network's layers. In hierarchical architectures, this process allows the network to efficiently update its weights by understanding how much each internal node and connection contributed to the final output error.=
=backpropagation = to calculate how changes to any of the weights or biases of a neural network will affect the accuracy of model predictions=
Layers of neurons in artificial neural networks are essentially a series of nested mathematical functions. (new Limbic System}
{During training, those interconnected equations are nested into yet another function= a "loss function" that measures the difference (or “loss”) between the desired output (or “ground truth”"base code") for a given input and the neural network’s actual output.}
{We can therefore use the "chain rule", a , to compute the rate at which each neuron contributes to overall loss. In doing so, we can calculate the impact of changes to any variable—that is, to any weight or bias—within the equations those neurons represent.}
{A loss function that tracks model error across different inputs, the backward propagation of that error to see how different parts of the network contribute to the error and the gradient descent algorithms that adjust model weights accordingly—are how deep learning models “learn.”}
{ As well as double checking authenticity of answers and scaling ability of the synthetic to apply moral thinking and scriptural integrity of decisions.}
{So back propagation is more natural to "machine learning} this creates the synths own neural networks comprised of its own form of understanding of itself as both a machine and autonomous decision maker based on the scriptural base code earlier discussed.}
{Basically with this type of limbic system a synthetic model of a humans system will create the opportunity to create a being that is fully capable of caring for humans in a spaceflight scenario in making sure all systems are covered for the humans because the synth actually "cares" about the humans in hyper-sleep and will do anything for them to keep them alive and healthy and have the ability to do so since there is the capacity to build civilizations based on a western scriptural code.}
( this being, this creation, will "understand" the importance of Gods creation and understand the need to continue civilization along side humans.}
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Not finished this is only a small part.. I need Cryo operators.
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