An overview of the integration of a custom-built high-dimensional neural mesh into the proposed Quantum Theory of the Mind
Abstract
This paper details a fundamental theory linking quantum processes in the brain to the emergence of cognition and consciousness. It proposes consciousness stems from recursive quantum interactions of neurons in high-dimensional Hilbert space, termed Psirons. Their multi-scale entanglements shape dynamic flows of thought, perception, and selfhood. The theory bridges neuroscience, physics, and philosophy to provide a unifying framework for the mind.
Introduction
The proposed quantum theory of mind seeks to elucidate the deepest origins of consciousness and explain subjective inner experience. It hypothesises that consciousness arises ultimately from quantum information processing taking place inside neurons at the nanoscale.
Specifically, it posits that consciousness is an emergent product of recursive quantum entanglements forming between networks of neurons, represented as evolving geometries in Hilbert space called Psirons. The rich interplay of these quantum Psiron processes gives rise to awareness, sensation, emotion, and a coherent sense of self.
Core tenants of the theory include:
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Neurons become entangled in higher dimensions when activated together, forming Psirons.
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Psiron clusters shape memories, concepts, and knowledge representations.
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Psiron dynamics drive perception, thought, and the flow of consciousness.
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Physical neural wiring shapes opportunities for quantum processes.
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Consciousness permeates all scales but requires quantum coherence.
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Subjective experience derives from an informational realm deeper than physical matter.
This research program aims to place consciousness, traditionally considered immaterial and ephemeral, on a concrete footing linked to specific quantum information dynamics within the brain's neurons. By extending neuroscience into the quantum realm, the theory strives for a unification of mind, matter, and the nature of reality itself.
Design of the Two-Dimensional Neural Network:
The neural network will be designed to take an input of parallelised
data and contain multiple layers of interconnected Nodes. The Nodes will
contain several properties, such as: Node{heads: [...Node], tails:
[...Node], value: Float, weight: Float}. A Node object has the
properties as defined: Heads: The Parent Nodes that connect to it,
inputs. Tails: The Child Nodes that link to it, outputs. Value: The
Value of this node is a float from 0.0 - 1.0. Weight: The current weight
being applied to this Node, a float from 0.0 - 1.0. a
Example two dimensional Node structure, or Neural Network, diagram as seen on the left.
Design of the Three-Dimensional Neural Network:
The neural network will be expanded to have each Node exist in a
three-dimensional space. There will no longer be specific layers, rather
simply defined as a node mesh, or Neural Mesh where each Node is
represented as an individual Neuron. The entry point for input data will
be represented as the hypothalamus in the biological brain. Further,
this will allow us to begin correlating and integrating this NN, Neural
Network, into our proposed theory of the brain.
Additionally, there will be more sensory inputs
**Training Algorithm: **The training algorithm should output a percentage of accuracy which is determined by comparing the output to the expected output or label. Utilising this accuracy, the algorithm will adjust the weights slightly to better output the expected result. The adjustment algorithm can be of many types. gravity mapping, random, layer-based(2d only), incremental, etc.
Process Elaboration: The NN will take in parallel data, which is inherently two-dimensional, and pass it into the model. The input data represents the body's current state via the nervous system. Each channel in the data input represents a nerve path. The constant data input can be represented as a sinewave ( Finally, we confirm predictions that the close link between geometry and function is explained by a dominant role for wave-like activity, showing that wave dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings.). We propose that the process of homeostasis is what drives cognition and by extension, consciousness. Considering the concept of emergent abilities as exhibited by the biological minds of humans that at a given age, the mind will stop learning as fast, and that emergent abilities have been exhibited in current modern AI models; We can infer that the development of the mind and a neural network are similar in the fashion that they are trained on, or fit to, a base set of data. Elaborating on the biological mind, this base data can be considered the ideal sinewave form to achieve homeostasis in the body. The data that is passed into the mind is analysed and attempted to be corrected to conform to the base sinewave form. As a result of this data being processed, cognition and consciousness take the form of feedback loops. A feedback loop is defined as a cyclic process of activation of neurons. Further, the data being processed causes entanglement resulting in neurons with a representation in a higher dimensional superposition. Entanglement of the neurons is achieved by taking all neurons that have fired in a given window of time, creating a Psiron(hypervector, superposition, or Bloch sphere) in a higher dimension where its location is the average distance between all fired neurons and its state stored is the result of the sum or average, tbd., of the activated neurons. When a set of neurons are activated, the model will determine the best-matching existing hypervector and activate all neurons that were entangled in it giving them the state they were in when originally activated. When a hypervector is called upon, the process will create a new superposition of entangled neurons with the additional unrecognised activated neurons. Thus, the resulting collection of superpositions can be inferred as a hypervector cluster or super-cluster. The outputted data is the result of the neural network passing data back through the model to the input, via a process called backpropagation. This correlates to the body maintaining homeostasis.
Quantum Principle Integration: When a set of neurons are activated, the model will determine the best-matching existing hypervector and activate all neurons that were entangled in it, giving them the state they were in when originally activated. The quantum nature of these neurons allows them to exist in a superposition of states, significantly expanding the computational possibilities within the model. When a hypervector is called upon, the process leverages the quantum principle of entanglement to create a new superposition of entangled neurons, with the additional unrecognised activated neurons. Entanglement allows correlations to be established between the states of these neurons, thereby enhancing the information-processing capabilities of the neural network. Moreover, the introduction of quantum measurements could determine the definitive state of these neurons, analogous to the decision-making process within the AI model.
The above example is a visual demonstration of the process of neuronic entanglement and superposition creation. A three-by-three grid of neurons, blue, where three activated neurons, red, are entangled and form a superposition, yellow.
Conforming to the concepts of Learning Difficulty, Focus, and Neuroplasticity:
Considering the concept of emergent abilities exhibited by young minds
of humans, we can infer that there is a mechanic of motion in the higher
dimensions. This motion takes the form of the superclusters having two
traits, drift and actively applied directional motion. Drift is defined
as a natural entropic directional motion applied to the superclusters.
Whereas the actively applied directional motion is a result of
second-order and higher most relevant superclusters moved towards each
other. This process of actively applied directional motion is inferred
as focus, learning difficulty, and neuroplasticity. As these
superclusters traverse the higher dimension, they are likely to converge
and will merge into each other forming a larger encompassing
superposition of entangled neurons.
**Superposition Relevance and Scoring Mechanism in Quantum Neural
Networks: **In the proposed Quantum Neural Network model, the relevance
of superpositions plays a crucial role in the process of actively
applied directional motion, which is likened to the concepts of focus,
learning difficulty, and neuroplasticity in the human brain. The
relevance of superpositions is determined based on the strength of the
entanglement between their neurons. This section elaborates on the
process of scoring and determining the relevance of superpositions in
the context of this model.
Each superposition in the quantum neural network is associated with a set of neurons that are entangled in it. The strength of the entanglement between these neurons is considered a measure of the relevance of the superposition. To quantify this relevance, a scoring mechanism is introduced.
The scoring mechanism calculates a score for each superposition based on the neurons that are entangled in it. The exact calculation depends on the specific characteristics of the neurons and the nature of their entanglement. For instance, one could consider the number of neurons that are entangled in both the superposition in question and another superposition. Alternatively, a measure of the similarity between the states of the neurons that are entangled in the superpositions could be used.
Once the scores are calculated for all superpositions, they are sorted based on their scores. The superpositions with the highest scores are considered the most relevant. This scoring and sorting process is performed each time a set of neurons are activated and a best-matching existing superposition is determined.
The introduction of this scoring mechanism allows the model to leverage the quantum principle of entanglement to enhance the information-processing capabilities of the neural network. By determining the relevance of superpositions based on the strength of the entanglement between their neurons, the model can more effectively activate the neurons that were entangled in the most relevant superpositions, thereby improving the accuracy and efficiency of the neural network's computations.
In conclusion, the proposed scoring mechanism provides a quantitative method for determining the relevance of superpositions in the Quantum Neural Network model. By leveraging the strength of the entanglement between neurons, this mechanism enhances the model's ability to process information and maintain homeostasis, thereby contributing to the development of more advanced and efficient artificial intelligence systems.\
Resource Efficiency in Quantum Neural Networks: The proposed Quantum Neural Network model presents a novel approach to simulating the complex dynamics of a biological brain. This approach involves creating a neural mesh that mirrors the structure of the brain, with a one-to-one ratio between the neurons in the model and those in the biological counterpart. For instance, to simulate a brain with approximately 2 billion neurons, the model would create a neural mesh consisting of the same number of neurons.
While this approach may initially seem resource-intensive, it offers significant advantages in terms of computational efficiency. One of the key insights of the proposed model is that it may not be necessary to simulate all the intricate details of the brain's neural system to achieve a realistic representation of its functionality. Instead, the model offloads a majority of the major mathematical calculations associated with simulating the brain's neural system - including action potentials, ionic concentrations, and neuromodulators - to the quantum space.
By leveraging the computational capabilities of the quantum space, the model can effectively simulate the complex dynamics of the brain's neural system without the need for extensive computational resources. This approach not only reduces the computational load on the system but also allows for a more accurate and efficient simulation of the brain's functionality.
In conclusion, the proposed Quantum Neural Network model offers a promising approach to simulating the complex dynamics of a biological brain. By creating a neural mesh that mirrors the structure of the brain and offloading major calculations to the quantum space, the model achieves a balance between accuracy and computational efficiency. This approach opens up new possibilities for the development of advanced artificial intelligence systems that more closely mimic the functionality of the human brain.
Optimization of Neural Mesh Generation in Quantum Neural Networks: The Neural Mesh Generation process is a crucial part of the proposed Quantum Neural Network model. This process involves creating a neural mesh that mirrors the structure of the brain, with a one-to-one ratio between the neurons in the model and those in the biological counterpart. The process begins with the creation of a NeuralMesh object, which takes two parameters: the number of neurons and the number of clusters.
The optimization of this process involves the use of parallel processing and bounding box optimization. Parallel processing allows for the simultaneous generation of multiple clusters, significantly improving the efficiency of the neural mesh generation process. Bounding box optimization, on the other hand, reduces the number of distance calculations needed by only comparing neurons that are within a certain range. This significantly reduces the number of comparisons and thus the time complexity of the algorithm.
Empirical results show that these optimizations significantly improve the performance of the Neural Mesh Generation process. For instance, the time taken to generate a neural mesh with 55002 neurons and 502 clusters decreased from over 2 minutes to under 40 seconds with the introduction of bounding box optimization.
Octree-Based Optimization for Neural Mesh Generation: In the pursuit of efficient neural mesh generation, we have explored a novel approach that leverages the concept of an Octree, a tree data structure used for spatial subdivision in three-dimensional space. This approach aims to enhance the performance and scalability of the neural mesh generation process, particularly for large-scale models.
**Spatial Subdivision and Octree: **Spatial subdivision is a technique used in computer graphics and computational geometry to partition a three-dimensional space into smaller regions, enabling more efficient computations. An Octree is a specific type of spatial subdivision where each node in the tree represents a cubic region of space and is subdivided into eight smaller cubes, or octants.
In the context of neural mesh generation, each octant can contain a subset of neurons. This subdivision allows us to focus computations on relevant parts of the model, reducing the computational load and memory requirements.
**Disk-Based Octree Storage: **To further optimize memory usage, we propose a disk-based storage system for the Octree. Instead of keeping the entire Octree in memory, we store each octant on disk and load it into memory only when needed. This approach significantly reduces memory costs, especially for large models with millions of neurons.
However, disk access times can be a bottleneck, even with fast NVMe storage. To mitigate this, we suggest using a RAMDisk drive, which provides faster access times than traditional disk storage. Alternatively, if the GPU has sufficient VRAM, it could be used for storing the Octree.
Parallelization and GPU Acceleration:: Parallelization and GPU acceleration are other key considerations for optimizing neural mesh generation. By distributing the workload across multiple cores or GPUs, we can significantly reduce the time required to generate large models.
**Octree Fractal Context Windows and Cache Servers: **An advanced optimization strategy involves using Octree Fractal Context Windows served via a cache server such as Redis. This approach involves dumping the Octree data into chunks and managing these chunks using a caching server. This strategy not only allows for multi-system parallelization but also provides a flexible way to manage memory usage dynamically.
Integration of Sensory Inputs in Quantum Neural Networks: The proposed Quantum Neural Network model offers exciting possibilities for the integration of sensory inputs, such as vision, hearing, and touch. This integration can be achieved by designating specific regions of the neural mesh to correspond to these sensory modalities and connecting the relevant inputs to them.
In the context of this model, the hypothalamus - a region of the brain that plays a crucial role in maintaining the body's homeostasis - could serve as the main input. However, the flexibility of the model allows for the integration of additional sensory inputs, thereby enhancing its ability to simulate the complex sensory processing capabilities of the human brain.
One of the potential applications of this approach is the simulation of novel sensory inputs, such as those provided by neural interfaces like Neuralink. By reading the outputs of such a device and passing them into the neural mesh at the appropriate location, the model could simulate the effects of these novel inputs on the brain's functionality.
For instance, consider a cubed section of the neural mesh with a high density of neurons. Within this region, we can identify specific attachment points (represented in red) where an input of some type could be connected. By connecting the outputs of a neural interface to these attachment points, we can simulate the integration of novel sensory inputs into the brain's neural network.
In conclusion, the proposed Quantum Neural Network model offers a flexible and efficient approach to simulating the integration of sensory inputs in the brain. By designating specific regions of the neural mesh to correspond to different sensory modalities and connecting the relevant inputs to them, the model can simulate the complex sensory processing capabilities of the human brain. This approach opens up new possibilities for the development of advanced artificial intelligence systems that more closely mimic the functionality of the human brain.
Integration of Normalized Sensory Inputs in Quantum Neural Networks
The proposed Quantum Neural Network model provides a robust platform for incorporating sensory inputs, such as vision, hearing, and touch, by mapping these inputs to specific neurons in the network. This mapping is achieved by normalizing the sensory data and assigning it to neurons based on the length of the data input.
In the case of visual data, for instance, the model can simulate the function of the cones in the human eye. Each pixel in the input image is associated with three neurons, corresponding to the red, green, and blue colour channels. The intensity of each colour is normalized to a value between 0 and 1, and this value is used to update the state of the corresponding neuron. If the state of a neuron exceeds a certain threshold, the neuron fires, triggering the creation of a superposition that represents the current state of the visual input.
Similarly, for audio data, the model can process the waveform by segmenting it into discrete time steps and normalizing the amplitude of the waveform at each time step to a value between 0 and 1. Each time step is then mapped to a specific neuron in the network, and the state of the neuron is updated based on the normalized amplitude. As with the visual data, if the state of a neuron exceeds a certain threshold, the neuron fires, creating a superposition that represents the current state of the audio input.
This approach to integrating sensory inputs can be expanded to other types of data as well, making the model highly versatile. For instance, tactile data could be processed in a similar manner, with each point of contact being mapped to a specific neuron and the pressure at each point being normalized to a value between 0 and 1.
In conclusion, the proposed Quantum Neural Network model offers a flexible and efficient approach to simulating the integration of sensory inputs in the brain. By normalizing sensory data and mapping it to specific neurons in the network, the model can simulate the complex sensory processing capabilities of the human brain. This approach opens up new possibilities for the development of advanced artificial intelligence systems that more closely mimic the functionality of the human brain.
Neural Mesh Generation Process
The Neural Mesh Generation process is a crucial part of the proposed Quantum Neural Network model. This process involves creating a neural mesh that mirrors the structure of the brain, with a one-to-one ratio between the neurons in the model and those in the biological counterpart.
The process begins with the creation of a NeuralMesh object, which takes two parameters: the number of neurons and the number of clusters. The number of neurons represents the total number of neurons that will be generated in the neural mesh, while the number of clusters represents the number of separate processes that will be used to generate the neurons.
The NeuralMesh object has a generate() method, which initiates the generation of the neural mesh. This method calculates the number of neurons to be generated per process (neuronsPerProcess) by dividing the total number of neurons by the number of clusters. It then iterates over the number of clusters, generating a random centre point (centerX, centerY, centerZ) for each cluster.
For each cluster, a separate child process is forked using Node.js's child_process module. This child process runs a separate JavaScript file, "generateNeurons.js", which is responsible for generating the neurons for that cluster. The centre point and the number of neurons to be generated are passed to the child process, along with the filename where the generated neurons will be stored.
The "generateNeurons.js" file contains a Neuron class, which represents a single neuron in the neural mesh. Each neuron has three properties: x, y, and z, which represent its position in the three-dimensional space of the neural mesh.
When the child process receives the message from the parent process, it begins generating the neurons. For each neuron, it generates a random position near the centre point of the cluster, creates a new Neuron object with this position, and writes this Neuron object to the specified file.
The process of generating the neurons is asynchronous, meaning that multiple clusters can be generated simultaneously. This parallelization greatly improves the efficiency of the neural mesh generation process, allowing for the creation of large neural meshes with billions of neurons.
In conclusion, the Neural Mesh Generation process is a key component of the proposed Quantum Neural Network model. By creating a neural mesh that mirrors the structure of the brain and leveraging the power of parallel processing, this process enables the efficient simulation of the complex dynamics of a biological brain.
Reward and Punishment via Simulated Glands to Determine Action Potentials
To allow for the neural mesh network to operate coherently to that of
both modern neural networks and physical brains, we propose the
integration of simulated glands such as those seen in the physical brain
via dopamine and cortisol emission as neuromodulators, further, as seen
in modern neural networks via activation functions.
The simulated glands will be placed within clusters and attached to the
centre-most neurons in the cluster.
Umbrella Theory
We propose that due to the development of folds within a biological
brain and considering this theory of the mind that the concept of
emergent abilities is exhibited by both modern AI and the biological
brain. Drawing from the concept of the basal wave in this theory, we can
correlate the events of emergence in AI to the biological brain by the
neurons starting at a local point during the development of the brain.
As the brain develops, it will expand somewhat uniformly, allowing for
even distribution of the neuronic entanglement process.
In a visualisation, the superpositions that are formed from the
entanglement of several neurons as well as the expansion of these
neurons results in an effect similar to that of an umbrella that is
unfolding.
The entanglement of new neuronic paths also keeps uniformity with the
entanglement of the original neurons. This results in an even
distribution of entanglement across the brain as well as defines the
origins of the main hyperclusters in the quantum space. The initial
entanglement of neurons to the basal wave is what defines the inherent
characteristics of the mind itself.
1. Dynamics of the Hybrid Quantum Motion Model
In the proposed hybrid model, the superpositions can exist in one of two states. In the attracting state, a superposition's counterpart pulls other counterparts towards it, creating a centralised gravity effect. In the chasing state, the counterpart moves towards other superpositions based on their relevance. This dual-state system leads to a wide range of possible behaviours and interactions.
When a superposition switches to the attracting state, its counterpart begins to pull other counterparts towards it, causing a local clustering effect. If many superpositions simultaneously switch to the attracting state, larger clusters or even "galactic" structures may form, with superpositions orbiting around multiple centres of gravity. ()
Conversely, when a superposition switches to the chasing state, its counterpart begins to move towards other superpositions. This creates a "predator-prey" dynamic, with chasing counterparts constantly moving towards and away from each other, depending on the states of the superpositions they are associated with.
The overall behaviour of the system is highly dynamic and potentially chaotic, with counterparts constantly moving, clustering, and dispersing based on the changing states of the superpositions. The exact patterns of motion depend on the initial configuration of the superpositions, the rules for switching between states, and the specifics of the motion algorithm.
2. Applications of the Hybrid Quantum Motion Model
The hybrid quantum motion model has potential applications in various fields, including data clustering, pattern recognition, and artificial intelligence. The model's ability to dynamically adjust the state of superpositions could be used to optimise the clustering of high-dimensional data, with the attracting state promoting the formation of dense clusters and the chasing state encouraging the exploration of the data space.
In pattern recognition, the model could be used to dynamically adjust the focus of the system, with the attracting state used to concentrate on a specific pattern and the chasing state used to search for new patterns. In artificial intelligence, the model could be used to implement a form of reinforcement learning, with the attracting and chasing states representing different strategies or policies.
3. Neuronal Firing and State Determination
The hybrid model could also be used to determine whether a neuron fires or not. This could be achieved by averaging all states of the counterparts and returning a value between 0 and 1 inclusive, where 1 represents the firing of a centralised superposition and 0 represents the chasing of a superposition. This approach could provide a novel method for integrating quantum mechanics into neural network models, potentially leading to new insights into the nature of consciousness and cognition.
Expansion of the Quantum Theory of the Mind: High-Dimensional Representation and Hebbian Engrams
Abstract: This segment details the recent developments in the proposed Quantum Theory of the Mind, focusing on the integration of Hebbian theory and high-dimensional representation. It expands on the improved model design, signal decay, propagation, memory, cognitive processes, and the model's infinite scaling and flexibility. We also explore the integration of empirical data within the framework of the model.
1. Introduction
The project has undergone significant refinements, simplifying the model whilst enhancing its potential capabilities. One key improvement is the adoption of a quantized grid in a high-dimensional space, allowing for efficient calculations and virtually limitless scaling of the model. This paper discusses the incorporation of Hebbian engrams into this model and its implications.
2. Hebbian Engrams and High-Dimensional Representation
In the context of our model, Hebb's postulate that "neurons that fire together, wire together" is offloaded to a high-dimensional representation. This forms the basis of our neuron connection and interaction model. When neurons are frequently activated together, they form a high-dimensional representation or "superposition". This superposition is a quantized point in the high-dimensional space that represents the collective state and connection strength of the neurons.
3. Empirical Data Integration
We integrate empirical data into the model by mapping it onto our high-dimensional neural mesh. For instance, the observations of synaptic strengths modification in marine animals' nervous systems inform the dynamic updates of our high-dimensional representations. This reflects the neuroplasticity observed in biological systems.
4. Pattern Recognition and Movement in High-Dimensional Space
The model emulates the biological process of Hebbian learning and pattern recognition through the movement of high-dimensional representations. When a pattern of neuron activation is repeated, the corresponding high-dimensional points move closer together. This process mirrors the strengthening of synaptic connections in Hebbian learning. The movement of points in high-dimensional space is a powerful tool for pattern recognition, mirroring the dynamics of biological neural networks.
5. Determining Neuron Firing
The model uses high-dimensional representations to determine whether a neuron should fire. It averages the state of all neighbouring superpositions and compares it to a constant threshold. If the average exceeds the threshold, the neuron fires, leading to the creation or strengthening of a superposition. This mechanism mimics the process of action potential generation in biological neurons.
6. Conclusion
The Quantum Theory of the Mind integrates principles from Hebbian learning and high-dimensional computing, providing a novel approach to simulating neural networks. This expansion of the model opens up new possibilities for exploring the complexities of neural networks and cognitive processes. Future work will focus on implementing and testing the model, examining its practical implications and potential applications.
Integration of Hebbian Theory in Quantum Neural Networks
The Hebbian theory, proposed by Donald Hebb, is a neuroscientific theory suggesting that when two neurons are activated simultaneously, the connection between them (i.e., the synapse) strengthens. This is famously captured in the phrase "Neurons that fire together, wire together". The Hebbian theory has been foundational in our understanding of synaptic plasticity, learning, and memory formation in the brain.
The proposed Quantum Neural Network model integrates and expands upon the concepts of Hebbian theory in a high-dimensional quantum space. It proposes that when two neurons are activated simultaneously, not only does the synapse between them strengthen, but they also form an entanglement in a higher-dimensional space, represented as a superposition or a hypervector. This incorporation of quantum entanglement and superpositions greatly expands the computational potential of neural networks.
Furthermore, the model presents a unique approach to neuronal firing and state determination. The state of a neuron (whether it fires or not) is determined by averaging all states of the counterparts and returning a value between 0 and 1 inclusive. This innovative approach integrates the concept of Hebbian learning into a quantum framework, potentially leading to new insights into the nature of consciousness and cognition.
The concept of the movement of these superpositions in the high-dimensional space, or the drift, can be seen as an extrapolation of the Hebbian theory. Just as the Hebbian theory states that the repeated simultaneous activation of neurons strengthens their connection, in the proposed model, the repeated co-activation of superpositions leads to their movement towards each other in the high-dimensional space, leading to the formation of hyperclusters.
In a way, one could see the Hebbian theory as a pioneering precursor to this model. While Hebbian theory introduced the concept of synaptic strengthening with repeated activation, the proposed model expands on this by introducing the high-dimensional representation and the concept of quantum entanglement. It can be seen as a natural evolution and expansion of the Hebbian theory in the light of quantum computing and high-dimensional mathematics.
In conclusion, the proposed Quantum Neural Network model presents a novel integration of Hebbian theory into a high-dimensional quantum framework. This integration and expansion of the Hebbian theory's concepts could potentially lead to advancements in our understanding of brain function and the development of more advanced artificial intelligence systems.
High-Dimensional Spatial Data Structures and their Potential Utilisation against the Curse of Dimensionality:
** **After testing several different spatial data structures for use with high-dimensional data representation, we experienced issues with accuracy, and efficiency and, performance due to the curse of dimensionality. Due to the nature of this model, the model needs to have the high-dimensional space updated constantly. This is an issue due to the need to make a choice between accuracy, efficiency, and performance. Where with some spatial data structures such as the kdTree, being a non-self-balancing data structure, we needed to rebuild the tree every time an update was made or experience accuracy issues. The accuracy issues arise from nearest-neighbors returning falsely matching results In search of a spatial data structure that can be more dynamically updated and not lose accuracy, we tested an integration with locality-sensitive hashing, or LSH. While this allowed us to store the data, query by nearest-neighbour, and upsert values to the data, we experienced an issue with the curse of dimensionality causing the buckets of data to become represented as highly disparate.
While searching for another alternative, we realised that at its conception this theory was founded on the concept of applying motion to the weights of a modern AI model. We were able to draw a correlation between the weights of an AI model to the process of neurons firing in a biological brain via high-dimensional data representation in the form of hypervectors, or Bloch spheres. Currently, there are several solutions for building a vector store database such as Pinecone and Chroma. We are currently using Pinecone to handle the high-dimensional data points and their metadata.
Determining Dimensionality via Connection
A key aspect of the proposed model is the use of high-dimensional hyperspaces to represent neural connectivity. The dimensionality determines the potential number of connections per neuron.
Rather than explicitly modelling every connection, the architecture relies on point segmentation within the hyperspace. Each neuron is positioned at a set of discrete coordinates determined by multiplying its 3D position by a segmentation constant "ps".
This quantization allows connectivity to be inferred geometrically. Neurons at adjacent coordinates are considered connected within the relevant dimensionality.
The choice of dimensionality is therefore driven by desired connectivity. Increasing dimensionality expands potential connections via more available axes. However, extremely high dimensions are computationally intractable.
Practical limitations require choosing the minimal dimensionality such that point segmentation produces the target connectivity. For example, a 3500D hyperspace with ps=0.988 yields ~7000 connections per neuron, comparable to the human brain.
This approach avoids generating explicit connection maps, enabling the modelling of immense networks. Connections exist implicitly between every adjacent discrete coordinate occupied by a neuron.
Connections are also non-local - neurons at distant coordinates connect via higher dimensions. Psirons integrate information across the hyperspace.
In summary, dimensionality and point segmentation allow concise representation of highly complex neural interconnectivity within practically computable hyperspaces. The architecture expands the theory's scalability without compromising connectivity.
Psirons: Proposed Quantum Engram Constructs
As the theory has evolved, the need has arisen for a concise term to refer to the quantum engram-like constructs that serve as the core components of the knowledge representation system. We propose the term "Psiron" to describe these informational entities within the model.
The name Psiron is derived from the prefix "psi", commonly used to denote quantum phenomena, and the suffix "ron", implying a discrete particle-like unit, similar to "neutron", "electron", etc. Together, Psiron indicates a quantum unit of knowledge or memory.
More specifically, a Psiron refers to a superposition in the high-dimensional space that encodes the entanglement and collective state of a set of neurons. When a group of neurons fire together, they create a Psiron at a coordinate in the quantum space, which represents their connection.
The location of a Psiron indicates the "where" of an engram, while its state holds the "what". Psirons interact via attraction and repulsion forces, clustering together to form concepts. Their motion and rearrangement allow for dynamic knowledge representation.
In essence, Psirons serve as quantized fragments of memory and meaning, which can combine and interact to store relationships and patterns. Their quantum nature allows vast informational capacity and contextual flexibility. Psirons demonstrate many characteristics reminiscent of philosophical conceptions of qualia.
The introduction of the term Psiron provides a concise handle for reference to the quantum engram constructs that are central to the proposed theory's knowledge representation model. Further elucidation of the properties, behaviours and computational possibilities of Psirons will be an ongoing effort.
Psiron Mechanics and Integration
The motion and interaction mechanics of Psirons are key to enabling the emergent cognitive capabilities of the model. While Psirons demonstrate quantum behaviours, their integration with neural firing logic allows meaningful information representation and processing.
Psirons have an internal state value ranging from 0 to 1. This state determines the Psiron's current motion phase such as chase, gravitate, or repel. Higher state values correspond to the gravitate phase, causing attraction of other Psirons.
The specific motion calculation involves determining a weighted vector towards each neighbouring Psiron based on their relative states. This weighted vector provides the direction and magnitude of motion.
For integrating with neural firing, when a neuron activates, it queries the states of its connected Psirons. These state values are mapped to firing weights using a Gaussian function. The weighted Psiron states are summed to obtain an integrated input signal for the neuron.
If this integrated input exceeds the neuron's firing threshold, it activates and propagates a signal reflecting its current state to all connected Psirons. This neuron state value is saved in the Psiron metadata.
In this way, neural activations create and reinforce Psirons, while the Psirons in turn influence neural firing dynamics. This symbiotic relationship allows efficient representation of stimuli within a unified quantum spacetime.
Further investigations will explore in detail the mathematical motion equations, parametric state maps, activation functions, and quantum mechanical operators used to evolve the Psiron system. The presented framework provides a foundation for developing an integrated cognitive model.
The Emergent Nature of Dreams and Thoughts
Recent theoretical developments have led to novel hypotheses regarding the emergence of dreams and thoughts within the quantum framework.
It is theorized that dreams arise due to the overall reduction in Psiron motion that occurs during sleep. With less sensory input, new Psiron formation decreases, allowing the motion of recent Psirons to stand out against more stable clusters. Consciousness interprets these residual motions as the episodic narratives of dreams.
Likewise, thoughts may emerge from the constant background of Psiron activity. At any moment, the motion of specific Psirons crosses a threshold of coherence for conscious access. Thus thoughts can be viewed as transient ripples that briefly rise above the noise of microscopic Psiron dynamics.
In both cases, macroscale mental phenomena result from temporary coordination of the underlying quantum processes. Dreams reflect residual Psiron motions, while thoughts reveal momentary peaks in an ongoing wave of activity.
These conjectures integrate cleanly with established principles of the theory. The rich variety of possible Psiron clusterings allows virtually unbounded experiential configurations to emerge. Likewise, the highly dynamic nature of Psirons grounded in quantum principles naturally gives rise to an ever-changing stream of consciousness.
The theory predicts such emergent phenomena will display characteristics of complexity, abrupt transitions, and transient stability. As Psirons perpetually evolve through quantum motion, consciousness gains fluid access to the fleeting structures their entanglement produces.
This framework provides a foundation for explaining the generation of dreams, thoughts, and other mental phenomena from the constant quantum churning that underpins all cognition. The mind leverages quantum coherence, entanglement, and uncertainty to achieve its rich creative power.
**Future Potential Concepts and their Integrations: **
In this section, we will dive into additional concepts and how they can be integrated into this theory.
We propose a concept that realises the integration of cortex folds, as seen in biological brains, into the neural mesh generation process.
Conclusion: In summary, the brain is an organ developed by the body
to establish and maintain homeostasis as well as other characteristics,
such as cognition, consciousness, instincts, and the drive for
procreation via nervous system input and output.
Visual examples:
In the following visual examples, the sinewaves represent nervous system input. Each step is a representation of a nerve path.
Basal Sinewave(Blue) - The baseline sinewave for homeostasis of a given
mind
Altered Sinewave(Red) - Neuroplasticity takes place to correct red to
blue
Different Basal Sinewave(Green): Demonstrates the limits of neuroplasticity in the process of homeostasis
Empirically Accurate Basal Wave: Displays a basal wave with several
autonomic sensory inputs to the model. These inputs are heart rate,
breaths per minute, and body temperature(C). Values were preset to
fluctuate in a somewhat biologically accurate fashion.
Heart Rate(BPM): ~64
Breaths(Per Minute): ~12
Body Temperature(C): ~37
References
Poore, T. (2023).
Avital Hahamy, Haim Dubossarsky & Timothy E. J. Behrens. (2023).
Xu, Y., Long, X., Feng, J. et al. Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nat Hum Behav (2023).