AND Corporation has developed and commercialized a process known as Holographic Neural Technology.
AND Corporation has developed a rather complete neuro-morphic model of the brain, based upon Holographic/Quantum neural technology (“H.Ne.T.”). They have been distributing these systems for some years now. Details are avaikable at http://www.andcorporation.com/.
John Sutherland from AND Corporation describes the relationship between H.Ne.T. technology and consciousness as follows:
The basis of consciousness arises from the mathematics which indicates that the number of stimulus-response associations that can be enfolded or “superimposed” onto the complex manifold is in essence unbounded (mathematically infinite). This is demonstrated by the ability to learn virtually unrestricted numbers of stimulus-response mappings (or associations) through the underlying phase coherence/decoherence principle, this principle being fundamental also to Quantum Mechanics. This aspect of unbounded information storage and “superposition” is summarized very briefly on our website at:
The consciousness aspect arises from configuring Quantum/Holographic neural cells or cell assemblies within a hyper-incursive architecture, whereby N-dimension state information flows in a continuous loop from the AI systems external environment – through multimodal input sensors of the AI system (i.e. cameras, microphones, axial position, contact sensors etc.) – through to generation of responses (i.e. manipulators, mechanical control of PTZ camera, sound generation, etc.) – and the resultant effect back into the AI systems external environment. Hyperincursive data flow arises, of course, from the AI systems influence upon its external environment, this environmental influence then again fed back in through the systems input sensors.
In essence, the system develops an ID whereby all stimulus-response input-output scenarios are superimposed within the Quantum/Holographic cells (or assemblies) allowing the system to very quickly learn its environment and the effect that the AI system exerts upon its environment, as well as develop autonomously self motivated responses to the external environment.
Motivation is controlled through the attenuation/amplification of signal pathways that are internal to the AI system (analogous to neurotransmitter based depression/elation motivational factors within biological systems).
HNeT and Consciousness
Holographic Neural Technology (HNeT) is based upon superposition of associative information through phase coherence/decoherence principles applying vector product operations within complex manifolds, or so called finite Hilbert spaces. Non-linearity is realized through the quantum tensor product relationships applying product combinatorics across complex manifolds (the stimulus input vectors). The algorithmic process of applying combinatorics is analogous to quantum entanglement. Error reduction is achieved through optimization of combinatoric complex product selection applying neural plasticity. Within the QM context, associative recall is produced through quantum collapse of the wave function. Associative information storage has been shown theoretically to be unbounded applying operations within combinatorial complex manifolds; or in more technical jargon, Nth order tensor product relationships within finite Hilbert spaces.
The process is entirely independent of the material processing substrate or physical quantum effects, but is based solely on algorithmic principles, albeit quantum-like computations. Consciousness within the HNeT model is believed to occur as an emergent property, achieved through temporal buffering of sensory stimuli and/or issued response actions; combined with self referential and hyperincursive influence flowing from efferent nerves (i.e. muscles) via the environment back into the system’s sensory receptive fields. In this manner the AI system develops an “Id”; defined entirely by its ability to associatively store experiences recorded from, and influences induced upon the systems external environment. The HNeT process proposes that conventional computational hardware (ALU, ASIC, FPGA, etc.) has the ability to assume consciousness demonstrated by, and potentially extend beyond current levels in biology, due to its mathematical precision (floating point), speed and physical extension beyond existing biological constraints (i.e. number of cells, synapses per cell, tensor product order, etc.).