A theory of general intelligence. (February 2019)

• Record Type: Journal Article
• Authors: Lui, Hin Wai
• Is Part Of: Medical hypotheses. Volume 123(2019)
• Page Start: 35
• Page End: 46
• Publication Date: 2019-02
• Subjects: System neuroscience -- Artificial general intelligence -- Neuron dual firing mode -- Awareness -- Subliminal processes; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Medicine; Periodicals; 610
• DOI: 10.1016/j.mehy.2018.12.001
• Languages: English
• ISSNs: 0306-9877

Abstract

Abstract: This paper proposes a theoretical framework for the biological learning mechanism as a general learning system. The proposal is as follows. The bursting and tonic modes of firing patterns found in many neuron types in the brain correspond to two separate modes of information processing, with one mode resulting in awareness, and another mode being subliminal. In such a coding scheme, a neuron in bursting state codes for the highest level of perceptual abstraction representing a pattern of sensory stimuli, or volitional abstraction representing a pattern of muscle contraction sequences. Within the 50–250 ms minimum integration time of experience, the bursting neurons form synchrony ensembles to allow for binding of related percepts. The degree which different bursting neurons can be merged into the same synchrony ensemble depends on the underlying cortical connections that represent the degree of perceptual similarity. These synchrony ensembles compete for selective attention to remain active. The dominant synchrony ensemble triggers episodic memory recall in the hippocampus, while forming new episodic memory with current sensory stimuli, resulting in a stream of thoughts. Neuromodulation modulates both top-down selection of synchrony ensembles, and memory formation. Episodic memory stored in the hippocampus is transferred to semantic and procedural memory in the cortex during rapid eye movement sleep, by updating cortical neuron synaptic weights with spike timing dependent plasticity. With the update of synaptic weights, new neurons become bursting while previous bursting neurons become tonic, allowing bursting neurons to move up to a higher level of perceptual abstraction. Finally, the proposed learning mechanism is compared with the back-propagation algorithm used in deep neural networks, and a proposal of how the credit assignment problem can be addressed by the current theory is presented.