The ability to create abstract categorizations to be reused in different contexts is one of the most critical abilities for intelligent behavior. In this letter, we present a proposal for how neurobiological structure plays a role in this ability. We introduce two terms: kernel and kernel dimension. The first to describe what the abstraction is and the second to numerically quantify the first. Based on the common abnormalities in both biological structure and cognitive functioning of two psychiatric disorders, autism and schizophrenia, we hypothesize that the density of the dendritic spines in the pyramidal neurons of the Prefrontal Cortex determines the dimension. We show the computational validity of our claims by making two demonstrations with artificial intelligence - one to show that structural density can effect kernel dimension and the other to show that cognitive functioning is effected by the dimension of the kernel .
Over the last few decades, a lot of progress has been made in understanding different aspects of the brain's ability to form abstract representations, but a specific mechanism for how they are created and used remains to emerge. Here, we review recent findings on the subject and we propose a mechanism for the dynamics of forming abstract representations, where the formation of local connectivity in neural networks of abstract representations are the transcription of individual details .
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The ability to create abstract categorizations to be reused in different contexts is one of the most critical abilities for intelligent behavior. In this letter, we present a proposal for how neurobiological structure plays a role in this ability. We introduce two terms: kernel and kernel dimension. The first to describe what the abstraction is and the second to numerically quantify the first. Based on the common abnormalities in both biological structure and cognitive functioning of two psychiatric disorders, autism and schizophrenia, we hypothesize that the density of the dendritic spines in the pyramidal neurons of the Prefrontal Cortex determines the dimension. We show the computational validity of our claims by making two demonstrations with artificial intelligence - one to show that structural density can effect kernel dimension and the other to show that cognitive functioning is effected by the dimension of the kernel
Over the last few decades, a lot of progress has been made in understanding different aspects of the brain's ability to form abstract representations, but a specific mechanism for how they are created and used remains to emerge. Here, we review recent findings on the subject and we propose a mechanism for the dynamics of forming abstract representations, where the formation of local connectivity in neural networks of abstract representations are the transcription of individual details