Nara Logics has developed a new kind of neuroscience-based synaptic intelligence that uses association networks to automatically organize information using synaptic learning rules.
The Nara Logics team tested its platform’s ability to deliver truly individualized restaurant recommendations against a more traditional marketing approach of simply recommending the most popular recommendations. In this paper, we cover the methodology and results, as well as the ease of integration with Nara Logics’ synaptic intelligence platform for personalization.
This work helps define a mathematical logic for brain networks, and how they function to compute answers. It describes how “inhibition” in our brains can be used to scale and sharpen representations, and this is used at Nara to cancel noise for retrieving recommendations.
This reviews the current thinking in the field of neuroscience about how neural networks undergo plasticity or “learn” new information. There are a range of rules and principles available to networks that have been identified, and these work together to organize information in our brains automatically. At Nara we attempt to judiciously apply a growing number of these rules in balance, to build next-generation synaptic intelligence based on a new understanding that is emerging among neuroscientists about how brain networks function.
This academic journal article written by Nara Logics CTO, Nathan Wilson, shows how varying synaptic parameters can change the formation, organization and degeneration of neuronal circuits. Being able to coordinate the size and shape of synaptic networks demonstrates a first step toward engineering customized neuronal networks to explore how network architecture impacts network function.