Howard is known for his Theory of Intention Awareness, which provides a possible model for explaining volition in human intelligence, recursively throughout all layers of biological organization. He next developed the Mood State Indicator a machine learning system capable of predicting emotional states by modeling the mental processes involved in human speech and writing. The Language Axiological Input/Output system was built upon this MSI framework and found to be capable of detecting both sentiment and cognitive states by parsing sentences into words, then processing each through time orientation, contextual-prediction and subsequent modules, before computing each word's contextual and grammatical function with a Mind Default Axiology. The key significance of LXIO was its ability to incorporate conscious thought and bodily expression into a uniform code schema. In 2012,. Howard published the Fundamental Code Unit theory, which uses unitary mathematics to correlate networks of neurophysiological processes to higher order function. In 2013, he proposed the Brain Code theory, a methodology for using the FCU to map entire circuits of neurological activity to behavior and response, effectively decoding the language of the brain. In 2014, he hypothesized a functional endogenous optical network within the brain, mediated by neuropsin. This self-regulating cycle of photon-mediated events in the neocortex involves sequential interactions among 3 mitochondrial sources of endogenously-generated photons during periods of increased neural spiking activity: near-UV photons, a free radical reaction byproduct; blue photons emitted by NADH upon absorption of near-UV photons; and green photons generated by NADH oxidases, upon NADH-generated blue photon absorption. The bistable nature of this nanoscale quantum process provides evidence that an on/off coding system exists at the most fundamental level of brain operation.
Selected works
Books
Howard, N., Argamon, S. . . Berlin: Springer-Verlag.
Most-cited journal articles
Hussain, A., Cambria, E., Schuller, B., Howard, N.. , Neural Networks, Special Issue, 58, 1-3.
Howard, N., Bergmann, J. & Stein, J.. Frontiers Special Issue INCF Course Imaging the Brain at Different Scales.
Howard, N.. . Lecture Notes in Computer Science, MICAI, November 24–30, 2013, Mexico City, Mexico.
Howard, N.. . The Brain Sciences Journal, 1, 4-45.
Howard, N.. . The Brain Sciences Journal, 1, 46-61.
Howard, N., Lieberman, H.. BrainSpace: Automated Brain Understanding and Machine Constructed Analytics in Neuroscience. The Brain Sciences Journal, 1, 85-97.
Howard, N., Guidere, M.. . The Brain Sciences Journal, 1, 98-109.
Howard, N. & Bergmann, J.. . Journal of Functional Neurology, Rehabilitation and Ergonomics, 2, 29-38
Howard, N., Kanareykin, S. . The Brain Sciences Journal, 1, 110-124.
Howard, N. The . Published by Center for Advanced Defense Studies /Institute for the Mathematical Complexity & Cognition Centre de Recherche en Informatique, Université Paris Sorbonne