Tohru Nitta

National Institute of Advanced Industrial Science and Technology

Papers

Total Citations

200

H-Index

About

Tohru Nitta is a distinguished researcher whose work sits at the intersection of advanced mathematics and artificial neural networks, with a particular focus on extending classical neural network architectures into richer algebraic domains. His most celebrated contribution lies in the application of Clifford's Geometric Algebra to neural computation, a 2013 work that has garnered 169 citations and established him as a leading voice in geometrically informed machine learning. Nitta has also made sustained contributions to the field of complex-valued neural networks, exploring how the incorporation of complex numbers — ubiquitous in telecommunications, robotics, bioinformatics, image processing, and speech recognition — can enhance the representational power of neural architectures beyond conventional real-valued approaches. His 2011 volume on the subject attracted 27 citations and helped consolidate the theoretical foundations of this emerging discipline. Taken together, Nitta's body of work reflects a consistent intellectual mission: to bridge abstract mathematical structures and practical neural network design, offering researchers tools better suited to the multidimensional nature of real-world data. His research continues to inform developments in signal processing, pattern recognition, and the broader study of hypercomplex neural systems.