Abstract: We are at the crossroads of powerful currents in science and technology, spurred by recent advances in both neuroscience research, and neuromorphic engineering. A recent focus on brain studies has produced a wealth of new, multi-modal structural and functional neural data. At the same time, advances in electronics and the search for unconventional computational paradigms led to the creation of neuromorphic systems like Intel’s Loihi, IBM’s TrueNorth, and the European SpiNNaker and BrainScaleS. We see a natural match between hardware realizations of data-based neuronal models produced by the neuroscience research endeavor and the neuromorphic hardware abstractions forming the foundation of current and future neuromorphic chips. We also see a new paradigm in computing abstractions, possibly advancing beyond the von Neumann paradigm, and through Moore’s scaling law.

It seems that we are finally at the stage when we can design true biomimetic neuromorphic systems, essentially replicating a functioning brain tissue in hardware. We are developing neural tissue simulations on neuromorphic systems: creating “digital” neural twins: precise, patient-specific simulations of neural tissue to enhance clinical outcomes for patients.

There is also a broader question: what is computable with these new systems? Neuromorphic system elements have their own dynamics, which is steerable to a degree. How close can such modules approximate dynamics of interest to us? What are appropriate cost functions to assess levels of approximation? And in our context: to what degree can such a neural dynamical system be represented in current neuromorphic computing environment?

And in the midst of that is the practical issue of programming neuromorphic systems. How can we efficiently combine neuromorphic modules to achieve specific dynamics, and later – specific task by that dynamics? As with the original development of computers, tools from applied math, with 70-80 years additional development past the complement of the 1940-s will be crucial for this: nonlinear optimization, including evolutionary programming; linear and nonlinear control; sensitivity analysis; neural-derived cost functions. All neuromorphic simulations should be validated against classic numeric algorithms, e.g. using neuroinformatics tools developed for neural data and model validation.