Paolo Milani ^1* Francesca Borghi ¹ Thierry Ralph Nieus ² Davide E Galli ¹

• ¹ Department of Physics, University of Milan, Milan, Italy
• ² Department of Environmental Sciences, University of Milan, Milan, Italy

The brain’s ability to perform efficient and fault-tolerant data processing is strongly related to its peculiar interconnected adaptive architecture, based on redundant neural circuits interacting at different scales. By emulating the brain's processing and learning mechanisms, computing technologies strive to achieve higher levels of energy efficiency and computational performance. Although efforts to address neuromorphic solutions through hardware based on top-down CMOS-based technologies have obtained interesting results in terms of energetic efficiency improvement, the replication of brain's self-assembled and redundant architectures is not considered in the roadmaps of data processing electronics. The exploration of solutions based on self-assembled elemental blocks to mimic biological networks' complexity is explored in the general frame of unconventional computing and it has not reached yet a maturity stage enabling a benchmark with standard electronic approaches in terms of performances, compatibility and scalability. Here we discuss some aspects related to advantages and disadvantages in the emulation of the brain for neuromorphic hardware. We also discuss possible direction in terms of hybrid hardware solutions where self-assembled substrates coexist and integrate with conventional electronics in view of neuromorphic architectures.