Neuromorphic Embedded Processing for Touch

Touch, one of our primary senses, allows us to explore and understand the physical
world around us. It involves the detection of mechanical stimuli, such as pressure,
vibration, and texture, through specialized receptors distributed across our skin.
Robots and prostheses struggle to replicate the nuanced sense of touch found in
humans. This limitation degrades their ability to perform tasks reliant on tactile
perception, such as object manipulation and texture discrimination, hindering their
overall functionality.
For this reason, there is a critical need to enhance these technologies with advanced
tactile sensing capabilities to improve their versatility and enable more natural
interactions.
In this thesis, we demonstrate how, using neuromorphic principles inspired by
neuroscientific literature, we can address these limitations. In the first part of the
thesis, we investigate how taking inspiration from mechanoreceptors can inform the
design of novel sensors capable of encoding pressure into spike patterns. Following
this, we explore the decoding of these signals using networks composed only of
spiking neurons and synapses, drawing inspiration from biological findings. The
resulting architectures show dynamics qualitatively resembling evidence from
neuroscientific experiments. Finally, we demonstrate how the networks we designed
can be translated onto CMOS hardware for future deployments in real-world
agents.
Our results underscore the importance of leveraging neuroscientific literature to
inform the design of future technologies for tactile perception in artificial agents.
This paradigm promises extremely powerful given the shared constraints between
artificial systems and their biological counterparts.
This work lays down a compelling example of drawing inspiration from biology to
enhance the design of artificial agents with tactile capabilities. As such, the results
presented in this thesis pave the way for further research endeavors informed by the
blueprint of natural systems, encouraging neuromorphic engineers to explore the
knowledge available in biology to inform their own designs. Embracing biological
principles not only facilitates the development of more efficient and effective artificial
agents but also fosters a deeper understanding of the natural world.