Instrumentation & Measurement Magazine 25-9 - 19

Insect-Inspired Spiking Neural
Controllers for Adaptive
Behaviors in Bio-Robots
Paolo Arena, Alessia Li Noce, Luca Patanè, and Salvatore Taffara
T
he perception-action loop is at the basis of living beings'
behavior and still represents a challenging
process to be modeled, due to its complexity both at
the level of the input sensory system and the output behavioral
repertoire, determined by the real-life constraints imposed by
the environment.
Taking inspiration from nature, it is possible to formalize
behavior generation with a holistic approach: internal neural
dynamics merge fresh sensory information with internally
memorized ones and properly define a specific, context-aware
behavior. Within this framework, and to design new families
of neural controllers for perception, insect brains offer a
unique inspiration source, being not so complex to be studied
in detail and, at the same time, still able to show impressive
adaptive behaviors. Recent studies on the insect brain demonstrated
that important neural centers of the central brain,
such as the mushroom bodies (MBs), behave as complex reaction-diffusion
systems where the information gathered from
the sensory system is processed, generating a variety of internal
dynamics that can be exploited by extrinsic neurons
for the generation of motor activities. Several studies on MB
structures, carried out also by the authors, identified a basic
similar (even if simplified) computing scheme in the reservoir
computing (RC) approach. An added value of this dynamic
processing scheme is the possibility to reduce the computational
complexity through neural reuse. This is a fundamental
optimization principle adopted in the brain which involves the
exploitation of the same neural circuit for different tasks after
the expected initial function. The RC approach is a perfect paradigm
to demonstrate this property, allowing the application
of multiple read-out maps able to shape the internal knowledge
generated in the same pool of spiking neurons to solve
different tasks, guided by a learning process. The authors were
involved for many years in designing and realizing spiking
neural models to act as nonlinear behavioral and locomotion
controllers in bio-inspired robots. This work illustrates,
in a concise way, the main guidelines for architectural formulation
and implemention of spiking neural controllers for the
generation of bio-inspired adaptive behaviors, using the RC
December 2022
approach as a feasible model of specific insect brain structures.
Moreover, we discuss two cases of study related to robotic applications,
involving a low- and a high-level sensory-motor
loop: estimation of the ground reaction force in a quadruped
robot and sequence learning applied to navigation in a roving
platform.
Background on Insect Brain Modeling
Understanding brain dynamics is one of the most important
targets of worldwide research from many different
perspectives. From the engineering one, the focus is to identify
and formalize the details linking the structure, function
and behavior of biological neural networks, from the micro
to the macro scale, to design efficient and highly adaptive
sensing-perceiving-moving artefacts, able to actively and
collaboratively interact with humans while accomplishing
difficult tasks. If the main focus is to " understand-modeldesign-reproduce-test, "
it is too ambitious, at least presently,
to inspect higher (e.g., mammals) brain structures. In fact,
the simpler the brain, the simpler the analysis and design
task. Within this framework, insects are the first candidates
from the bio-engineering side: although their brain is orders
of magnitude simpler than mammals, they are still able
to show impressive behaviors: they can count, decide even
in front of dilemmas, implement a successful homing even
after exploring complex terrains, and learn such complex
concepts as sameness and difference, besides showing those
well-known impressive adaptive motion capabilities [1]. A
large part of the insect brain of the drosophila melanogaster
was recently identified, and powerful genetic tools are available
to control the spiking activity of specific neurons within
the fly brain, to identify their role within the expected behavioral
responses.
A relevant aspect accompanying the modeling phase is related
to the methodologies for extracting neural information.
Several methods are available, among which direct neuron
electrophysiology and fluorescence image analysis after genetic
manipulation [2], [3]. The former approach, although
allowing direct measurement of neural activity, has two main
IEEE Instrumentation & Measurement Magazine
1094-6969/22/$25.00©2022IEEE
19

Instrumentation & Measurement Magazine 25-9

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