Biomedical Engineering Reference
In-Depth Information
References
continuously adapted. In all the experiments,
the position of the speaker was fixed and the
microphone array was rotated in steps of the
desired angle. For each angular orientation of
the source, 10 sets of measurements (
W
11
,
W
21
,
and
W
22
) were recorded every 80 ms using a
field-programmable gate array. These sets were
then used to estimate the bearing. The mea-
sured response shown in
Figure 2.10
demon-
strates a resolution of 2° that is similar to or
better than the localization capability of the
parasitoid fly. For all the experiments, the min-
iaturized microphone array consumes only a
few microwatts of power, which is also compa-
rable to the energy efficiency of the parasitoid
fly's localization apparatus.
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2.7 CONCLUSIONS
In this chapter, we described two important
noise exploitation and adaptation mechanisms
observed in neurobiology: (a) stochastic reso-
nance and (b) noise shaping. We argued the
importance of synaptic plasticity in its role in
noise exploitation and signal enhancement. The
concepts of stochastic resonance, noise shaping,
and adaptation (synaptic plasticity) have been
integrated into a unified algorithmic framework
called
ΣΔ
learning. As a case study, we described
the application of the
ΣΔ
learning framework
toward the design of a miniature acoustic source
localizer that mimics the response of a parasitoid
fly (
O. ochracea
). This case study illustrates one
specific example, and the future challenge lies in
extending the framework to more complex sen-
sory tasks such as recognition and perception.
In this regard, the objective will be to apply the
noise exploitation and adaptation concepts to
emerging devices such as the memristor, which
provides a compact apparatus for storing syn-
aptic weights. Furthermore, extensive simula-
tion and emulation studies need to be conducted
that can verify the scalability of the
ΣΔ
learning
framework.
