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Another Example: Watts's Model of the Auditory Regions
I believe that the way to create a brain-like intelligence is to build a real-time working model system, accurate
in sufficient detail to express the essence of each computation that is being performed, and verify its correct
operation against measurements of the real system. The model must run in real-time so that we will be forced
to deal with inconvenient and complex real-world inputs that we might not otherwise think to present to it.
The model must operate at sufficient resolution to be comparable to the real system, so that we build the right
intuitions about what information is represented at each stage. Following Mead,
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the model development
necessarily begins at the boundaries of the system (i.e., the sensors) where the real system is well-understood,
and then can advance into the less-understood regions....In this way, the model can contribute fundamentally
to our advancing understanding of the system, rather than simply mirroring the existing understanding. In the
context of such great complexity, it is possible that the only practical way to understand the real system is to
build a working model, from the sensors inward, building on our newly enabled ability to
visualize the
complexity of the system
as we advance into it. Such an approach could be called
reverse-engineering of the
brain
....Note that I am not advocating a blind copying of structures whose purpose we don't understand, like
the legendary Icarus who naively attempted to build wings out of feathers and wax. Rather, I am advocating
that we respect the complexity and richness that is already well-understood at low levels, before proceeding
to higher levels.
—L
LOYD
W
ATTS
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A major example of neuromorphic modeling of a region of the brain is the comprehensive replica of a significant
portion of the human auditory-processing system developed by Lloyd Watts and his colleagues.
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It is based on
neurobiological studies of specific neuron types as well as on information regarding interneuronal connection. The
model, which has many of the same properties as human hearing and can locate and identify sounds, has five parallel
paths of processing auditory information and includes the actual intermediate representations of this information at
each stage of neural processing. Watts has implemented his model as real-time computer software which, though a
work in progress, illustrates the feasibility of converting neurobiological models and brain connection data into
working simulations. The software is not based on reproducing each individual neuron and connection, as is the
cerebellum model described above, but rather the transformations performed by each region.
Watts's software is capable of matching the intricacies that have been revealed in subtle experiments on human
hearing and auditory discrimination. Watts has used his model as a preprocessor (front end) in speech-recognition
systems and has demonstrated its ability to pick out one speaker from background sounds (the "cocktail party effect").
This is an impressive feat of which humans are capable but up until now had not been feasible in automated speech-
recognition systems.
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Like human hearing, Watts's cochlea model is endowed with spectral sensitivity (we hear better at certain
frequencies), temporal responses (we are sensitive to the timing of sounds, which create the sensation of their spatial
locations), masking, nonlinear frequency-dependent amplitude compression (which allows for greater dynamic
range—the ability to hear both loud and quiet sounds), gain control (amplification), and other subtle features. The
results it obtains are directly verifiable by biological and psychophysical data.
The next segment of the model is the cochlear nucleus, which Yale University professor of neuroscience and
neurobiology Gordon M. Shepherd
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has described as "one of the best understood regions of the brain."
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Watts's
simulation of the cochlear nucleus is based on work by E. Young that describes in detail "the essential cell types
responsible for detecting spectral energy, broadband transients, fine tuning in spectral channels, enhancing sensitivity
to temporary envelope in spectral channels, and spectral edges and notches, all while adjusting gain for optimum
sensitivity within the limited dynamic range of the spiking neural code.
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The Watts model captures many other details, such as the interaural time difference (lTD) computed by the medial
superior olive cells.
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It also represents the interaural-level difference (ILD) computed by the lateral superior olive
cells and normalizations and adjustments made by the inferior colliculus cells.
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