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Abstract:
(Poster Presentation)
Introduction:
It has been suggested that the construction of computational
devices intended for the modelling of neurobiological processes,
including cognition, will be unsuccessful unless the issue of
physical implementation is addressed. This work focusses on the
implications for neuron or cell models. It is assumed that the
form and function of individual cells make a non-trivial
contribution to overall computational behaviour and that there
are cases in which it is appropriate to make a direct use of
physical variables by implementation.
Technologies and Equivalences:
The choice of substrate for implementation is constrained by the
requirement that an equivalence exist between the technology of
the substrate and the technology of the cell. Much has been said
about the status of various kinds of equivalences: physical,
algorithmic, computational. (Harnad, 1994, Marr, 1982) It is
argued here that the investment of time and resources required to
construct a physical implementation is only justified in cases
where a physical equivalence exists between the technology of the
substrate and the technology of the cell. This means that the
physics of the model cell must be identical to the physics of the
biological cell.
Neuromorphic Engineering:
A cell provides a barrier between the external world and its
internal world. An electrical potential difference arises across
the barrier and controls the diffusion of particles between
extra- and intracellular spaces. The potential alters in response
to changes in physicochemical variables. These alterations are
the basis for information encoding and transmission in the
nervous system. Likewise, a field-effect transistor provides a
barrier between source and drain regions. The gate terminal
controls the diffusion of particles between these regions. The
physics in both systems is described by Boltzmann's distribution.
The equivalence between the two systems was first noted by Mead
(1989). This means that cellular computation can be constructed
from the intrinsic properties of transistors and the materials
from which they are composed. Neuromorphic engineering uses this
technology to model cells, networks and whole sensorimotor
systems. The technology is also capable of providing an interface
with the external world and with other biological elements. Cell
membranes are used interchangeably with the gates of transistors
to create interfaces between the technologies of biology and
analogue devices. (Fromherz & Stett, 1995)
Conclusions:
The morphology of the cell can affect information encoding and
transmission. Physically implementing this morphology makes it
possible to explore the relationship between structure and
function in way that has explanatory and predictive ability.
Whilst this can be seen to have benefits for cellular
neuroscience, it is not obvious how this extends to cognitive
neuroscience. It may be necessary to decide whether it is more
useful to continue to shape the silicon technology in order to
closely model neurobiology, or to allow the properties of the
silicon technology to determine the evolution of the silicon
cells. (Etienne-Cummings et al., 1998, Sarpeshkar, 1997) This
decision may be influenced by whether the structural richness and
diversity of cells is seen as an explanation for the complexity
of cognitive processing or as compensation for other deficits,
such as the relatively slow speeds of transmission.
Acknowledgments:
This work is funded by the Gatsby Foundation.
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