|dc.description.abstract||The cerebral cortex of mammals comprises a series of topographic maps, forming sensory
and motor areas such as those in the visual, auditory, and somatosensory systems.
Understanding the rules that govern the development of these maps and how this topographic
organization relates to information processing is critical for the understanding
of cortical processing and whole brain function.
Previous computational models have shown that topographic maps can develop through
a process of self-organization, if spatially localized patches of cortical neurons are activated
by particular stimuli. This thesis presents a series of computational models,
based on this principle of self-organization, that focus on the development of the map
of orientation preference in primary visual cortex (V1). This map is the prototypical
example of topographic map development in the brain, and is the most widely studied,
however the same self-organizing principles can also apply to maps of many other
visual features and maps in many other sensory areas.
Experimental evidence indicates that orientation preference maps in V1 develop in a
stable way, with the initial layout determined before eye opening. This constraint is at
odds with previous self-organizing models, which have used biologically unfounded
ad-hoc methods to obtain robust and reliable development. Such mechanisms inherently
lead to instability, by causing massive reorganization over time. The first model
presented in this thesis (ALISSOM) shows how ad-hoc methods can be replaced with
biologically realistic homeostatic mechanisms that lead to development that is both robust
and stable. This model shows for the first time how orientation maps can remain
stable despite the massive circuit reconstruction and change in visual inputs occurring
during development. This model also highlights the requirements for homeostasis in
the developing visual circuit.
A second model shows how this development can occur using circuitry that is consistent
with the known wiring in V1, unlike previous models. This new model, LESI, contains
Long-range Excitatory and Short-range Inhibitory connections between model
neurons. Instead of direct long-range inhibition, it uses di-synaptic inhibition to ensure
that when visual stimuli are at high contrast, long-range excitatory connections
have an overall inhibitory influence. The results match previous models in the special
case of the high-contrast inputs that drive development most strongly, but show how
the behavior relates to the underlying circuitry, and also make it possible to explore effects at a wide range of contrasts.
The final part of this thesis explores the differences between rodents and higher mammals
that lead to the lack of topographic organization in rodent species. A lack of
organization for orientation also implies local disorder in retinotopy, and analysis of
retinotopy data from two-photon calcium imaging in mouse (provided by Tom Mrsic-
Flogel, University College London) confirms this hypothesis. A self-organizing model
is used to investigate how this disorder can arise via variation in either feed-forward
connections to V1 or lateral connections within V1, and how the effects of disorder
may vary between species. These results suggest that species with and without topographic
maps implement similar visual algorithms differing only in the values of some
key parameters, rather than having fundamental differences in architecture.
Together, these results help us understand how and why neurons develop preferences
for visual features such as orientation, and how maps of these neurons are formed.
The resulting models represent a synthesis of a large body of experimental evidence
about V1 anatomy and function, and offer a platform for developing a more complete
explanation of cortical function in future work.||en