How do circuits in the mammalian cerebral cortex encode properties of the sensory
environment in a way that can drive adaptive behavior? This question is fundamental
to neuroscience, but it has been very difficult to approach directly. Various computational
and theoretical models can explain a wide range of phenomena observed in the
primary visual cortex (V1), including the anatomical organization of its circuits, the
development of functional properties like orientation tuning, and behavioral effects
like surround modulation. However, so far no model has been able to bridge these
levels of description to explain how the machinery that develops directly affects behavior.
Bridging these levels is important, because phenomena at any one specific
level can have many possible explanations, but there are far fewer possibilities to
consider once all of the available evidence is taken into account.
In this thesis we integrate the information gleaned about cortical development, circuit
and cell-type specific interactions, and anatomical, behavioral and electrophysiological
measurements, to develop a computational model of V1 that is constrained
enough to make predictions across multiple levels of description. Through a series
of models incorporating increasing levels of biophysical detail and becoming increasingly
better constrained, we are able to make detailed predictions for the types of
mechanistic interactions required for robust development of cortical maps that have
a realistic anatomical organization, and thereby gain insight into the computations
performed by the primary visual cortex.
The initial models focus on how existing anatomical and electrophysiological knowledge
can be integrated into previously abstract models to give a well-grounded and
highly constrained account of the emergence of pattern-specific tuning in the primary
visual cortex. More detailed models then address the interactions between specific
excitatory and inhibitory cell classes in V1, and what role each cell type may play
during development and function. Finally, we demonstrate how these cell classes
come together to form a circuit that gives rise not only to robust development but
also the development of realistic lateral connectivity patterns. Crucially, these patterns
reflect the statistics of the visual environment to which the model was exposed
during development. This property allows us to explore how the model is able to
capture higher-order information about the environment and use that information to
optimize neural coding and aid the processing of complex visual tasks.
Using this model we can make a number of very specific predictions about the
mechanistic workings of the brain. Specifically, the model predicts a crucial role of
parvalbumin-expressing interneurons in robust development and divisive normalization,
while it implicates somatostatin immunoreactive neurons in mediating longer
range and feature-selective suppression. The model also makes predictions about the
role of these cell classes in efficient neural coding and under what conditions the
model fails to organize. In particular, we show that a tight coupling of activity between
the principal excitatory population and the parvalbumin population is central
to robust and stable responses and organization, which may have implications for
a variety of diseases where parvalbumin interneuron function is impaired, such as
schizophrenia and autism. Further the model explains the switch from facilitatory to
suppressive surround modulation effects as a simple by-product of the facilitating
response function of long-range excitatory connections targeting a specialized class
of inhibitory interneurons. Finally, the model allows us to make predictions about the
statistics that are encoded in the extensive network of long-range intra-areal connectivity
in V1, suggesting that even V1 can capture high-level statistical dependencies
in the visual environment.
The final model represents a comprehensive and well constrained model of the
primary visual cortex, which for the first time can relate the physiological properties
of individual cell classes to their role in development, learning and function. While
the model is specifically tuned for V1, all mechanisms introduced are completely
general, and can be used as a general cortical model, useful for studying phenomena
across the visual cortex and even the cortex as a whole. This work is also highly
relevant for clinical neuroscience, as the cell types studied here have been implicated
in neurological disorders as wide ranging as autism, schizophrenia and Parkinson’s
disease
