Building a mechanistic model of the development and function of the primary visual cortex (original) (raw)
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The development of topography in the visual cortex: a review of models
Network: Computation in Neural Systems, 1996
The repetitive stochastic patterns of eye dominance and orientation preference found in the mammalian visual cortex have attracted much attention from theoretical neurobiologists during the last two decades. Reasons for this include the visually intriguing nature of the patterns and the fact that many aspects of their development seem likely to be dependent upon both spontaneous and visually driven patterns of neural activity. Understanding these processes holds out the promise that general theories of learning and memory may be derived from those found to be applicable to the visual cortex. It has turned out, in fact, that remarkably simple models, based on Hebbian synaptic plasticity, intracortical interactions and competitive interactions between cells and growing axons, have been able to explain much of the phenomenology. This article reviews the models of topographic organization in the visual cortex in a roughly historical sequence, beginning with von der Malsburg's paper 1973 paper in Kybernetik on selforganization of orientation selectivity. The principles on which each of the models is based are explained, and the plausibility of each model and the extent to which it is able to account for the relevant experimental data are evaluated. Attention is drawn to the underlying similarities and differences between the models and suggestions are made for future directions in research.
Journal of Neuroscience, 2013
Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be stable, in that the earliest measurable maps are similar in form to the eventual adult map, robust, in that similar maps develop in both dark rearing and in a variety of normal visual environments, and yet adaptive, in that the final map pattern reflects the statistics of the specific visual environment. How can these three properties be reconciled? Using mechanistic models of the development of neural connectivity in V1, we show for the first time that realistic stable, robust, and adaptive map development can be achieved by including two low-level mechanisms originally motivated from single-neuron results. Specifically, contrast-gain control in the retinal ganglion cells and the lateral geniculate nucleus reduces variation in the presynaptic drive due to differences in input patterns, while homeostatic plasticity of V1 neuron excitability reduces the postsynaptic variability in firing rates. Together these two mechanisms, thought to be applicable across sensory systems in general, lead to biological maps that develop stably and robustly, yet adapt to the visual environment. The modeling results suggest that topographic map stability is a natural outcome of low-level processes of adaptation and normalization. The resulting model is more realistic, simpler, and far more robust, and is thus a good starting point for future studies of cortical map development.
Development of Maps of Simple and Complex Cells in the Primary Visual Cortex
Frontiers in Computational Neuroscience, 2011
- pointed out, these three classes of models are becoming remarkably similar to each other as additional connectivity is being added to account for new physiological constraints. Regardless of which of the three classes of models is closest to reality, a fundamental question still remains largely unanswered: How does the specific and precise circuitry (assumed by each of these theories) develop? One possible explanation is that initially homogeneous or stochastic cortical connectivity can be modified by activity dependent-mechanisms in such a way that, over time, adult connectivity patterns emerge (von der Malsburg, 1973;. This selforganization can be driven by intrinsic spontaneous neural activity, external visual stimuli, or both.
A laminar V1 model consisting of layers 4 and 2/3 is presented. The model is in line with the modular structure of the neocortex and addresses development of the horizontal connections within the V1. Later, the functional roles of the horizontal connections are addressed. Layer 4 model exhibits sharp orientation selectivity despite poorly tuned LGN input. Sharp orientation selectivity is achieved through (i) control of the total activity of the modeled hypercolumns by normalization inhibition, and (ii) providing layer 4 units with ...
Representation and adaptation in the primary visual cortex
2005
We investigate the processing of visual stimuli in local networks of the primary visual cortex. Cortical cells can display highly stereotypical behavior for some aspects of a stimulus, but show considerable plasticity with respect to others. Here, we investigate both, how neurons in the primary visual cortex achieve the stable representation of oriented contours and the cause of their adaptation to prolonged stimuli. The formation of orientation tuned responses is one of the best-explored features of cells in the primary visual cortex and serves as a model problem for understanding cortical circuitry and computation. Yet, the detailed mechanisms of integration of various inputs to a cell that give rise to the orientation selective responses remain unclear. Novel intracellular recordings of conductances in cortical neurons in the primary visual cortex in vivo by our collaborators, which take into account the recorded cell’s location within the orientation preference map, have charact...
Modeling the development of organization for orientation preference in primary visual cortex
2009
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 fea...
Cerebral Cortex, 2003
How is development of cortical maps in V1 coordinated across cortical layers to form cortical columns? Previous neural models propose how maps of orientation (OR), ocular dominance (OD), and related properties develop in V1. These models show how spontaneous activity, before eye opening, combined with correlation learning and competition, can generate maps similar to those found in vivo. These models have not discussed laminar architecture or how cells develop and coordinate their connections across cortical layers. This is an important problem since anatomical evidence shows that clusters of horizontal connections form, between iso-oriented regions, in layer 2/3 before being innervated by layer 4 afferents. How are orientations in different layers aligned before these connections form? Anatomical evidence demonstrates that thalamic afferents wait in the subplate for weeks before innervating layer 4. Other evidence shows that ablation of the cortical subplate interferes with the development of OR and OD columns. The model proposes how the subplate develops OR and OD maps, which then entrain and coordinate the development of maps in other lamina. The model demonstrates how these maps may develop in layer 4 by using a known transient subplate-to-layer 4 circuit as a teacher. The model subplate also guides the early clustering of horizontal connections in layer 2/3, and the formation of the interlaminar circuitry that forms cortical columns. It is shown how layer 6 develops and helps to stabilize the network when the subplate atrophies. Finally the model clarifies how brain-derived neurotrophic factor (BDNF) manipulations may influence cortical development. Rationale and Methods This section summarizes key properties that the model clarifies of the laminar organization of cortex, the cortical subplate, orientation tuning, ocular dominance columns, and clustered horizontal connections. The
The mechanism or microcircuitry behind orientation selectivity in primary visual cortex (V1), and the means by which it develops without supervision or visual input, both remain unresolved questions. Work on the developmental question has assumed the prevalent spatial convergence model of orientation selectivity as the target mechanism. Encouraged by growing evidence challenging both the completeness of this model and its developmental viability, we investigated an alternative scheme. Accordingly, we demonstrate computationally how a scheme in which orientation selectivity originates from the orientation biases already in the retina and lateral geniculate nucelus (LGN) can answer both the mechanistic and developmental questions. In this scheme, the divergence of outputs from the retina allows retinal spontaneous activity to create correlations within the LGN. These correlations in turn allow a Hebbian plasticity mechanism to strengthen those LGN inputs to V1 which carry similar orie...
A comprehensive data-driven model of cat primary visual cortex
bioRxiv (Cold Spring Harbor Laboratory), 2018
Systems neuroscience has produced an extensive body of evidence on the anatomy and function of mammalian visual cortex, but the transformation of this knowledge into a coherent understanding of cortical computation in early visual processing has been limited. Computational modeling can integrate such fragmented data into models that satisfy the broad range of constraints imposed by experiments, hence advancing our understanding of their computational role. However, such integrative modeling efforts have been scarce and mostly unsystematic. To address this issue here we present a comprehensive multi-scale spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under wide range of visual input statistics. The model reproduces a number of experimentally identified properties including: self-generated asynchronous irregular ongoing activity, realistic interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code. This data-driven model offers numerous insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception.