8 research outputs found
Dynamical mechanism for sharp orientation tuning in an integrate-and-fire model of a cortical hypercolumn
Orientation tuning in a ring of pulse-coupled integrate-and-fire (IF) neurons is analyzed in terms of spontaneous pattern formation. It is shown how the ring bifurcates from a synchronous state to a non-phase-locked state whose spike trains are characterized by clustered but irregular fluctuations of the interspike intervals (ISIs). The separation of these clusters in phase space results in a localized peak of activity as measured by the time-averaged firing rate of the neurons. This generates a sharp orientation tuning curve that can lock to a slowly rotating, weakly tuned external stimulus. Under certain conditions, the peak can slowly rotate even to a fixed external stimulus. The ring also exhibits hysteresis due to the subcritical nature of the bifurcation to sharp orientation tuning. Such behavior is shown to be consistent with a corresponding analog version of the IF model in the limit of slow synaptic interactions. For fast synapses, the deterministic fluctuations of the ISIs associated with the tuning curve can support a coefficient of variation of order unity.<br/
Illusions in the Ring Model of visual orientation selectivity
The Ring Model of orientation tuning is a dynamical model of a hypercolumn of
visual area V1 in the human neocortex that has been designed to account for the
experimentally observed orientation tuning curves by local, i.e.,
cortico-cortical computations. The tuning curves are stationary, i.e. time
independent, solutions of this dynamical model. One important assumption
underlying the Ring Model is that the LGN input to V1 is weakly tuned to the
retinal orientation and that it is the local computations in V1 that sharpen
this tuning. Because the equations that describe the Ring Model have built-in
equivariance properties in the synaptic weight distribution with respect to a
particular group acting on the retinal orientation of the stimulus, the model
in effect encodes an infinite number of tuning curves that are arbitrarily
translated with respect to each other. By using the Orbit Space Reduction
technique we rewrite the model equations in canonical form as functions of
polynomials that are invariant with respect to the action of this group. This
allows us to combine equivariant bifurcation theory with an efficient numerical
continuation method in order to compute the tuning curves predicted by the Ring
Model. Surprisingly some of these tuning curves are not tuned to the stimulus.
We interpret them as neural illusions and show numerically how they can be
induced by simple dynamical stimuli. These neural illusions are important
biological predictions of the model. If they could be observed experimentally
this would be a strong point in favour of the Ring Model. We also show how our
theoretical analysis allows to very simply specify the ranges of the model
parameters by comparing the model predictions with published experimental
observations.Comment: 33 pages, 12 figure
Bifurcation of hyperbolic planforms
Motivated by a model for the perception of textures by the visual cortex in
primates, we analyse the bifurcation of periodic patterns for nonlinear
equations describing the state of a system defined on the space of structure
tensors, when these equations are further invariant with respect to the
isometries of this space. We show that the problem reduces to a bifurcation
problem in the hyperbolic plane D (Poincar\'e disc). We make use of the concept
of periodic lattice in D to further reduce the problem to one on a compact
Riemann surface D/T, where T is a cocompact, torsion-free Fuchsian group. The
knowledge of the symmetry group of this surface allows to carry out the
machinery of equivariant bifurcation theory. Solutions which generically
bifurcate are called "H-planforms", by analogy with the "planforms" introduced
for pattern formation in Euclidean space. This concept is applied to the case
of an octagonal periodic pattern, where we are able to classify all possible
H-planforms satisfying the hypotheses of the Equivariant Branching Lemma. These
patterns are however not straightforward to compute, even numerically, and in
the last section we describe a method for computation illustrated with a
selection of images of octagonal H-planforms.Comment: 26 pages, 11 figure
Local/global analysis of the stationary solutions of some neural field equations
Neural or cortical fields are continuous assemblies of mesoscopic models,
also called neural masses, of neural populations that are fundamental in the
modeling of macroscopic parts of the brain. Neural fields are described by
nonlinear integro-differential equations. The solutions of these equations
represent the state of activity of these populations when submitted to inputs
from neighbouring brain areas. Understanding the properties of these solutions
is essential in advancing our understanding of the brain. In this paper we
study the dependency of the stationary solutions of the neural fields equations
with respect to the stiffness of the nonlinearity and the contrast of the
external inputs. This is done by using degree theory and bifurcation theory in
the context of functional, in particular infinite dimensional, spaces. The
joint use of these two theories allows us to make new detailed predictions
about the global and local behaviours of the solutions. We also provide a
generic finite dimensional approximation of these equations which allows us to
study in great details two models. The first model is a neural mass model of a
cortical hypercolumn of orientation sensitive neurons, the ring model. The
second model is a general neural field model where the spatial connectivity
isdescribed by heterogeneous Gaussian-like functions.Comment: 38 pages, 9 figure
Dynamical mechanism for sharp orientation tuning in an integrate-and-fire model of a cortical hypercolumn
Orientation tuning in a ring of pulse-coupled integrate-and-fire (IF) neurons is analyzed in terms of spontaneous pattern formation. It is shown how the ring bifurcates from a synchronous state to a non-phase-locked state whose spike trains are characterized by clustered but irregular fluctuations of the interspike intervals (ISIs). The separation of these clusters in phase space results in a localized peak of activity as measured by the time-averaged firing rate of the neurons. This generates a sharp orientation tuning curve that can lock to a slowly rotating, weakly tuned external stimulus. Under certain conditions, the peak can slowly rotate even to a fixed external stimulus. The ring also exhibits hysteresis due to the subcritical nature of the bifurcation to sharp orientation tuning. Such behavior is shown to be consistent with a corresponding analog version of the IF model in the limit of slow synaptic interactions. For fast synapses, the deterministic fluctuations of the ISIs associated with the tuning curve can support a coefficient of variation of order unity.<br/