center-surround interactions

Motion-based prediction is sufficient to solve the aperture problem

In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an …

Probabilistic models of the low-level visual system: the role of prediction in detecting motion

Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual system could describe superposed and ambiguous information. This allows to describe the interactions of neighboring …

Functional consequences of correlated excitatory and inhibitory conductances in cortical networks

Neurons in the neocortex receive a large number of excitatory and inhibitory synaptic inputs. Excitation and inhibition dynamically balance each other, with inhibition lagging excitation by only few milliseconds. To characterize the functional …

Models of low-level vision: linking probabilistic models and neural masses

see this more recent talk @ UCL, London

Dynamical emergence of a neural solution for motion integration

Based on Perrinet et al, 2012 See a followup in Khoei et al, 2013

Dynamical emergence of a neural solution for motion integration

Probabilistic models of the low-level visual system: the role of prediction in detecting motion

Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual system could describe superposed and ambiguous information. This allows to describe the interactions of neighboring …

Decoding center-surround interactions in population of neurons for the ocular following response

Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed to low-level motion processing such as spatial and temporal integration, contrast gain control and divisive …

Decoding the population dynamics underlying ocular following response using a probabilistic framework

The machinery behind the visual perception of motion and the subsequent sensorimotor transformation, such as in Ocular Following Response (OFR), is confronted to uncertainties which are efficiently resolved in the primate's visual system. We may …

Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework

Dynamical Neural Networks: modeling low-level vision at short latencies

The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in ocular following response (OFR), is confronted to uncertainties which are efficiently resolved in the primate's visual system. We may …

Modeling spatial integration in the ocular following response using a probabilistic framework

The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in Ocular Following Response (OFR), is confronted to uncertainties which are efficiently resolved in the primate's visual system. We may …

Network of integrate-and-fire neurons using Rank Order Coding A: how to implement spike timing dependant plasticity