center-surround interactions

A Behavioral Receptive Field for Ocular Following in Monkeys: Spatial Summation and Its Spatial Frequency Tuning
In human and non-human primates, reflexive tracking eye movements can be initiated at very short latency in response to a rapid shift …
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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 …
Jan 12, 2012
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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 …
Dec 17, 2010
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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 …
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Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
Models of low-level vision: linking probabilistic models and neural masses
see this more recent talk @ UCL, London
Jan 8, 2010
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Dynamical emergence of a neural solution for motion integration
Based on Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation. Preprint PDF Cite see follow-up on motion extrapolation: Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013).
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Dynamical emergence of a neural solution for motion integration
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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 …
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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 …
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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 …
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Decoding the population dynamics underlying ocular following response using a probabilistic framework
Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
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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 …
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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 …
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Modeling spatial integration in the ocular following response using a probabilistic framework
Network of integrate-and-fire neurons using Rank Order Coding A: how to implement spike timing dependant plasticity
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