| Citekey |
Title |
| Adams12 |
Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia |
| Barthelemy07 |
Dynamics of distributed 1D and 2D motion representations for short-latency ocular following |
| Bogadhi10 |
Pursuing motion illusions: a realistic oculomotor framework for Bayesian inference |
| Cessac07 |
Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision |
| CristobalPerrinetKeil15bicv_chap1 |
Introduction |
| CristobalPerrinetKeil15bicv |
Biologically Inspired Computer Vision |
| Damasse18 |
Reinforcement effects in anticipatory smooth eye movements |
| Daucé10 |
Computational Neuroscience, from Multiple Levels to Multi-level |
| Davison08 |
PyNN: A common interface for neuronal network simulators |
| Fischer07cv |
Self-invertible 2D log-Gabor wavelets |
| Fischer07 |
Sparse approximation of images inspired from the functional architecture of the primary visual areas |
| Friston12 |
Perceptions as Hypotheses: Saccades as Experiments |
| Kaplan13 |
Anisotropic connectivity implements motion-based prediction in a spiking neural network |
| KaplanKhoei14 |
Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network |
| Khoei13jpp |
Role of motion-based prediction in motion extrapolation |
| KhoeiMassonPerrinet17 |
The flash-lag effect as a motion-based predictive shift |
| Kremkow10jcns |
Functional consequences of correlated excitatory and inhibitory conductances in cortical networks |
| Kremkow16 |
Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1 |
| Masson12 |
The behavioral receptive field underlying motion integration for primate tracking eye movements |
| Montagnini07 |
Bayesian modeling of dynamic motion integration |
| Montagnini15bicv |
Visual motion processing and human tracking behavior |
| Nava13 |
Advances in Texture Analysis for Emphysema Classification |
| Perrinet02sparse |
Sparse spike coding in an asynchronous feed-forward multi-layer neural network using Matching Pursuit |
| Perrinet02stdp |
Coherence detection in a spiking neuron via hebbian learning |
| Perrinet03ieee |
Coding static natural images using spiking event times : do neurons cooperate? |
| Perrinet03 |
Emergence of filters from natural scenes in a sparse spike coding scheme |
| Perrinet04nc |
Finding Independent Components using spikes : a natural result of hebbian learning in a sparse spike coding scheme |
| Perrinet04tauc |
Feature detection using spikes : the greedy approach |
| Perrinet06 |
Dynamical Neural Networks: modeling low-level vision at short latencies |
| Perrinet07neurocomp |
Modeling spatial integration in the ocular following response using a probabilistic framework |
| Perrinet10shl |
Role of homeostasis in learning sparse representations |
| Perrinet12pred |
Motion-based prediction is sufficient to solve the aperture problem |
| Perrinet15bicv |
Sparse Models for Computer Vision |
| Perrinet15eusipco |
Sparse coding of natural images using a prior on edge co-occurences |
| Perrinet16EUVIP |
Biologically-inspired characterization of sparseness in natural images |
| PerrinetAdamsFriston14 |
Active Inference, tracking eye movements and oculomotor delays |
| PerrinetBednar15 |
Edge co-occurrences can account for rapid categorization of natural versus animal images |
| Sanz12 |
Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception |
| Simoncini12 |
More is not always better: dissociation between perception and action explained by adaptive gain control |
| Taouali15 |
Testing the Odds of Inherent versus Observed Overdispersion in Neural Spike Counts |
| Voges10neurocomp |
Phase space analysis of networks based on biologically realistic parameters |
| Voges12 |
Complex dynamics in recurrent cortical networks based on spatially realistic connectivities |