coding decoding

A dynamic model for decoding direction and orientation in macaque primary visual cortex
Testing the odds of inherent vs. observed overdispersion in neural spike counts
A dynamic model for decoding direction and orientation in macaque primary visual cortex
see a follow-up in this poster This is a followup in Perrinet et al, 2012
A dynamic model for decoding direction and orientation in macaque primary visual cortex
Sparse Coding Of Natural Images Using A Prior On Edge Co-Occurences
On overdispersion in neuronal evoked activity
see a follow-up in this publication
A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
see a follow-up in this publication
A Simple Model of Orientation Encoding Accounting For Multivariate Neural Noise
see a follow-up in this publication
Role of homeostasis in learning sparse representations
related publication : Laurent U Perrinet (2019). An adaptive homeostatic algorithm for the unsupervised learning of visual features. Vision. Preprint PDF Cite Code DOI
Role of homeostasis in learning sparse representations
Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
Probabilistic models of the low-level visual system: the role of prediction in detecting motion
See a followup in Perrinet et al, 2012
Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
Dynamical Neural Networks: modeling low-level vision at short latencies
Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in the emergence of functional computational properties. The definition and study of these models involves the cooperation of a large range of scientific fields from statistical physics, probabilistic modelling, neuroscience and psychology to control theory.
Feature detection using spikes : the greedy approach
Feature detection using spikes : the greedy approach
Coherence detection in a spiking neuron via Hebbian learning
Coherence detection in a spiking neuron via Hebbian learning