Blindfold learning of an accurate neural metric
@article{Gardella2018BlindfoldLO, title={Blindfold learning of an accurate neural metric}, author={Christophe Gardella and Olivier Marre and Thierry Mora}, journal={Proceedings of the National Academy of Sciences}, year={2018}, volume={115}, pages={3267 - 3272} }
Significance To understand how neural signals code sensory stimuli, most approaches require knowing both the true stimulus and the neural response. The brain, however, only has access to the neural signals put out by sensory organs. How can it learn to relate neural responses to sensory stimuli, especially for responses to which it has never been exposed? Here we show how to solve this problem by building a metric on neural responses such that responses to the same stimulus are close. Although…
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References
SHOWING 1-10 OF 85 REFERENCES
Ruling out and ruling in neural codes
- Computer Science, BiologyProceedings of the National Academy of Sciences
- 2009
The results show that standard coarse coding (spike count coding) is insufficient; finer, more information-rich codes are necessary.
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
- Computer Science, PsychologyPLoS Comput. Biol.
- 2014
These evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task and propose an extension of “kernel analysis” that measures the generalization accuracy as a function of representational complexity.
Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding
- Computer SciencebioRxiv
- 2017
A method to characterize the sensitivity of the retinal network to perturbations of a stimulus is developed, and it is argued that a peak in the sensitivity is set to maximize information transmission.
Mapping a Complete Neural Population in the Retina
- BiologyThe Journal of Neuroscience
- 2012
This work shows that the combination of a large, dense multielectrode array and a novel, mostly automated spike-sorting algorithm allowed them to record simultaneously from a highly overlapping population of >200 ganglion cells in the salamander retina, allowing unprecedented access to the complete neural representation of visual information.
Decoding visual information from a population of retinal ganglion cells.
- Biology, Computer ScienceJournal of neurophysiology
- 1997
This work investigates how a time-dependent visual stimulus is encoded by the collective activity of many retinal ganglion cells, and shows that the optimal interpretation of aganglion cell's action potential depends strongly on the simultaneous activity of other nearby cells.
Distinct time scales in cortical discrimination of natural sounds in songbirds.
- Biology, PsychologyJournal of neurophysiology
- 2006
The existence of distinct time scales for temporal resolution and temporal integration is demonstrated and how they arise from cortical neural responses to complex dynamic sounds is explained.
Quality Time: Representation of a Multidimensional Sensory Domain through Temporal Coding
- BiologyThe Journal of Neuroscience
- 2009
It is found that for the more broadly tuned neurons in the NTS, the taste space is a systematic representation of the entire taste domain, and the way that taste quality is represented by the firing rate envelope is consistent across the population of cells.
Error-Robust Modes of the Retinal Population Code
- Biology, Computer SciencePLoS Comput. Biol.
- 2016
A novel statistical model is developed that decomposes the population response into modes and predicts the distribution of spiking activity in the ganglion cell population with high accuracy; it is found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons.
A thesaurus for a neural population code
- Computer Science, BiologyeLife
- 2015
This work uses models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry.
Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines
- Computer Science, BiologyArXiv
- 2017
Results show that binary states can encode the regularities associated to different stimuli, using both gratings and natural scenes as stimuli, and hidden variables encode interesting properties of retinal activity, interpreted as population receptive fields.