Stimulus-invariant aspects of the retinal code drive discriminability of natural scenes.

Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells (RGCs), less is known about how populations form both flexible and reliable encoding in natural moving scenes.

Encoding surprise by retinal ganglion cells.

The efficient coding hypothesis posits that early sensory neurons transmit maximal information about sensory stimuli, given internal constraints. A central prediction of this theory is that neurons should preferentially encode stimuli that are most surprising. Previous studies suggest this may be the case in early visual areas, where many neurons respond strongly to rare or surprising stimuli.

Optogenetic targeting of AII amacrine cells restores retinal computations performed by the inner retina.

Most inherited retinal dystrophies display progressive photoreceptor cell degeneration leading to severe visual impairment. Optogenetic reactivation of inner retinal neurons is a promising avenue to restore vision in retinas having lost their photoreceptors. Expression of optogenetic proteins in surviving ganglion cells, the retinal output, allows them to take on the lost photoreceptive function.

Small-correlation expansion to quantify information in noisy sensory systems.

Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with a complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations.

Stimulus invariant aspects of the retinal code drive discriminability of natural scenes.

Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells, less is known about how populations form both flexible and reliable encoding in natural moving scenes.

Learning low-dimensional generalizable natural features from retina using a U-net.

Much of sensory neuroscience focuses on presenting stimuli that are chosen by the experimenter because they are parametric and easy to sample and are thought to be behaviorally relevant to the organism. However, it is not generally known what these relevant features are in complex, natural scenes. This work focuses on using the retinal encoding of natural movies to determine the presumably behaviorally-relevant features that the brain represents.

Ectopic expression of a mechanosensitive channel confers spatiotemporal resolution to ultrasound stimulations of neurons for visual restoration.

Remote and precisely controlled activation of the brain is a fundamental challenge in the development of brain-machine interfaces for neurological treatments. Low-frequency ultrasound stimulation can be used to modulate neuronal activity deep in the brain, especially after expressing ultrasound-sensitive proteins. But so far, no study has described an ultrasound-mediated activation strategy whose spatiotemporal resolution and acoustic intensity are compatible with the mandatory needs of brain-machine interfaces, particularly for visual restoration.

Mice Lacking with Complete Congenital Stationary Night Blindness Are a Good Model for Myopia.

Mutations in are one of the most common causes of autosomal recessive complete congenital stationary night blindness (cCSNB). This retinal disease is characterized in patients by impaired dim and night vision, associated with other ocular symptoms, including high myopia. cCSNB is caused by a complete loss of signal transmission from photoreceptors to ON-bipolar cells.

Context-dependent selectivity to natural images in the retina.

Retina ganglion cells extract specific features from natural scenes and send this information to the brain. In particular, they respond to local light increase (ON responses), and/or decrease (OFF). However, it is unclear if this ON-OFF selectivity, characterized with synthetic stimuli, is maintained under natural scene stimulation.

All-optical inter-layers functional connectivity investigation in the mouse retina.

We developed a multi-unit microscope for all-optical inter-layers circuits interrogation. The system performs two-photon (2P) functional imaging and 2P multiplexed holographic optogenetics at axially distinct planes. We demonstrated the capability of the system to map, in the mouse retina, the functional connectivity between rod bipolar cells (RBCs) and ganglion cells (GCs) by activating single or defined groups of RBCs while recording the evoked response in the GC layer with cell-type specificity and single-cell resolution.

Inferring the function performed by a recurrent neural network.

A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems.

Predicting synchronous firing of large neural populations from sequential recordings.

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area.

Optogenetic therapy: high spatiotemporal resolution and pattern discrimination compatible with vision restoration in non-human primates.

Vision restoration is an ideal medical application for optogenetics, because the eye provides direct optical access to the retina for stimulation. Optogenetic therapy could be used for diseases involving photoreceptor degeneration, such as retinitis pigmentosa or age-related macular degeneration. We describe here the selection, in non-human primates, of a specific optogenetic construct currently tested in a clinical trial.

Towards optogenetic vision restoration with high resolution.

In many cases of inherited retinal degenerations, ganglion cells are spared despite photoreceptor cell death, making it possible to stimulate them to restore visual function. Several studies have shown that it is possible to express an optogenetic protein in ganglion cells and make them light sensitive, a promising strategy to restore vision. However the spatial resolution of optogenetically-reactivated retinas has rarely been measured, especially in the primate.

A biophysical model explains the spontaneous bursting behavior in the developing retina.

During early development, waves of activity propagate across the retina and play a key role in the proper wiring of the early visual system. During a particular phase of the retina development (stage II) these waves are triggered by a transient network of neurons, called Starburst Amacrine Cells (SACs), showing a bursting activity which disappears upon further maturation. The underlying mechanisms of the spontaneous bursting and the transient excitability of immature SACs are not completely clear yet.

Modeling the Correlated Activity of Neural Populations: A Review.

The principles of neural encoding and computations are inherently collective and usually involve large populations of interacting neurons with highly correlated activities. While theories of neural function have long recognized the importance of collective effects in populations of neurons, only in the past two decades has it become possible to record from many cells simultaneously using advanced experimental techniques with single-spike resolution and to relate these correlations to function and behavior. This review focuses on the modeling and inference approaches that have been recently developed to describe the correlated spiking activity of populations of neurons.

A Simple Model for Low Variability in Neural Spike Trains.

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking.

Maximum-entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity.

Maximum entropy models can be inferred from large datasets to uncover how collective dynamics emerge from local interactions. Here, such models are employed to investigate neurons recorded by multi-electrode arrays in the human and monkey cortex. Taking advantage of the separation of excitatory and inhibitory neuron types, we construct a model including this distinction.

Nonlinear decoding of a complex movie from the mammalian retina.

Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed "pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs.

A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo.

In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data.

Blindfold learning of an accurate neural metric.

The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells.