Biology subjects
Publications and source records attributed to Cessac, B..
Using simulation and a simple mathematical argument we argue that the refresh rate of overhead projectors, used in experiments on the visual system, may impact the perception of fast moving objects at the retinal and cortical level (V1), and thereby at the level of psychophysics.
Motion is an important feature of visual scenes and retinal neuronal circuits selectively signal different motion features. It has been shown that the retina can extrapolate the position of a moving object, thereby compensating sensory transmission delays and enabling signal processing in real-time. Amacrine cells, the inhibitory interneurons of the retina, play essential roles in such computations although their precise function remain unclear. Here, we computationally explore the effect of two different inhibitory connectivity motifs on the retinas response to moving objects: feed-forward and recurrent feed-back inhibition. We show that both can account for motion anticipation with two different mechanisms. Feed-forward inhibition truncates motion responses and shifts peak responses forward via subtractive inhibition, whereas recurrent feedback coupling evokes, via divisive inhibition, excitatory and inhibitory waves with different phases that add up and shift the response peak. A key difference between the two mechanisms is how the peak response scales with the speed of a moving object. Motion prediction with feedforward circuits monotonically decreases with increasing speeds, while recurrent feedback coupling induces tuning curves that exhibit a preferred speed for which motion prediction is maximal.
A fundamental task for the brain is to generate predictions of future sensory inputs, and signal errors in these predictions. Many neurons have been shown to signal omitted stimuli during periodic stimulation, even in the retina. However, the mechanisms of this error signaling are unclear. Here we show that depressing inhibitory synapses enable the retina to signal an omitted stimulus in a flash sequence. While ganglion cells, the retinal output, responded to an omitted flash with a constant latency over many frequencies of the flash sequence, we found that this was not the case once inhibition was blocked. We built a simple circuit model and showed that depressing inhibitory synapses were a necessary component to reproduce our experimental findings. We also generated new predictions with this model, that we confirmed experimentally. Depressing inhibitory synapses could thus be a key component to generate the predictive responses observed in many brain areas.
Retinal neurons are remarkedly diverse based on structure, function and genetic identity. Classifying these cells is a challenging task, requiring multimodal methodology. Here, we introduce a novel approach for retinal ganglion cell (RGC) classification, based on pharmacogenetics combined with immunohistochemistry and large-scale retinal electrophysiology. Our novel strategy allows grouping of cells sharing gene expression and understanding how these cell classes respond to basic and complex visual scenes. Our approach consists of several consecutive steps. First, the spike firing frequency is increased in RGCs co-expressing a certain gene (Scnn1a or Grik4) using excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) in order to single out activity originating specifically from these cells. Their spike location is then combined with post hoc immunostaining, to unequivocally characterize their anatomical and functional features. We grouped these isolated RGCs into multiple clusters based on spike train similarities. Using this novel approach. we were able to extend the pre-existing list of Grik4 expressing RGC types to a total of 8 and, for the first time, we provide a phenotypical description of 13 Scnn1a-expressing RGCs. The insights and methods gained here can guide not only RGC classification but neuronal classification challenges in other brain regions as well.
Chronic pain is a global healthcare problem with a huge societal impact. Its management remains unsatisfactory, with no single treatment clinically approved in most cases. In this study we use an in vitro experimental model of erythromelalgia consisting of sensory neurons derived from human induced pluripotent stem cells obtained from a patient (carrying the mutation F1449V) and a control subject. We combine neurophysiology and computational modelling to focus on the Nav1.7 voltage gated sodium channel, which acts as an amplifier of the receptor potential in nociceptive neurons and plays a critical role in erythromelalgia due to gain of function mutations causing the channel to open with smaller depolarisations. Using multi-electrode array (extracellular) recordings, we found that the scorpion toxin OD1 increases the excitability of sensory neurons in cultures obtained from the control donor, evidenced by increased spontaneous spike rate and amplitude. In erythromelalgia cultures, the application of the Nav1.7 blocker PF-05089771 effectively stopped spontaneous firing. These results, which are in accordance with current clamp and voltage clamp recordings reported in the literature, are explained with a conductance-based computational model of a single human nociceptive neuron. The disease was simulated through a decrease of the Nav1.7 half activation voltage, which decreased the rheobase and increased the response to supra threshold depolarizing currents. This enhanced response could be successfully supressed by blocking the Nav1.7 channels. The painful effects of OD1 were simulated through a slower establishment and a quicker removal of Nav1.7 inactivation, reproducing the effects of the toxin on the spike frequency and amplitude. Our model simulations suggest that the increase in extracellular spike amplitude observed in the MEA after OD1 treatment can be due mainly to a slope increase in the ascending phase of the intracellular spike caused by impaired inactivation gating.
This manuscript has been withdrawn by the authors as they no longer stand by part of their conclusions and wish not to be cited for this work. If you have any questions, please contact the corresponding authors.
Maximum entropy models (MEM) have been widely used in the last 10 years to characterize the statistics of networks of spiking neurons. A major drawback of this approach is that the number of parameters used in the statistical model increases very fast with the network size, hindering its interpretation and fast computation. Here, we present a novel framework of dimensionality reduction for generalized MEM handling spatio-temporal correlations. This formalism is based on information geometry where a MEM is a point on a large-dimensional manifold. We exploit the geometrical properties of this manifold in order to find a projection on a lower dimensional space that best captures the high-order statistics. This allows us to define a quantitative criterion that we call the \"degree of compressibility\" of the neuronal code. A powerful aspect of this method is that it does not require fitting the model. Indeed, the matrix defining the metric of the manifold is computed directly via the data without parameters fitting. The method is first validated using synthetic data generated by a known statistics. We then analyze a MEM having more parameters than the underlying data statistics and show that our method detects the extra dimensions. We then test it on experimental retinal data. We record retinal ganglion cells (RGC) spiking data using multi-electrode arrays (MEA) under different visual stimuli: spontaneous activity, white noise stimulus, and natural scene. Using our method, we report a dimensionality reduction up to 50% for retinal data. As we show, this is quite a huge reduction compared to a randomly generated spike train, suggesting that the neuronal code, in these experiments, is highly compressible. This additionally shows that the dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics by modifying the level of redundancy.\n\nAuthor SummaryMaximum entropy models (MEM) have been widely used to characterize the statistics of networks of spiking neurons. However, as the network size increases, the number of model parameters increases rapidly, hindering its interpretation and fast computation. Here, we propose a method to evaluate the dimensionality reduction of MEM, based on the geometrical properties of the manifold best capturing the network high-order statistics. Our method is validated with synthetic data using independent or correlated neural responses. Importantly, we show that dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics modifying the level of redundancy.