Ulisse Ferrari | bioRxiv Science

bioRxiv2016

Multiplexed computations in retinal ganglion cells of a single type

In the early visual system, cells of the same type perform the same computation in dierent places of the visual field. How these cells code together a complex visual scene is unclear. A common assumption is that cells of the same type will extract a single stimulus feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the rat retina, we show that a homogeneous population of fast OFF ganglion cells simultaneously encodes two radically different features of a visual scene. Cells close to a moving object code linearly for its position, while distant cells remain largely invariant to the objects position and, instead, respond non-linearly to changes in the objects speed. Cells switch between these two computations depending on the stimulus. We developed a quantitative model that accounts for this effect and identified a likely disinhibitory circuit that mediates it. Ganglion cells of a single type thus do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems.

Neuroscience↗

bioRxiv2015

Inferred Model of the Prefrontal Cortex Activity Unveils Cell Assemblies and Memory Replay

Cell assemblies are thought to be the units of information representation in the brain, yet their detection from experimental data is arduous. Here, we propose to infer effective coupling networks and model distributions for the activity of simultaneously recorded neurons in prefrontal cortex, during the performance of a decision-making task, and during preceding and following sleep epochs. Our approach, inspired from statistical physics, allows us to define putative cell assemblies as the groups of co-activated neurons in the models of the three recorded epochs. It reveals the existence of task-related changes of the effective couplings between the sleep epochs. The assemblies which strongly coactivate during the task epoch are found to replay during subsequent sleep, in correspondence to the changes of the inferred network. Across sessions, a variety of different network scenarios is observed, providing insight in cell assembly formation and replay.\n\nAuthor SummaryMemories are thought to be represented in the brain through groups of coactivating neurons, the so-called cell assemblies. We propose an approach to identify cell assemblies from multi-electrode recordings of neural activity in vivo, and apply it to the prefrontal cortex activity of a behaving rat. Our statistical physics inspired approach consists in inferring effective interactions between the recorded cells, which reproduce the correlations in their spiking activities. The analysis of the effective interaction networks and of the model distributions allows us to identify cell assemblies, which strongly co-activate when the rat is learning, and also during subsequent sleep. Our approach is thus capable of providing detailed insights in cell-assembly formation and replay, crucial for memory consolidation.

Neuroscience↗