Nested calcium dynamics support daily cell unity and diversity in the suprachiasmatic nuclei of free-behaving mice

The suprachiasmatic nuclei (SCN) of the anterior hypothalamus host the circadian pacemaker that synchronizes mammalian rhythms with the day-night cycle. SCN neurons are intrinsically rhythmic, thanks to a conserved cell-autonomous clock mechanism. In addition, circuit-level emergent properties confer a unique degree of precision and robustness to SCN neuronal rhythmicity. However, the multicellular functional organization of the SCN is not yet fully understood. Although SCN neurons are well coordinated, experimental evidences indicate that some neurons oscillate out of phase in SCN explants, and possibly to a larger extent in vivo. Here, we used microendoscopic Ca2+i imaging to investigate SCN rhythmicity at a single cell resolution in free-behaving mice. We found that SCN neurons in vivo exhibited fast Ca2+i spikes superimposed upon slow changes in baseline Ca2+i levels. Both spikes and baseline followed a time-of-day modulation in many neurons, but independently from each other. Daily rhythms in basal Ca2+i were well coordinated, while spike activity from the same neurons peaked at multiple times of the light cycle, and unveiled clock-independent interactions at the multicellular level. Hence, fast Ca2+i spikes and slow changes in baseline Ca2+i levels highlighted how diverse activity patterns could articulate within the temporal network unity of the SCN in vivo, and provided support for a multiplex neuronal code in the circadian pacemaker.

A neuronal code for space in hippocampal coactivity dynamics independent of place fields

Hippocampus CA1 place cells express a spatial neural code by discharging action potentials in cell-specific locations (′place fields′), but their discharge timing is also coordinated by multiple mechanisms, suggesting an alternative ′ensemble cofiring′ neural code, potentially distinct from place fields. We compare the importance of these distinct information representation schemes for encoding environments. Using miniature microscopes, we recorded the ensemble activity of mouse CA1 principal neurons expressing GCaMP6f across a multi-week experience of two distinct environments. We find that both place fields and ensemble coactivity relationships are similarly reliable within environments and distinctive between environments. Decoding the environment from cell-pair coactivity relationships is effective and improves after removing cell-specific place tuning. Ensemble decoding relies most crucially on anti-coactive cell pairs distributed across CA1 and is independent of place cell firing fields. We conclude that ensemble cofiring relationships constitute an advantageous neural code for environmental space, independent of place fields.

Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements which are strictly controlled via the experimental settings. It remains unclear how results can be carried over to less constrained behavior, i.e. freely moving subjects. Towards this goal, we developed a self-paced behavioral paradigm which encouraged rats to conduct different types of movements. Via bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking, we identified behavioral coupling of neurons with lateralization and an anterior-posterior gradient from premotor to primary sensory cortex. The structure of population activity patterns was conserved across animals, in spite of severe undersampling of the total number of neurons and variations of electrode positions across individuals. Via alignments of low-dimensional neural manifolds, we demonstrate cross-subject and cross-session generalization in a decoding task arguing for a conserved neuronal code.

Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements which are strictly controlled via the experimental settings. It remains unclear how results can be carried over to less constrained behavior, i.e. freely moving subjects. Towards this goal, we developed a self-paced behavioral paradigm which encouraged rats to conduct different types of movements. Via bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking, we identified behavioral coupling of neurons with lateralization and an anterior-posterior gradient from premotor to primary sensory cortex. The structure of population activity patterns was conserved across animals, in spite of severe undersampling of the total number of neurons and variations of electrode positions across individuals. Via alignments of low-dimensional neural manifolds, we demonstrate cross-subject and cross-session generalization in a decoding task arguing for a conserved neuronal code.

Neuronal activity in sensory cortex predicts the specificity of learning

SummaryLearning to avoid dangerous signals while preserving normal behavioral responses to safe stimuli is essential for everyday behavior and survival. Like other forms of learning, fear learning has a high level of inter-subject variability. Following an identical fear conditioning protocol, different subjects exhibit a range of fear specificity. Under high specificity, subjects specialize fear to only the paired (dangerous) stimulus, whereas under low specificity, subjects generalize fear to other (safe) sensory stimuli. Pathological fear generalization underlies emotional disorders, such as post-traumatic stress disorder. Despite decades of work, the neuronal basis that determines fear specificity level remains unknown. We identified the neuronal code that underlies variability in fear specificity. We performed longitudinal imaging of activity of neuronal ensembles in the auditory cortex of mice prior to and after the mice were subjected to differential fear conditioning. The neuronal code in the auditory cortex prior to learning predicted the level of specificity following fear learning across subjects. After fear learning, population neuronal responses were reorganized: the responses to the safe stimulus decreased, whereas the responses to the dangerous stimulus remained the same, rather than decreasing as in pseudo-conditioned subjects. The magnitude of these changes, however, did not correlate with learning specificity, suggesting that they did not reflect the fear memory. Together, our results identify a new, temporally restricted, function for cortical activity in associative learning. These results reconcile seemingly conflicting previous findings and provide for a neuronal code for determining individual patterns in learning.

Dimensionality Reduction on Spatio-Temporal Maximum Entropy Models of Spiking Networks

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.Author 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.