Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity.

Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static synapses to the case of dynamic synapses exhibiting short-term plasticity (STP). The extended theory offers an approximate mean-field dynamics for the synaptic input currents arising from populations of spiking neurons and synapses undergoing Tsodyks-Markram STP. The approximate mean-field dynamics accounts for both finite number of synapses and correlation between the two synaptic variables of the model (utilization and available resources) and its numerical implementation is simple. Comparisons with Monte Carlo simulations of the microscopic model show that in both feedforward and recurrent networks, the mesoscopic mean-field model accurately reproduces the first- and second-order statistics of the total synaptic input into a postsynaptic neuron and accounts for stochastic switches between Up and Down states and for population spikes. The extended mesoscopic population theory of spiking neural networks with STP may be useful for a systematic reduction of detailed biophysical models of cortical microcircuits to numerically efficient and mathematically tractable mean-field models.

Pre-existing astrocytes form functional perisynaptic processes on neurons generated in the adult hippocampus.

The adult dentate gyrus produces new neurons that morphologically and functionally integrate into the hippocampal network. In the adult brain, most excitatory synapses are ensheathed by astrocytic perisynaptic processes that regulate synaptic structure and function. However, these processes are formed during embryonic or early postnatal development and it is unknown whether astrocytes can also ensheathe synapses of neurons born during adulthood and, if so, whether they play a role in their synaptic transmission. Here, we used a combination of serial-section immuno-electron microscopy, confocal microscopy, and electrophysiology to examine the formation of perisynaptic processes on adult-born neurons. We found that the afferent and efferent synapses of newborn neurons are ensheathed by astrocytic processes, irrespective of the age of the neurons or the size of their synapses. The quantification of gliogenesis and the distribution of astrocytic processes on synapses formed by adult-born neurons suggest that the majority of these processes are recruited from pre-existing astrocytes. Furthermore, the inhibition of astrocytic glutamate re-uptake significantly reduced postsynaptic currents and increased paired-pulse facilitation in adult-born neurons, suggesting that perisynaptic processes modulate synaptic transmission on these cells. Finally, some processes were found intercalated between newly formed dendritic spines and potential presynaptic partners, suggesting that they may also play a structural role in the connectivity of new spines. Together, these results indicate that pre-existing astrocytes remodel their processes to ensheathe synapses of adult-born neurons and participate to the functional and structural integration of these cells into the hippocampal network.

Visuospatial viewpoint manipulation during full-body illusion modulates subjective first-person perspective.

Self-consciousness is based on multisensory signals from the body. In full-body illusion (FBI) experiments, multisensory conflict was used to induce changes in three key aspects of bodily self-consciousness (BSC): self-identification (which body 'I' identify with), self-location (where 'I' am located), and first-person perspective (from where 'I' experience the world; 1PP). Here, we adapted a previous FBI protocol in which visuotactile stroking was administered by a robotic device (tactile stroking) and simultaneously rendered on the back of a virtual body (visual stroking) that participants viewed on a head-mounted display as if filmed from a posterior viewpoint of a camera. We compared the effects of two different visuospatial viewpoints on the FBI and thereby on these key aspects of BSC. During control manipulations, participants saw a no-body object instead of a virtual body (first experiment) or received asynchronous versus synchronous visuotactile stroking (second experiment). Results showed that within-subjects visuospatial viewpoint manipulations affected the subjective 1PP ratings if a virtual body was seen but had no effect for viewing a non-body object. However, visuospatial viewpoint had no effect on self-identification, but depended on the viewed object and visuotactile synchrony. Self-location depended on visuospatial viewpoint (first experiment) and visuotactile synchrony (second experiment). Our results show that the visuospatial viewpoint from which the virtual body is seen during FBIs modulates the subjective 1PP and that such viewpoint manipulations contribute to spatial aspects of BSC. We compare the present data with recent data revealing vestibular contributions to the subjective 1PP and discuss the multisensory nature of BSC and the subjective 1PP.

Multisensory origin of the subjective first-person perspective: visual, tactile, and vestibular mechanisms.

In three experiments we investigated the effects of visuo-tactile and visuo-vestibular conflict about the direction of gravity on three aspects of bodily self-consciousness: self-identification, self-location, and the experienced direction of the first-person perspective. Robotic visuo-tactile stimulation was administered to 78 participants in three experiments. Additionally, we presented participants with a virtual body as seen from an elevated and downward-directed perspective while they were lying supine and were therefore receiving vestibular and postural cues about an upward-directed perspective. Under these conditions, we studied the effects of different degrees of visuo-vestibular conflict, repeated measurements during illusion induction, and the relationship to a classical measure of visuo-vestibular integration. Extending earlier findings on experimentally induced changes in bodily self-consciousness, we show that self-identification does not depend on the experienced direction of the first-person perspective, whereas self-location does. Changes in bodily self-consciousness depend on visual gravitational signals. Individual differences in the experienced direction of first-person perspective correlated with individual differences in visuo-vestibular integration. Our data reveal important contributions of visuo-vestibular gravitational cues to bodily self-consciousness. In particular we show that the experienced direction of the first-person perspective depends on the integration of visual, vestibular, and tactile signals, as well as on individual differences in idiosyncratic visuo-vestibular strategies.