Neuroscience seminars

Recent advancements in anatomical and functional imaging emphasize the presence of whole-brain networks organized according to functional and connectivity gradients, but how such structure shapes activity propagation and memory processes still lacks asatisfactory model. We analyse the fine-grained spatiotemporal dynamics of spontaneous activity in the entire dorsal cortex. through simultaneous recordings of wide-field voltage sensitive dye transients (VS), cortical ECoG, and hippocampal LFP in anesthetized mice. Both VS and ECoG show cortical avalanches. When measuring avalanches from the VS signal, we find a major deviation of the size scaling from the power-law distribution predicted by the criticality hypothesis and well approximated by the results from the ECoG. Breaking from scale-invariance, avalanches can thus be grouped in two regimes. Small avalanches consists of a limited number of co-activation modes involving a sub-set of cortical networks (related to the Default Mode Network), while larger avalanches involve a substantial portion of the cortical surface and can be clustered into two families: one immediately preceded by Retrosplenial Cortex activation and mostly involving medial-posterior networks, the other initiated by Somatosensory Cortex and extending preferentially along the lateral-anterior region. Rather than only differing in terms of size, these two set of events appear to be associated with markedly different brain-wide dynamical states: they are accompaniedby a shift in the hippocampal LFP, from the ripple band (smaller) to the gamma band (larger avalanches), and correspond to opposite directionality in the cortex-to-hippocampus causal relationship. These results provide a concrete description of global cortical dynamics, and shows how cortex in its entirety is involved in bi-directional communication in the hippocampus even in sleep-like states.

General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), antinociception (loss of pain sensation), akinesia (immobility), and hemodynamic stability with control of the stress response. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We illustrate this effect by presenting findings from our human studies of general anesthesia using high-density EEG recordings and intracranial recordings. These studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness due to propofol. We show how these dynamics change systematically with different anesthetic classes and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia. We demonstrate that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. Finally, we demonstrate that the state of general anesthesia can be controlled using closed loop feedback control systems. The success of our research has depended critically on tight coupling of experiments, signal processing research and mathematical modeling.

A wealth of experimental studies show that the trial-to-trial variability of neuronal activity is quenched during stimulus evoked responses. This fact has helped ground a popular view that the variability of spiking activity can be decomposed into two components. The first is due to irregular spike timing conditioned on the firing rate of a neuron (i.e. a Poisson process), and the second is the trial-to-trial variability of the firing rate itself. Quenching of the variability of the overall response is assumed to be a reflection of a suppression of firing rate variability. Network models have explained this phenomenon through a variety of circuit mechanisms. However, in all cases, from the vantage of a neuron embedded within the network, quenching of its response variability is inherited from its synaptic input. We analyze in vivo whole cell recordings from principal cells in layer (L) 2/3 of mouse visual cortex. While the variability of the membrane potential is quenched upon stimulation, the variability of excitatory and inhibitory currents afferent to the neuron are amplified. This discord complicates the simple inheritance assumption that underpins network models of neuronal variability. We propose and validate an alternative (yet not mutually exclusive) mechanism for the quenching of neuronal variability. We show how an increase in synaptic conductance in the evoked state shunts the transfer of current to the membrane potential, formally decoupling changes in their trial-to-trial variability. The ubiquity of conductance based neuronal transfer combined with the simplicity of our model, provides an appealing framework. In particular, it shows how the dependence of cellular properties upon neuronal state is a critical, yet often ignored, factor. Further, our mechanism does not require a decomposition of variability into spiking and firing rate components, thereby challenging a long held view of neuronal activity.

Our group is interested in discovering design principles that govern the structure and function of neurons and neural circuits. We record from well-defined neurons, mainly in flies’ visual systems, to measure the molecular and cellular factors that determine relevant measures of performance, such as representational capacity, dynamic range and accuracy. We combine this empirical approach with modelling to see how the basic elements of neural systems (ion channels, second messengers systems, membranes, synapses, neurons, circuits and codes) combine to determine performance. We are investigating four general problems. How are circuits designed to integrate information efficiently? How do sensory adaptation and synaptic plasticity contribute to efficiency? How do the sizes of neurons and networks relate to energy consumption and representational capacity? To what extent have energy costs shaped neurons, sense organs and brain regions during evolution?

In a few months, human infants develop complex capacities in numerous cognitive domains. They learn their native language, recognize their parents, refine their numerical capacities and their perception of the world around them but are they conscious and how can we study consciousness when no verbal report is possible? One way to approach this question is to rely on the neural responses correlated with conscious perception in adults (i.e. a global increase of activity in notably frontal regions with top-down amplification of the sensory levels). We can thus study at what age the developing anatomical architecture might be mature enough to allow this type of responses, but moreover we can use similar experimental paradigms than in adults in which we expect to observe a similar pattern of functional responses.

Exposure to proper environment during early development is essential for brain maturation. Impaired sensory input or abnormal experiences can have long-term negative consequences on brain health. We seek to define the precise synaptic aberrations caused by abnormal visual experiences early in life, and how these can be remedied through viral, genetic and environmental approaches. Resulting knowledge will contribute to the development of new approaches to mitigate nervous system damage caused by abnormal early life experience.

The brain has a hugely diverse, heterogeneous structure. By contrast, many functional neural models are homogeneous. We compared the performance of spiking neural networks trained to carry out difficult tasks, with varying degrees of heterogeneity. Introducing heterogeneity in membrane and synapse time constants substantially improved task performance, and made learning more stable and robust across multiple training methods, particularly for tasks with a rich temporal structure. In addition, the distribution of time constants in the trained networks closely matches those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.

Spontaneous behavior in animals and humans shows a striking amount of variability both in the spatial domain (which actions to choose) and temporal domain (when to act). Concatenating actions into sequences and behavioral plans reveals the existence of a hierarchy of timescales ranging from hundreds of milliseconds to minutes. How do multiple timescales emerge from neural circuit dynamics? How do circuits modulate temporal responses to flexibly adapt to changing demands? In this talk, we will present recent results from experiments and theory suggesting a new computational mechanism generating the temporal variability underlying naturalistic behavior and cortical activity. We will show how neural activity from premotor areas unfolds through temporal sequences of attractors, which predict the intention to act. These sequences naturally emerge from recurrent cortical networks, where correlated neural variability plays a crucial role in explaining the observed variability in action timing. We will then discuss how reaction times can be accelerated or slowed down via gain modulation, flexibly induced by neuromodulation or perturbations; and how gain modulation may control response timing in the visual cortex. Finally, we will present a new biologically plausible way to generate a reservoir of multiple timescales in cortical circuits.

Temporal lobe epilepsy is considered a neuronal microcircuit dysfunction, yet mechanisms are poorly understood. Here we will discuss recent data on cell-type specific alterations of hippocampal microcircuit function in experimental models of temporal lobe epilepsy. We will highlight the importance of leveraging on cellular heterogeneity to better understand the complexities accompanying hippocampal sclerosis.