Seminars

Inter-individual variability refers to differences in the expression of behaviors between members of a population. For instance, some individuals take greater risks, are more attracted to immediate gains or are more susceptible to drugs of abuse than others. To probe the neural bases of inter-individual variability we study reward seeking and decision-making in mice, and dissect the specific role of dopamine in the modulation of these behaviors. Using a spatial version of the multi-armed bandit task, in which mice are faced with consecutive binary choices, we could link modifications of midbrain dopamine cell dynamics with modulation of exploratory behaviors, a major component of individual characteristics in mice. By analyzing mouse behaviors in semi-naturalistic environments, we then explored the role of social relationships in the shaping of dopamine activity and associated beahviors. I will present recent data from the laboratory suggesting that changes in the activity of dopaminergic networks link social influences with variations in the expression of non-social behaviors: by acting on the dopamine system, the social context may indeed affect the capacity of individuals to make decisions, as well as their vulnerability to drugs of abuse, in particular nicotine.

It has long been established that the hippocampus plays a crucial role for episodic memory. As opposed to the modular approach, now it is generally assumed that being a complex structure, the HC performs multiplex interconnected functions, whose hierarchical organization provides basis for the higher cognitive functions such as semantics-based encoding and retrieval. However, the «where, when and how» properties of distinct memory aspects within and outside the HC are still under debate. Here we used a visual associative memory task as a probe to test the hypothesis about the differential involvement of the rostral and caudal portions of the human hippocampus in memory encoding, recognition and associative recall. In epilepsy patients implanted with stereo-EEG, we show that at retrieval the rostral HC is selectively active for recognition memory, whereas the caudal HC is selectively active for the associative memory. Low frequency desynchronization and high frequency synchronization characterize the temporal dynamic in encoding and retrieval. Therefore, we describe here anatomical segregation in the hippocampal contributions to associative and recognition memory.

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

To localize ourselves in an environment for spatial navigation, we rely on vision and self-motion inputs, which only provide noisy and partial information. It is unknown how the resulting uncertainty affects navigation behavior and neural representations. Here we show that spatial uncertainty underlies key effects of environmental geometry on navigation behavior and grid field deformations. We develop an ideal observer model, which continually updates probabilistic beliefs about its allocentric location by optimally combining noisy egocentric visual and self-motion inputs via Bayesian filtering. This model directly yields predictions for navigation behavior and also predicts neural responses under population coding of location uncertainty. We simulate this model numerically under manipulations of a major source of uncertainty, environmental geometry, and support our simulations by analytic derivations for its most salient qualitative features. We show that our model correctly predicts a wide range of experimentally observed effects of the environmental geometry and its change on homing response distribution and grid field deformation. Thus, our model provides a unifying, normative account for the dependence of homing behavior and grid fields on environmental geometry, and identifies the unavoidable uncertainty in navigation as a key factor underlying these diverse phenomena.

Solving complex planning problems efficiently and flexibly requires reusing expensive previous computations. The brain can do this, but how? I present a new theory that addresses this question and connects planning to hitherto distinct areas within cognitive neuroscience, such as entorhinal representation of cognitive maps and cognitive control.

The origins, mechanisms and consequences of epilepsy in the developing brain are incompletely understood. Many developmental epilepsies have a genetic basis and their mechanisms stem from deficits in the function of one or numerous genes. Others, such as those that follow prolonged febrile seizures or severe birth asphyxia in a ‘normal’ brain may depend on the interaction of the insult with the rapidly evolving brain cells and circuits. Yet, how early-life insults may provoke epilepsy is unclear, and requires multiple levels of analysis: behavior, circuits, cells [neurons, glia] and molecules. Here we discuss developmental epileptogenesis, addressing some of its special features: the epilepsy phenotype, the effects insults on the maturation of brain circuits, the role of neuron-glia-neuron communication in cellular and circuit refinement, and how transient epileptogenic insults provoke enduring changes in the structure, connectivity and function of salient neuronal populations. We will highlight resolved questions- and the many unresolved issues that require tackling in 2022 and beyond.

The cerebellum resembles a feedforward, three-layer network of neurons in which the “hidden layer” consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

Mesmerize is a platform for the annotation and analysis of neuronal calcium imaging data. Mesmerize encompasses the entire process of calcium imaging analysis from raw data to interactive visualizations. Mesmerize allows you to create FAIR-functionally linked datasets that are easy to share. The analysis tools are applicable for a broad range of biological experiments and come with GUI interfaces that can be used without requiring a programming background.

Modeling brain mechanisms is often confined to a given scale, such as single-cell models, network models or whole-brain models, and it is often difficult to relate these models. Here, we show an approach to build models across scales, starting from the level of circuits to the whole brain. The key is the design of accurate population models derived from biophysical models of networks of excitatory and inhibitory neurons, using mean-field techniques. Such population models can be later integrated as units in large-scale networks defining entire brain areas or the whole brain. We illustrate this approach by the simulation of asynchronous and slow-wave states, from circuits to the whole brain. At the mesoscale (millimeters), these models account for travelling activity waves in cortex, and at the macroscale (centimeters), the models reproduce the synchrony of slow waves and their responsiveness to external stimuli. This approach can also be used to evaluate the impact of sub-cellular parameters, such as receptor types or membrane conductances, on the emergent behavior at the whole-brain level. This is illustrated with simulations of the effect of anesthetics. The program codes are open source and run in open-access platforms (such as EBRAINS).

Discovering dynamical patterns from high fidelity timeseries is typically a challenging task. In this talk, the timeseries data consist of neural recordings taken from the auditory cortex of human subjects who listened to sequences of repeated triplets of tones and reported their perception by pressing a button. Subjects reported spontaneous alternations between two auditory perceptual states (1-stream and 2-streams). We discuss a data-driven method, which leverages time-delayed coordinates, diffusion maps, and dynamic mode decomposition, to identify neural features that correlated with subject-reported switching between perceptual states.

Language production is a form of behavior and as such involves executive control and the prefrontal function. The cognitive architecture of prefrontal executive function thus certainly plays an important role in shaping language production. In this talk, I will review the main features of the prefrontal executive function we have uncovered during the last two decades and I will discuss how these features may help understanding language production.

Dr. Davis will present a summary regarding the identification and characterization of mechanisms of homeostatic plasticity as they relate to the control of synaptic transmission. He will then provide evidence of translation to the mammalian neuromuscular junction and central synapses, and provide tangible links to the etiology of neurological disease.