Past events

With increased life expectancy, the incidence of patients suffering from Alzheimer’s disease (AD) and dementia has been steadily increasing. Currently, there is not a single treatment that can change the diseases course. Our team, over more than two decades, has demonstrated that the brain needs support from the immune system for its life-long functional plasticity and repair. Furthermore, using immunological and immunogenomic tools, we demonstrated that in AD, the immune system dysfunctions and perpetuates the pathology. Based on these observations and numerous others, we proposed that boosting the systemic immune system might facilitate mobilization of immune cells to help the brain. We found that the optimal way to activate such a reparative immune response is by reducing the restraints on the immune system, by blocking the PD-1/PD-L1 inhibitory immune checkpoint pathway. This therapy facilitates translocation of phagocytic cells to the brain; based on their transcriptomic profile, we demonstrated that these cells express molecules that can uniquely remove the toxic forms of misfolded proteins plaques, dead cells, and cell debris, and can thereby rescue synapses, change the disease course and improve brain function. Overall, our results indicate that targeting systemic and local immune cells rather than brain-specific disease-escalating factors provides a multi-dimensional disease-modifying therapy for AD and dementia, regardless of the primary disease etiology. Our approach is under an expedited development process towards clinical trial.

Firing-rate (FR) or neural-mass models are widely used for studying computations performed by neural populations. Despite their success, classical firing-rate models do not capture spike timing effects on the microscopic level such as spike synchronization and are difficult to link to spiking data in experimental recordings. For large neuronal populations, the gap between the spiking neuron dynamics on the microscopic level and coarse-grained FR models on the population level can be bridged by mean-field theory formally valid for infinitely many neurons. It remains however challenging to extend the resulting mean-field models to finite-size populations with biologically realistic neuron numbers per cell type (mesoscopic scale). In this talk, I present a mathematical framework for mesoscopic populations of generalized integrate-and-fire neuron models that accounts for fluctuations caused by the finite number of neurons. To this end, I will introduce the refractory density method for quasi-renewal processes and show how this method can be generalized to finite-size populations. To demonstrate the flexibility of this approach, I will show how synaptic short-term plasticity can be incorporated in the mesoscopic mean-field framework. On the other hand, the framework permits a systematic reduction to low-dimensional FR equations using the eigenfunction method. Our modeling framework enables a re-examination of classical FR models in computational neuroscience under biophysically more realistic conditions.

Obesity is a complex disease with its roots in the physiology of various brain circuits. Although much progress has been made in understanding the disease, the most fundamental question remains unanswered – why do we overeat? As Clifford Saper (Harvard) points out, “if feeding were controlled solely by homeostatic mechanisms, most of us would be at our ideal body weight, and people would consider feeding like breathing or elimination, a necessary but unexciting part of existence”. Clearly this is not the case; hedonic eating has come increasing under the spotlight in recent years as a main driver of obesity. As food becomes more and more rewarding, could overeating be driven by a pathological search for reward? In my talk I will demonstrate that chronic diet of highly-palatable food changes the physiology of the reward system and that mice that gained the most weight differ from those that gained the least weight in the physiology of two regions of the reward system – the nucleus accumbens and the ventral pallidum. Furthermore, I will show that long term plasticity in the ventral pallidum may be an innate marker for the predisposition to overeat palatable food.

Interpreting high-dimensional datasets requires new computational and analytical methods. We developed such methods to extract and analyze neural activity from 20,000 neurons recorded simultaneously in awake, behaving mice. The neural activity was not low-dimensional as commonly thought, but instead was high-dimensional and obeyed a power-law scaling across its eigenvalues. We developed a theory that proposes that neural responses to external stimuli maximize information capacity while maintaining a smooth neural code. We then observed power-law eigenvalue scaling in many real-world datasets, and therefore developed a nonlinear manifold embedding algorithm called Rastermap that can capture such high-dimensional structure.

Individuals differ substantially in their attitudes to uncertainty: some avoid is at all costs, while others are tolerant of, or even seek, uncertainty. These differences are important, because uncertainty is everywhere – how we cope with uncertainty can have significant implications for our mental health and quality of life. I will describe a series of studies in which we characterize individual differences in decision-making under uncertainty, and use these characterizations to study the neural mechanisms of decision-making under uncertainty and variations in these mechanisms in mental illness.

Magnetic Resonance Spectroscopy (MRS) can be used to measure the in-vivo concentrations of several metabolites in the brain non-invasively. I will present our work using MRS to study two aspects of brain metabolism. First, I'll talk about our work on functional MRS, whereby we look at neurochemical changes during or after learning or function. In the second half of the talk, I will focus on new methods we're developing in the lab, and in particular on our ability to measure the thermal relaxation times of metabolites, which probe specific cellular and subcellular microenvironments. I will present some preliminary data showing where and how this could be useful.

Free behavior is likely the most fundamental and essential aspect of human life. It underlies our unique ability to self-generate actions and come up with creative and original solutions. Yet, the brain mechanism that drives such free and creative behaviors remains unknown. In my talk I will present experimental findings supporting the hypothesis that ultra-slow spontaneous (resting state) activity fluctuations are a central and ubiquitous mechanism underlying all types of free behavior. Traces of slow resting state fluctuations can account for the intriguing observation that free behaviors of all types- from generating names to free recall of visual images- are invariably preceded by a wave of slow (1-4 seconds) activity buildup. This buildup can be observed in BOLD-fMRI, intracranial recording of single neurons and more recently, in the massive hippocampal bursts called Sharp Wave Ripples. Could the similar slow dynamics of the spontaneous fluctuations and the anticipatory buildup preceding free behaviors be a mere coincidence? Crucially, I will present evidence that individual differences in the waveforms of spontaneous fluctuations measured during are significantly correlated to the shape of the buildup wave anticipating free and creative events. The critical role of spontaneous activity fluctuations in generating creative decisions is reminiscent of the use of stochastic noise in optimizing solutions in network models.