Neuroscience seminars

Understanding and remembering realistic experiences in our everyday lives requires activating many kinds of structured knowledge about the world, including spatial maps, temporal event scripts, and semantic relationships. My recent projects have explored the ways in which we build up this schematic knowledge (during a single experiment and across developmental timescales) and can strategically deploy them to construct event representations that we can store in memory or use to make predictions. I will describe my lab's ongoing work developing new experimental and analysis techniques for conducting functional MRI experiments using narratives, movies, poetry, virtual reality, and "memory experts" to study complex naturalistic schemas.

Animals exhibit extraordinary variation in their behaviour, yet little is known about the neural mechanisms that generate this diversity. My lab has been taking advantage of the rapid diversification of male courtship behaviours in Drosophila to gain insight into how evolution shapes the nervous system to generate species-specific behaviours. By translating neurogenetic tools from D. melanogaster to closely related Drosophila species, we have begun to directly compare the homologous neural circuits and pinpoint sites of adaptive change. Across species, P1 interneurons serve as a conserved and key node in regulating male courtship: these neurons are selectively activated by the sensory cues carried by an appropriate mate and their activation triggers enduring courtship displays. We have been examining how different sensory pathways converge onto P1 neurons to regulate a male’s state of arousal, honing his pursuit of a prospective partner. Moreover, by performing cross-species comparison of these circuits, we have begun to gain insight into how reweighting of sensory inputs to P1 neurons underlies species-specific mate recognition. Our results suggest how variation at flexible nodes within the nervous system can serve as a substrate for behavioural evolution, shedding light on the types of changes that are possible and preferable within brain circuits.

The neural mechanisms underlying decision-making are typically examined by statistical analysis of large numbers of trials from sequentially recorded single neurons. Averaging across sequential recordings, however, obscures important aspects of decision-making such as variations in confidence and 'changes of mind' (CoM) that occur at variable times on different trials. I will show that the covert decision variables (DV) can be tracked dynamically on single behavioral trials via simultaneous recording of large neural populations in prefrontal cortex. Vacillations of the neural DV, in turn, identify candidate CoM in monkeys, which closely match the known properties of human CoM. Thus simultaneous population recordings can provide insight into transient, internal cognitive states that are otherwise undetectable.

I will introduce a web portal for the retinal neuroscience community to explore the catalog of mouse retinal ganglion cell (RGC) types, including data on light responses, correspondences with morphological types in EyeWire, and gene expression data from single-cell transcriptomics. Our current classification includes 43 types, accounting for 90% of the cells in EyeWire. Many of these cell types have new stories to tell, and I will cover two of them that represent opposite ends of the spectrum of levels of analysis in my lab. First, I will introduce the “Bursty Suppressed-by-Contrast” RGC and show how its intrinsic properties rather than its synaptic inputs differentiate its function from that of a different well-known RGC type. Second, I will present the histogram of cell types that project to the Olivary Pretectal Nucleus, focusing on the recently discovered M6 ipRGC.

Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment jointly models shared information and idiosyncratic topographies. Pattern vectors for neural responses and connectivities are projected into a common, high-dimensional information space, rather than being aligned in a canonical anatomical space. Hyperalignment calculates individual transformation matrices that preserve the geometry of pairwise dissimilarities between pattern vectors. Individual cortical topographies are modeled as mixtures of overlapping, individual-specific topographic basis functions, rather than as contiguous functional areas. The fundamental property of brain function that is preserved across brains is information content, rather than the functional properties of local features that support that content.

This is the inaugural symposium of the Simons-Emory International Consortium on Motor Control, and speakers will deliver 10 minute talks (each followed by 10 minutes of discussion) addressing this question: "What tools are we missing to understand motor control, and what could we learn if we had them?”

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.

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In this webinar series, several experts describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This webinar is open for all interested students and researchers. If you are interested to participate live, please send a short message to smartstart@fz-juelich.de Please note, these lectures will be recorded for subsequent publishing as online lecture material.

behaviour varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical underpinnings of this individuality, though previous work implicates sensory periphery. Drosophila olfaction presents an ideal model to study the biological basis of behavioural individuality, because while the neural circuit underlying olfactory behaviour is well-described and highly stereotyped, persistent idiosyncrasy in behaviour, neural coding, and neural wiring have also been described. Projection neurons (PNs), which relay odor signals sensed by olfactory receptor neurons (ORNs) to deeper brain structures, exhibit variable calcium responses to identical odor stimuli across individuals, but how these idiosyncrasies relate to individual behavioural responses remains unknown. Here, using paired behaviour and two-photon imaging measurements, we show that idiosyncratic calcium dynamics in both ORNs and PNs predict individual preferences for an aversive monomolecular odorant versus air, suggesting that variation at the periphery of the olfactory system determines individual preference for an odor’s presence. In contrast, PN, but not ORN, calcium responses predict individual preferences in a two-odor choice assay. Furthermore, paired behaviour and immunohistochemistry measurements reveal that variation in ORN presynaptic density also predicts two-odor preference, suggesting this site is a locus of individuality where microscale circuit variation gives rise to idiosyncrasy in behaviour. Our results demonstrate how a neural circuit may vary functionally and structurally to produce variable behaviour among individuals.

The development of neural circuits is profoundly impacted by both spontaneous and sensory experience. This is perhaps most well studied in the visual system, where disruption of early spontaneous activity called retinal waves prior to eye opening and visual deprivation after eye opening leads to alterations in the response properties and connectivity in several visual centers in the brain. We address this question in the retina, which comprises multiple circuits that encode different features of the visual scene, culminating in over 40 different types of retinal ganglion cells. Direction-selective ganglion cells respond strongly to an image moving in the preferred direction and weakly to an image moving in the opposite, or null, direction. Moreover, as recently described (Sabbah et al, 2017) the preferred directions of direction selective ganglion cells cluster along four directions that align along two optic flow axes, causing variation of the relative orientation of preferred directions along the retinal surface. I will provide recent progress in the lab that addresses the role of visual experience and spontaneous retinal waves in the establishment of direction selective tuning and direction selectivity maps in the retina.

In this talk, Professor Hardy will discuss the different causes and predispositions of PD that exist in Africa, and the differences to European populations. He will go on to discuss the importance of highlighting these differences and the impact of this vital research to people living with PD in Africa, as well as their families and caregivers.

Our research team runs several related projects studying the cellular and molecular mechanisms involved in the development of axonal connections in the brain. In particular, our aim is to uncover the principles underlying thalamocortical axonal wiring, maintenance and ultimately the rewiring of connections, through an integrated and innovative experimental programme. The development of the thalamocortical wiring requires a precise topographical sorting of its connections. Each thalamic nucleus receives specific sensory information from the environment and projects topographically to its corresponding cortical. A second level of organization is achieved within each area, where thalamocortical connections display an intra-areal topographical organization, allowing the generation of accurate spatial representations within each cortical area. Therefore, the level of organization and specificity of the thalamocortical projections is much more complex than other projection systems in the CNS. The central hypothesis of our laboratory is that thalamocortical input influences and maintains the functional architecture of the sensory cortices. We also believe that rewiring and plasticity events can be triggered by activity-dependent mechanisms in the thalamus. Three major questions are been focused in the laboratory: i) the role of spontaneous patterns of activity in thalamocortical wiring and cortical development, ii) the role of the thalamus and its connectivity in the neuroplastic cortical changes following sensory deprivation, and iii) reprogramming thalamic cells for sensory circuit restoration. Within these projects we are using several experimental programmes, these include: optical imaging, manipulation of gene expression in vivo, cell and molecular biology, biochemistry, cell culture, sensory deprivation paradigms and electrophysiology. The results derived from our investigations will contribute to our understating of how reprogramming of cortical wiring takes place following brain damage and how cortical structure is maintained.

The vagus nerve contains a diversity of sensory neurons that detect peripheral stimuli such as blood pressure changes at the aortic arch, lung expansion during breathing, meal-induced stomach distension, and chemotherapeutics that induce nausea. Underlying vagal sensory mechanisms are largely unresolved at a molecular level, presenting tremendously important problems in sensory biology. We charted vagal sensory neurons by single cell RNA sequencing, identifying novel cell surface receptors and classifying a staggering diversity of sensory neuron types. We then generated a collection of ires-Cre knock-in mice to target each neuron type, and adapted genetic tools for Cre-based anatomical mapping, in vivo imaging, targeted ablation, and optogenetic control of vagal neuron activity. We found different sensory neuron types that innervate the lung and exert powerful effects on breathing, others that monitor and control the digestive system, and yet others that innervate that innervate the larynx and protect the airways. Together with Ardem Patapoutian, we also identified a critical role for Piezo mechanoreceptors in the sensation of airway stretch, which underlies a classical respiratory reflex termed the Hering-Breuer inspiratory reflex, as well as in the neuronal sensation of blood pressure and the baroreceptor reflex.

The description of neural computations currently relies on two competing views: (i) a classical single-cell view that aims to relate the activity of individual neurons to sensory or behavioural variables, and organize them into functional classes; (ii) a more recent population view that instead characterises computations in terms of collective neural trajectories, and focuses on the dimensionality of these trajectories as animals perform tasks. How the two key concepts of functional cell classes and low-dimensional trajectories interact to shape neural computations is however at present not understood. Here I will address this question by combining machine-learning tools for training recurrent neural networks with reverse-engineering and theoretical analyses of network dynamics.

Cognitive tasks typically require the integration of working memory, contextual processing, and planning to be carried out in close coordination. However, these computations are typically studied within neuroscience as independent modular processes in the brain. In this talk I will present an alternative view, that neural representations of mappings between expected stimuli and contingent goal actions can unify working memory and planning computations. We term these stored maps contingency representations. We developed a "conditional delayed logic" task capable of disambiguating the types of representations used during performance of delay tasks. Human behaviour in this task is consistent with the contingency representation, and not with traditional sensory models of working memory. In task-optimized artificial recurrent neural network models, we investigated the representational geometry and dynamical circuit mechanisms supporting contingency-based computation, and show how contingency representation explains salient observations of neuronal tuning properties in prefrontal cortex. Finally, our theory generates novel and falsifiable predictions for single-unit and population neural recordings.