Seminars

In this talk, Dr. Schweinhardt will discuss top-down (i.e. cerebral) modulation of the perception and processing of nociceptive stimuli using selected examples in chronic pain patients as well as healthy subjects. Data on activity-dependent central sensitization will be presented as a case of bottom-up pain modulation. Finally, Dr. Schweinhardt will present a new line of research with which she aims at studying the interaction of top-down and bottom-up pain modulation.

Dr Juavinett will be presenting her book "So you want to be a neuroscientist?", which aims to give aspiring neuroscientists honest, informative insight about the field, as well as education and careers through it. The talk will be followed by a pub quiz (which is optional).

Using a combination of in vivo imaging of neuronal circuit functional and structural dynamics, we have investigated the mechanisms by which patterned neural activity and sensory experience alter connectivity in the developing brain. We have identified, in addition to the long-hypothesized Hebbian structural plasticity mechanisms, a kind of plasticity induced by the absence of correlated firing that we dubbed “Stentian plasticity”. In the talk I will discuss the phenomenology and some mechanistic insights regarding Stentian mechanisms in brain development. Further, I will show how glia may have a key role in circuit remodeling during development. These studies have led us to an appreciation of the importance of neuron-glia interactions in early development and the ability of patterned activity to guide circuit wiring.

Many flexible behaviors are thought to rely on internal representations of an animal’s spatial relationship to its environment and of the consequences of its actions in that environment. While such representations—e.g. of head direction and value—have been extensively studied, how they are combined to guide behavior is not well understood. I will discuss how we are exploring these questions using a classical visual learning paradigm for the fly. I’ll begin by describing a simple policy that, when tethered to an internal representation of heading, captures structured behavioral variability in this task. I’ll describe how ambiguities in the fly’s visual surroundings affect its perception and, when coupled to this policy, manifest in predictable changes in behavior. Informed by newly-released connectomic data, I’ll then discuss how these computations might be carried out and combined within specific circuits in the fly’s central brain, and how perception and action might interact to shape individual differences in learning performance.

Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We therefore recently developed an approach for dual mesoscopic imaging of acetylcholine release and neural activity across the entire cortical mantle in behaving mice. We find spatiotemporally heterogeneous patterns of cholinergic signaling across the cortex. Transitions between distinct behavioral states reorganize the structure of large-scale cortico-cortical networks and differentially regulate the relationship between cholinergic signals and neural activity. Together, our findings suggest dynamic state-dependent regulation of cortical network operations at the levels of both local and large-scale circuits. Zoom Meeting ID: 964 8138 3003 Contact host if you cannot connect.

In vivo, the human genome folds into a characteristic ensemble of 3D structures. The mechanism driving the folding process remains unknown. A theoretical model for chromatin (the minimal chromatin model) explains the folding of interphase chromosomes and generates chromosome conformations consistent with experimental data is presented. The energy landscape of the model was derived by using the maximum entropy principle and relies on two experimentally derived inputs: a classification of loci into chromatin types and a catalog of the positions of chromatin loops. This model was generalized by utilizing a neural network to infer these chromatin types using epigenetic marks present at a locus, as assayed by ChIP-Seq. The ensemble of structures resulting from these simulations completely agree with HI-C data and exhibits unknotted chromosomes, phase separation of chromatin types, and a tendency for open chromatin to lie at the periphery of chromosome territories. Although this theoretical methodology was trained in one cell line, the human GM12878 lymphoblastoid cells, it has successfully predicted the structural ensembles of multiple human cell lines. Finally, going beyond Hi-C, our predicted structures are also consistent with microscopy measurements. Analysis of both structures from simulation and microscopy reveals that short segments of chromatin make two-state transitions between closed conformations and open dumbbell conformations. For gene active segments, the vast majority of genes appear clustered in the linker region of the chromatin segment, allowing us to speculate possible mechanisms by which chromatin structure and dynamics may be involved in controlling gene expression. * Supported by the NSF

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Neuroscientists routinely perform experiments aimed at recording or manipulating neural activity, uncovering physiological processes underlying brain function or elucidating aspects of brain anatomy. Understanding how the brain generates behaviour ultimately depends on merging the results of these experiments into a unified picture of brain anatomy and function. We present BrainGlobe, a new initiative aimed at developing common Python tools for computational neuroanatomy. These include cellfinder for fast, accurate cell detection in whole-brain microscopy images, brainreg for aligning images to a reference atlas, and brainrender for visualisation of anatomically registered data. These software packages are developed around the BrainGlobe Atlas API. This API provides a common Python interface to download and interact with reference brain atlases from multiple species (including human, mouse and larval zebrafish). This allows software to be developed agnostic to the atlas and species, increasing adoption and interoperability of software tools in neuroscience.

The human brain network architecture can reveal crucial aspects of brain function and dysfunction. The topology of this network (known as the connectome) is shaped by a trade-off between wiring cost and network efficiency, and it has highly connected hub regions playing a prominent role in many brain disorders. By studying a landscape of plausible brain networks that preserve the wiring cost, fragile and resilient hubs can be identified. In this webinar, Dr Leonardo Gollo and Dr James Pang from Monash University will discuss this approach across the lifespan and some of its implications for neurodevelopmental and neurodegenerative diseases. Dr Leonardo Gollo is a Senior Research Fellow at the Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University. He holds an ARC Future Fellowship and his research interests include brain modelling, systems neuroscience, and connectomics. Dr James Pang is a Research Fellow at the Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University. His research interests are on combining neuroimaging and biophysical modelling to better understand the mechanisms of brain function in health and disease.

Traditionally, the term dynamics means changes in a system evolving over time. However, in the brain action potentials propagate along axons to induce postsynaptic currents with different delays at many sites simultaneously. This fundamental computational mechanism evolves spatially to engage the neuron populations involved in brain functions. To identify and understand the spatial processing in brains, this workshop will focus on the spatial principles of brain dynamics that determine how action potentials and membrane currents propagate in the networks of neurons that brains are made of. We will focus on non-artificial dynamics, which excludes in vitro dynamics, interference, electrical and optogenetic stimulations of brains in vivo. Recent non-artificial studies of spatial brain dynamics can actually explain how sensory, motor and internal brain functions evolve. The purpose of this workshop is to discuss these recent results and identify common principles of spatial brain dynamics.

Converging evidence from fMRI and EEG shows that audtiory spatial attention engages the same fronto-parietal network associated with visuo-spatial attention. This network is distinct from an auditory-biased processing network that includes other frontal regions; this second network is can be recruited when observers extract rhythmic information from visual inputs. We recently used a dual-task paradigm to examine whether this "division of labor" between a visuo-spatial network and an auditory-rhythmic network can be observed in a working memory paradigm. We varied the sensory modality (visual vs. auditory) and information domain (spatial or rhythmic) that observers had to store in working memory, while also performing an intervening task. Behavior, pupilometry, and EEG results show a complex interaction across the working memory and intervening tasks, consistent with two cognitive control networks managing auditory and visual inputs based on the kind of information being processed.

Humans can adapt to a novel task on our first try. By contrast, artificial intelligence systems often require immense amounts of data to adapt. In this talk, I will discuss my recent work (https://www.pnas.org/content/117/52/32970) on creating deep learning systems that can adapt on their first try by exploiting relationships between tasks. Specifically, the approach is based on transforming a representation for a known task to produce a representation for the novel task, by inferring and then using a higher order function that captures a relationship between the tasks. This approach can be interpreted as a type of analogical reasoning. I will show that task transformation can allow systems to adapt to novel tasks on their first try in domains ranging from card games, to mathematical objects, to image classification and reinforcement learning. I will discuss the analogical interpretation of this approach, an analogy between levels of abstraction within the model architecture that I refer to as homoiconicity, and what this work might suggest about using deep-learning models to infer analogies more generally.

Bats are extreme aviators and amazing navigators. Many bat species nightly commute dozens of kilometres in search of food, and some bat species annually migrate over thousands of kilometres. Studying bats in their natural environment has always been extremely challenging because of their small size (mostly <50 gr) and agile nature. We have recently developed novel miniature technology allowing us to GPS-tag small bats, thus opening a new window to document their behaviour in the wild. We have used this technology to track fruit-bats pups over 5 months from birth to adulthood. Following the bats’ full movement history allowed us to show that they use novel short-cuts which are typical for cognitive-map based navigation. In a second study, we examined how nectar-feeding bats make foraging decisions under competition. We show that by relying on a simple reinforcement learning strategy, the bats can divide the resource between them without aggression or communication. Together, these results demonstrate the power of the large scale natural approach for studying animal behavior.

In this talk series, addiction neuroscientists from across the world share their personal stories/experiences on the beauty of addiction neuroscience and how/why they have decided to invest their scientific life in this field. We hope that this talk series would encourage and support a new generation of young and passionate addiction neuroscientists in different countries to revolutionize the field of addiction medicine.

It has generally been presumed that sensory information encoded by a nervous system should be as accurate as its biological limitations allow. However, perhaps counter intuitively, accurate representations of sensory signals do not necessarily maximize the organism’s chances of survival. We show that neural codes that maximize reward expectation—and not accurate sensory representations—account for retinal responses in insects, and retinotopically-specific adaptive codes in humans. Thus, our results provide evidence that fitness-maximizing rules imposed by the environment are applied at the earliest stages of sensory processing.