Seminars

Start of the talk will be 17:15h (CEST). This session is a double feature of the Cologne Theoretical Neuroscience Forum and the BCCN Berlin.

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.

Human vision relies on rapid eye movements (saccades) 2-3 times every second to bring peripheral targets to central foveal vision for high resolution inspection. This rapid sampling of the world defines the perception-action cycle of natural vision and profoundly impacts our perception. Marmosets have similar visual processing and eye movements as humans, including a fovea that supports high-acuity central vision. Here, I present a novel approach developed in my laboratory for investigating the neural mechanisms of visual processing using naturalistic free viewing and simple target foraging paradigms. First, we establish that it is possible to map receptive fields in the marmoset with high precision in visual areas V1 and MT without constraints on fixation of the eyes. Instead, we use an off-line correction for eye position during foraging combined with high resolution eye tracking. This approach allows us to simultaneously map receptive fields, even at the precision of foveal V1 neurons, while also assessing the impact of eye movements on the visual information encoded. We find that the visual information encoded by neurons varies dramatically across the saccade to fixation cycle, with most information localized to brief post-saccadic transients. In a second study we examined if target selection prior to saccades can predictively influence how foveal visual information is subsequently processed in post-saccadic transients. Because every saccade brings a target to the fovea for detailed inspection, we hypothesized that predictive mechanisms might prime foveal populations to process the target. Using neural decoding from laminar arrays placed in foveal regions of area MT, we find that the direction of motion for a fixated target can be predictively read out from foveal activity even before its post-saccadic arrival. These findings highlight the dynamic and predictive nature of visual processing during eye movements and the utility of the marmoset as a model of active vision. Funding sources: NIH EY030998 to JM, Life Sciences Fellowship to JY

Many microorganisms and cells function in complex (non-Newtonian) fluids, which are mixtures of different materials and exhibit both viscous and elastic stresses. For example, mammalian sperm swim through cervical mucus on their journey through the female reproductive tract, and they must penetrate the viscoelastic gel outside the ovum to fertilize. In micro-scale swimming the dynamics emerge from the coupled interactions between the complex rheology of the surrounding media and the passive and active body dynamics of the swimmer. We use computational models of swimmers in viscoelastic fluids to investigate and provide mechanistic explanations for emergent swimming behaviors. I will discuss how flexible filaments (such as flagella) can store energy from a viscoelastic fluid to gain stroke boosts due to fluid elasticity. I will also describe 3D simulations of model organisms such as C. Reinhardtii and mammalian sperm, where we use experimentally measured stroke data to separate naturally coupled stroke and fluid effects. We explore why strokes that are adapted to Newtonian fluid environments might not do well in viscoelastic environments.

The inability to observe relevant biological processes in vivo significantly restricts human neurodevelopmental research. Advances in appropriate in vitro model systems, including patient-specific human brain organoids and human cortical spheroids (hCSs), offer a pragmatic solution to this issue. In particular, hCSs are an accessible method for generating homogenous organoids of dorsal telencephalic fate, which recapitulate key aspects of human corticogenesis, including the formation of neural rosettes—in vitro correlates of the neural tube. These neurogenic niches give rise to neural progenitors that subsequently differentiate into neurons. Studies differentiating induced pluripotent stem cells (hiPSCs) in 2D have linked atypical formation of neural rosettes with neurodevelopmental disorders such as autism spectrum conditions. Thus far, however, conventional methods of tissue preparation in this field limit the ability to image these structures in three-dimensions within intact hCS or other 3D preparations. To overcome this limitation, we have sought to optimise a methodological approach to process hCSs to maximise the utility of a novel Airy-beam light sheet microscope (ALSM) to acquire high resolution volumetric images of internal structures within hCS representative of early developmental time points.

Stereopsis – deriving information about distance by comparing views from two eyes – is widespread in vertebrates but so far known in only class of invertebrates, the praying mantids. Understanding stereopsis which has evolved independently in such a different nervous system promises to shed light on the constraints governing any stereo system. Behavioral experiments indicate that insect stereopsis is functionally very different from that studied in vertebrates. Vertebrate stereopsis depends on matching up the pattern of contrast in the two eyes; it works in static scenes, and may have evolved in order to break camouflage rather than to detect distances. Insect stereopsis matches up regions of the image where the luminance is changing; it is insensitive to the detailed pattern of contrast and operates to detect the distance to a moving target. Work from my lab has revealed a network of neurons within the mantis brain which are tuned to binocular disparity, including some that project to early visual areas. This is in contrast to previous theories which postulated that disparity was computed only at a single, late stage, where visual information is passed down to motor neurons. Thus, despite their very different properties, the underlying neural mechanisms supporting vertebrate and insect stereopsis may be computationally more similar than has been assumed.

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.

ONS figures suggest that at least 10% of individuals suffering COVID -19 Infection continue to experience several weeks after testing positive, and other studies report the proportions as even higher (e.g. Logue et al., 2021). One of the most prevalent reported symptoms among these “Long Covid” sufferers is cognitive dysfunction (Davis et al., 2020). However, to date the cognitive sequelae of COVID -19 are little understood. There are a number of reasons why COVID -19 infection might be associated with cognitive impairment and mental illness (e.g. Bougakov et al., 2020). In particular, increasing evidence indicates inflammation (e.g. Huang et al., 2020) and dysfunctional clotting (e.g. Taquet et al., 2021) as issues of major concern, both of which have been previously linked to a range of cognitive deficits (e.g. Vintimilla et al., 2019; Cumming et al., 2013). Indeed, evidence is beginning to emerge that cognitive issues may be widespread in the post-infection period, particularly among hospitalised and ventilated patients (e.g. Hampshire et al., 2020; Alemanno et al,. 2020). Here I shall present “Hot off the [SPSS]Press” results from a study on memory and cognition following COVID infection in a non-hospitalized cohort.

Behavioural measurements are often overlooked by computational neuroscientists, who prefer to focus on electrophysiological recordings or neuroimaging data. This attitude is largely due to perceived lack of depth/richness in relation to behavioural datasets. I will show how contemporary psychophysics can deliver extremely rich and highly constraining datasets that naturally interface with computational modelling. More specifically, I will demonstrate how psychophysics can be used to guide/constrain/refine computational models, and how models can be exploited to design/motivate/interpret psychophysical experiments. Examples will span a wide range of topics (from feature detection to natural scene understanding) and methodologies (from cascade models to deep learning architectures).

Among mammals, excellent color vision has evolved only in certain non-human primates. And yet, color is often assumed to be just a low-level stimulus feature with a modest role in encoding and recognizing objects. The rationale for this dogma is compelling: object recognition is excellent in grayscale images (consider black-and-white movies, where faces, places, objects, and story are readily apparent). In my talk I will discuss experiments in which we used color as a tool to uncover an organizational plan in inferior temporal cortex (parallel, multistage processing for places, faces, colors, and objects) and a visual-stimulus functional representation in prefrontal cortex (PFC). The discovery of an extensive network of color-biased domains within IT and PFC, regions implicated in high-level object vision and executive functions, compels a re-evaluation of the role of color in behavior. I will discuss behavioral studies prompted by the neurobiology that uncover a universal principle for color categorization across languages, the first systematic study of the color statistics of objects and a chromatic mechanism by which the brain may compute animacy, and a surprising paradoxical impact of memory on face color. Taken together, my talk will put forward the argument that color is not primarily for object recognition, but rather for the assessment of the likely behavioral relevance, or meaning, of the stuff we see.

Transmissible spongiform encephalopathies (TSEs) are neurodegenerative diseases of humans and many animal species caused by prions. The main constituent of prions is PrPSc, an aggregated moiety of the host-derived membrane glycolipoprotein PrPC. Prions were found to encipher many phenotypic, genetically stable TSE variants. The latter is very surprising, since PrPC is encoded by the host genome and all prion strains share the same amino acid sequence. Here I will review what is known about the infectivity, the neurotoxicity, and the neuroinvasiveness of prions. Also, I will explain why I regard the prion strain question as a fascinating challenge – with implications that go well beyond prion science. Finally, I will report some recent results obtained in my laboratory, which is attempting to address the strain question and some other basic issues of prion biology with a “systems” approach that utilizes organic chemistry, photophysics, proteomics, and mouse transgenesis.

Though the term has many definitions, computational neuroscience is mainly about applying mathematics to the study of the brain. The brain—a jumble of all different kinds of neurons interconnected in countless ways that somehow produce consciousness—has been described as “the most complex object in the known universe”. Physicists for centuries have turned to mathematics to properly explain some of the most seemingly simple processes in the universe—how objects fall, how water flows, how the planets move. Equations have proved crucial in these endeavors because they capture relationships and make precise predictions possible. How could we expect to understand the most complex object in the universe without turning to mathematics? — The answer is we can’t, and that is why I wrote this book. While I’ve been studying and working in the field for over a decade, most people I encounter have no idea what “computational neuroscience” is or that it even exists. Yet a desire to understand how the brain works is a common and very human interest. I wrote this book to let people in on the ways in which the brain will ultimately be understood: through mathematical and computational theories. — At the same time, I know that both mathematics and brain science are on their own intimidating topics to the average reader and may seem downright prohibitory when put together. That is why I’ve avoided (many) equations in the book and focused instead on the driving reasons why scientists have turned to mathematical modeling, what these models have taught us about the brain, and how some surprising interactions between biologists, physicists, mathematicians, and engineers over centuries have laid the groundwork for the future of neuroscience. — Each chapter of Models of the Mind covers a separate topic in neuroscience, starting from individual neurons themselves and building up to the different populations of neurons and brain regions that support memory, vision, movement and more. These chapters document the history of how mathematics has woven its way into biology and the exciting advances this collaboration has in store.

We have long known that rod and cone signals interact within the retina and can even contribute to color vision, but the extent of these influences has remained unclear. New results with more powerful methods of RNA expression profiling, specific cell labeling, and single-cell recording have provided greater clarity and are showing that rod and cone signals can mix at virtually every level of signal processing. These interactions influence the integration of retinal signals and make an important contribution to visual perception.