Matthias Kaschube

Quantitative 4D analyses of epithelial folding during Drosophila gastrulation

Understanding the cellular and mechanical processes that underlie the shape changes of individual cells and their collective behaviors in a tissue during dynamic and complex morphogenetic events is currently one of the major frontiers in developmental biology. The advent of high-speed time-lapse microscopy and its use in monitoring the cellular events in fluorescently labeled developing organisms demonstrate tremendous promise in establishing detailed descriptions of these events and could potentially provide a foundation for subsequent hypothesis-driven research strategies. However, obtaining quantitative measurements of dynamic shapes and behaviors of cells and tissues in a rapidly developing metazoan embryo using time-lapse 3D microscopy remains technically challenging, with the main hurdle being the shortage of robust imaging processing and analysis tools. We have developed EDGE4D, a software tool for segmenting and tracking membrane-labeled cells using multi-photon microscopy data. Our results demonstrate that EDGE4D enables quantification of the dynamics of cell shape changes, cell interfaces and neighbor relations at single-cell resolution during a complex epithelial folding event in the early Drosophila embryo. We expect this tool to be broadly useful for the analysis of epithelial cell geometries and movements in a wide variety of developmental contexts.

Neural maps versus salt-and-pepper organization in visual cortex

Theoretical neuroscientists have long been intrigued by the spatial patterns of neuronal selectivities observed in the visual cortices of many mammals, including primates. While theoretical studies have contributed significantly to our understanding of how the brain learns to see, recent experimental discoveries of the spatial irregularity of visual response properties in the rodent visual cortex have prompted new questions about the origin and functional significance of cortical maps. Characterizing the marked differences of cortical design principles among species and comparing them may provide us with a deeper understanding of primate and non-primate vision.

The development of cortical circuits for motion discrimination

Stimulus discrimination depends on the selectivity and variability of neural responses, as well as the size and correlation structure of the responsive population. For direction discrimination in visual cortex, only the selectivity of neurons has been well characterized across development. Here we show in ferrets that at eye opening, the cortical response to visual stimulation exhibits several immaturities, including a high density of active neurons that display prominent wave-like activity, a high degree of variability and strong noise correlations. Over the next three weeks, the population response becomes increasingly sparse, wave-like activity disappears, and variability and noise correlations are markedly reduced. Similar changes were observed in identified neuronal populations imaged repeatedly over days. Furthermore, experience with a moving stimulus was capable of driving a reduction in noise correlations over a matter of hours. These changes in variability and correlation contribute significantly to a marked improvement in direction discriminability over development.

Passive Mechanical Forces Control Cell-Shape Change during Drosophila Ventral Furrow Formation

During Drosophila gastrulation, the ventral mesodermal cells constrict their apices, undergo a series of coordinated cell-shape changes to form a ventral furrow (VF) and are subsequently internalized. Although it has been well documented that apical constriction is necessary for VF formation, the mechanism by which apical constriction transmits forces throughout the bulk tissue of the cell remains poorly understood. In this work, we develop a computational vertex model to investigate the role of the passive mechanical properties of the cellular blastoderm during gastrulation. We introduce to our knowledge novel data that confirm that the volume of apically constricting cells is conserved throughout the entire course of invagination. We show that maintenance of this constant volume is sufficient to generate invagination as a passive response to apical constriction when it is combined with region-specific elasticities in the membranes surrounding individual cells. We find that the specific sequence of cell-shape changes during VF formation is critically controlled by the stiffness of the lateral and basal membrane surfaces. In particular, our model demonstrates that a transition in basal rigidity is sufficient to drive VF formation along the same sequence of cell-shape change that we observed in the actual embryo, with no active force generation required other than apical constriction.

Elucidating the control and development of skin patterning in cuttlefish

Few animals provide a readout that is as objective of their perceptual state as camouflaging cephalopods. Their skin display system includes an extensive array of pigment cells (chromatophores), each expandable by radial muscles controlled by motor neurons. If one could track the individual expansion states of the chromatophores, one would obtain a quantitative description—and potentially even a neural description by proxy—of the perceptual state of the animal in real time. Here we present the use of computational and analytical methods to achieve this in behaving animals, quantifying the states of tens of thousands of chromatophores at sixty frames per second, at single-cell resolution, and over weeks. We infer a statistical hierarchy of motor control, reveal an underlying low-dimensional structure to pattern dynamics and uncover rules that govern the development of skin patterns. This approach provides an objective description of complex perceptual behaviour, and a powerful means to uncover the organizational principles that underlie the function, dynamics and morphogenesis of neural systems.Tracking analyses of tens of thousands of individual chromatophores in freely behaving cephalopods enable studies of behaviour and development at cellular resolution.

Long-range order from local interactions: organization and development of distributed cortical networks

The cortical networks that underlie behavior exhibit an orderly functional organization at local and global scales, which is readily evident in the visual cortex of carnivores and primates1-6. Here, neighboring columns of neurons represent the full range of stimulus orientations and contribute to distributed networks spanning several millimeters2,7-11. However, the principles governing functional interactions that bridge this fine-scale functional architecture and distant network elements are unclear, and the emergence of these network interactions during development remains unexplored. Here, by using in vivo wide-field and 2-photon calcium imaging of spontaneous activity patterns in mature ferret visual cortex, we find widespread and specific modular correlation patterns that accurately predict the local structure of visually-evoked orientation columns from the spontaneous activity of neurons that lie several millimeters away. The large-scale networks revealed by correlated spontaneous activity show abrupt ‘fractures’ in continuity that are in tight register with evoked orientation pinwheels. Chronic in vivo imaging demonstrates that these large-scale modular correlation patterns and fractures are already present at early stages of cortical development and predictive of the mature network structure. Silencing feed-forward drive through either retinal or thalamic blockade does not affect network structure suggesting a cortical origin for this large-scale correlated activity, despite the immaturity of long-range horizontal network connections in the early cortex. Using a circuit model containing only local connections, we demonstrate that such a circuit is sufficient to generate large-scale correlated activity, while also producing correlated networks showing strong fractures, a reduced dimensionality, and an elongated local correlation structure, all in close agreement with our empirical data. These results demonstrate the precise local and global organization of cortical networks revealed through correlated spontaneous activity and suggest that local connections in early cortical circuits may generate structured long-range network correlations that underlie the subsequent formation of visually-evoked distributed functional networks.

Distributed network interactions and their emergence in developing neocortex

The principles governing the functional organization and development of long-range network interactions in the neocortex remain poorly understood. Using in vivo widefield and two-photon calcium imaging of spontaneous activity patterns in mature ferret visual cortex, we find widespread modular correlation patterns that accurately predict the local structure of visually evoked orientation columns several millimeters away. Longitudinal imaging demonstrates that long-range spontaneous correlations are present early in cortical development before the elaboration of horizontal connections and predict mature network structure. Silencing feedforward drive through retinal or thalamic blockade does not eliminate early long-range correlated activity, suggesting a cortical origin. Circuit models containing only local, but heterogeneous, connections are sufficient to generate long-range correlated activity by confining activity patterns to a low-dimensional subspace via multisynaptic short-range interactions. These results suggest that local connections in early cortical circuits can generate structured long-range network correlations that guide the formation of visually evoked distributed functional networks.Distributed networks in visual cortex precisely link the fine-scale functional architecture with distant network elements and appear early in development, when heterogeneous local connections may seed long-range network interactions.

Influence of recurrent interactions on texture processing in networks with different visual map organizations

The functional architecture of the visual cortex displays marked differences across mammalian species: in stark contrast to primates, in which the preferred stimulus orientation forms an almost smooth map across the cortical surface, in rodents a salt-and-pepper organization has been observed [1]. It is conceivable that the organization of preferred orientation has an impact on the processing of visual input. Recently [2], we found that in a biologically inspired object recognition system with a pure feed forward network architecture, smooth orientation maps outperform the salt-and-pepper organization in a texture recognition task. Here, we extend this work to study the effect of recurrent connections on neuronal response properties and texture recognition, comparing the two types of cortical architectures. n nOur model is a recurrent single-layer rate network. The feed forward connections to each neuron are properly oriented Gabor-filters. Recurrent connections between two neurons depend both on the spatial distance between these neurons and on their orientation selectivities. Inspired by [3], the angle-dependent interaction function is weighted by the product of selectivities of both neurons and enables excitation between cells with similar preferred orientations and inhibition between cells with orthogonal ones. We also study the effects of purely distant-dependent recurrent connectivity of Mexican-hat type, with spatial extent related to local column spacing in case of smooth map layout. We design a network for the salt-and-pepper organization in an analogous way, assuming the same spatial extent of connectivity and its tuning-dependence as observed in mouse visual cortex [4]. n nVarying the strength and selectivity of recurrent versus feed-forward connections, we first explore the influence of recurrence on the orientation selectivity. For that purpose, we drive the network with oriented gratings to reconstruct the selectivity from the activities. In agreement with [5] we observe sharpening of the orientation tuning of the network by recurrent interactions, such that oriented stimuli can be well discriminated even with weak feed-forward tuning. We further study the role of recurrent connections in processing more complex stimuli. We present visual textures to the model, then feed the responses into a classifier (linear SVM) that learns to predict a class label. This allows us to study how differences in feed-forward and recurrent connections impact texture classification, and to compare the orientation-preference map and salt-and-pepper organization in texture recognition tasks.

Discrete cortical representations and their stability in the presence of synaptic turnover

Population imaging in mouse auditory cortex revealed clustering of neural responses to brief complex sounds: the activity of a local population typically falls close to one out of a small number of observed states [1]. These clusters appear to group sets of auditory stimuli into a discrete set of activity patterns and could thereby form the basis for representations of sound categories. However, to be useful for the brain, such representations should be robust against fluctuations in the underlying circuitry, which are significant even in the absences of any explicit learning paradigm [2]. Here we introduce a novel firing rate based circuit model of mouse auditory cortex to study the emergence of the observed activity cluster states and their structural stability in the presence of synaptic noise. We find that generic random networks by virtue of their inhibitory recurrent connectivity can group complex sounds spontaneously into essentially discrete sets of activity states. Moreover, these states can display high degrees of stability, even when modifying a substantial fraction of synaptic connections, as long as the basic statistics of connectivity is maintained. We use the insights gained from the analysis of our model to interpret data gathered in a parallel effort, employing chronic two-photon imaging of population activity in the auditory cortex of awake mice.

The emergence of cohorts of co-active neurons in random recurrent networks provides a mechanism for orientation and direction selectivity

Poster presentation at The Twenty Third Annual Computational Neuroscience Meeting: CNS*2014 Quebec City, Canada. 26-31 July 2014: We study random strongly heterogeneous recurrent networks of firing rate neurons, introducing the notion of cohorts: groups of co-active neurons, who compete for firing with one another and whose presence depends sensitively on the structure of the input. The identities of neurons recruited to and dropped from an active cohort changes smoothly with varying input features. We search for network parameter regimes in which the activation of cohorts is robust yet easily switchable by the external input and which exhibit large repertoires of different cohorts. We apply these networks to model the emergence of orientation and direction selectivity in visual cortex. We feed these random networks with a set of harmonic inputs that vary across neurons only in their temporal phase, mimicking the feedforward drive due to a moving grating stimulus. The relationship between the phases that carries the information about the orientation of the stimulus determines which cohort of neurons is activated. As a result the individual neurons acquire non-monotonic orientation tuning curves which are characterized by high orientation and direction selectivity. This mechanism of emergence for direction selectivity differs from the classical motion detector scheme, which is based on the nonlinear summation of the time-shifted inputs. In our model these two mechanisms coexist in the same network, but can be distinguished by their different frequency and contrast dependences. In general, the mechanism we are studying here converts temporal phase sequence into population activity and could therefore be used to extract and represent also various other relevant stimulus features.