Neuroscience seminars

The development and maintenance of many tissues are fueled by stem cells. Many studies have addressed how intrinsic factors and local signals from neighboring niche cells maintain stem cell identity and proliferative potential. In contrast, it is poorly understood how stem cell activity is controlled by systemic, tissue-extrinsic signals in response to environmental cues and changes in physiological status. Our laboratory has been focusing on female germline stem cells (fGSCs) in the fruit fly Drosophila melanogaster as a model system and studying neuroendocrine control of fGSC increase. The increase of fGSCs is induced by mating stimuli. We have previously reported that mating-induced fGSC increase is regulated by the ovarian steroid hormone and the enteroendocrine peptide hormone [Ameku & Niwa, PLOS Genetics 2016; Ameku et al. PLOS Biology 2018]. In this presentation, we report our recent finding showing a neuronal mechanism of mating-induced fGSC increase. We first found that the ovarian somatic cell-specific RNAi for Oamb, a G protein-coupled receptor for the neurotransmitter octopamine, failed to induce fGSC proliferation after mating. Both ex vivo and in vivo experiments revealed that octopamine and Oamb positively regulated mating-induced fGSC increase via intracellular Ca 2+ signaling. We also found that a small subset of octopaminergic neurons directly projected to the ovary, and neuronal activity of these neurons was required for mating-induced fGSC increase. This study provides a mechanism describing how the neuronal system controls stem cell behavior through stem cell niche signaling [Yoshinari et al. eLife 2020]. Here I will also present our recent data showing how the neuroendocrine system couples fGSC behavior to multiple environmental cues, such as mating and nutrition.

The establishment of synaptic connections is essential for normal brain function, yet the molecular mechanisms responsible for the precise connectivity of specific neural circuits remain largely unknown. Previous work has shown that the assembly of cortical circuits requires specific functions of molecular signalling complexes at different classes of synapses. In this talk, I will describe the molecular logic through which specific pyramidal cell-interneuron circuits are established in the cerebral cortex of the mouse, and how alterations in some of these connectivity motifs might be liked to disease.

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known if the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher-order visual areas and measured stimulus discrimination thresholds of 0.35 degrees and 0.37 degrees respectively in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, the behavioral variability during a sensory discrimination task could not be explained by neural variability in primary visual cortex. Instead behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that sensory perception in mice is limited by downstream decoders, not by neural noise in sensory representations.

Learning a new skill requires assimilating into our brain the regularities of the external world and how our body interacts with them as we engage in this skill. Trained Recurrent Neural Networks (TRNNs) are increasingly used as models of neural circuits of animals that were trained in laboratory setups, but the learning process itself has received less attention. Furthermore, most use of TRNNs is of a heuristic, rather than theory-based, nature, leaving many open questions: Which tasks yield to this approach and why? How do initial network architecture and learning rules bias the resultant network? In this talk, I will argue that studying the learning process of TRNNs can both advance our understanding of TRNNs and set up possible comparisons to the biological process of learning. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2021-01-06. Recording duration: 00:42:41.

The link between behavior, learning and the underlying connectome is a fundamental open problem in neuroscience. In my talk I will show how it is possible to develop a theory that bridges across these three levels (animal behavior, learning and network connectivity) based on the geometrical properties of neural activity. The central tool in my approach is the dimensionality of neural activity. I will link animal complex behavior to the geometry of neural representations, specifically their dimensionality; I will then show how learning shapes changes in such geometrical properties and how local connectivity properties can further regulate them. As a result, I will explain how the complexity of neural representations emerges from both behavioral demands (top-down approach) and learning or connectivity features (bottom-up approach). I will build these results regarding neural dynamics and representations starting from the analysis of neural recordings, by means of theoretical and computational tools that blend dynamical systems, artificial intelligence and statistical physics approaches.

Human vision flaunts a remarkable ability to recognize objects in the surrounding environment even in the absence of complete visual representation of these objects. This process is done almost intuitively and it was not until scientists had to tackle this problem in computer vision that they noticed its complexity. While current advances in artificial vision systems have made great strides exceeding human level in normal vision tasks, it has yet to achieve a similar robustness level. One cause of this robustness is the extensive connectivity that is not limited to a feedforward hierarchical pathway similar to the current state-of-the-art deep convolutional neural networks but also comprises recurrent and top-down connections. They allow the human brain to enhance the neural representations of degraded images in concordance with meaningful representations stored in memory. The mechanisms by which these different pathways interact are still not understood. In this seminar, studies concerning the effect of recurrent and top-down modulation on the neural representations resulting from viewing blurred images will be presented. Those studies attempted to uncover the role of recurrent and top-down connections in human vision. The results presented challenge the notion of predictive coding as a mechanism for top-down modulation of visual information during natural vision. They show that neural representation enhancement (sharpening) appears to be a more dominant process of different levels of visual hierarchy. They also show that inference in visual recognition is achieved through a Bayesian process between incoming visual information and priors from deeper processing regions in the brain.

Historically, research on the brain has been working its way in from the outside world, hoping that such systematic exploration will take us some day to the middle and on through the middle to the output. Ever since the time of Aristotle, philosophers and scientists have assumed that the brain (or, more precisely, the mind) is initially a blank slate filled up gradually with experience in an outside-in manner. An alternative, brain-centric view, the one I am promoting, is that self-organized brain networks induce a vast repertoire of preformed neuronal patterns. While interacting with the world, some of these initially ‘nonsensical’ patterns acquire behavioral significance or meaning. Thus, experience is primarily a process of matching preexisting neuronal dynamics to events in the world. I suggest that perpetually active, internal dynamic is the source of cognition, a neuronal operation disengaged from immediate senses.