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1. Population encoding of stimulus features along the visual hierarchy

   https://doi.org/10.1073/pnas.2317773121  

   Dyballa, Luciano; Rudzite, Andra M; Hoseini, Mahmood S; Thapa, Mishek; Stryker, Michael P; Field, Greg D; Zucker, Steven W (January 2024, Proceedings of the National Academy of Sciences)

   The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to the mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains. 

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2. Three-Dimensionally Printed Microsystems to Facilitate Flow-Based Study of Cells from Neurovascular Barriers of the Retina

   https://doi.org/10.3390/mi15091103  

   Leverant, Adam; Oprysk, Larissa; Dabrowski, Alexandra; Kyker-Snowman, Kelly; Vazquez, Maribel (September 2024, Micromachines)

   Rapid prototyping has produced accessible manufacturing methods that offer faster and more cost-effective ways to develop microscale systems for cellular testing. Commercial 3D printers are now increasingly adapted for soft lithography, where elastomers are used in tandem with 3D-printed substrates to produce in vitro cell assays. Newfound abilities to prototype cellular systems have begun to expand fundamental bioengineering research in the visual system to complement tissue engineering studies reliant upon complex microtechnology. This project used 3D printing to develop elastomeric devices that examined the responses of retinal cells to flow. Our experiments fabricated molds for elastomers using metal milling, resin stereolithography, and fused deposition modeling via plastic 3D printing. The systems were connected to flow pumps to simulate different flow conditions and examined phenotypic responses of endothelial and neural cells significant to neurovascular barriers of the retina. The results indicated that microdevices produced using 3D-printed methods demonstrated differences in cell survival and morphology in response to external flow that are significant to barrier tissue function. Modern 3D printing technology shows great potential for the rapid production and testing of retinal cell responses that will contribute to both our understanding of fundamental cell response and the development of new therapies. Future studies will incorporate varied flow stimuli as well as different extracellular matrices and expanded subsets of retinal cells. 

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3. From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

   Tanaka, H; Nayebi, A; Maheswaranathan, N; McIntosh, L; Baccus, S; Ganguli, S. (December 2019, Advances in neural information processing systems)

   null (Ed.)

   Recently, deep feedforward neural networks have achieved considerable success in modeling biological sensory processing, in terms of reproducing the input-output map of sensory neurons. However, such models raise profound questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (a biological circuit) with another (a deep network), without understanding either? Moreover, beyond neural representations, are the deep network's computational mechanisms for generating neural responses the same as those in the brain? Without a systematic approach to extracting and understanding computational mechanisms from deep neural network models, it can be difficult both to assess the degree of utility of deep learning approaches in neuroscience, and to extract experimentally testable hypotheses from deep networks. We develop such a systematic approach by combining dimensionality reduction and modern attribution methods for determining the relative importance of interneurons for specific visual computations. We apply this approach to deep network models of the retina, revealing a conceptual understanding of how the retina acts as a predictive feature extractor that signals deviations from expectations for diverse spatiotemporal stimuli. For each stimulus, our extracted computational mechanisms are consistent with prior scientific literature, and in one case yields a new mechanistic hypothesis. Thus overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms. 

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4. Toward a manifold encoding neural responses

   Dyballa, L.; Rudzite, A.; Hoseini, M.; Thapa, M.; Stryker, M.; Field, G; Steven Zucker (May 2023, MODVIS Workshop on Computational Models in Vision)

   Understanding circuit properties from physiological data presents two challenges: (i) recordings do not reveal connectivity, and (ii) stimuli only exercise circuits to a limited extent. We address these challenges for the mouse visual system with a novel neural manifold obtained using unsupervised algorithms. Each point in our manifold is a neuron; nearby neurons respond similarly in time to similar parts of a stimulus ensemble. This ensemble includes drifting gratings and flows, i.e., patterns resembling what a mouse would “see” running through fields. Regarding (i), our manifold differs from the standard practice in computational neuroscience: embedding trials in neural coordinates. Topology matters: we infer that, if the circuit consists of separate components, the manifold is discontinuous (illustrated with retinal data). If there is significant overlap between circuits, the manifold is nearly-continuous (cortical data). Regarding (ii), most of the cortical manifold is not activated with conventional gratings, despite their prominence in laboratory settings. Our manifold suggests organizing cortical circuitry by a few specialized circuits for specific members of the stimulus ensemble, together with circuits involving ‘multi-stimuli’-responding neurons. To approach real circuits, local neighborhoods in the manifold are identified with actual circuit components. For retinal data, we show these components correspond to distinct ganglion cell types by their mosaic-like receptive field organization, while for cortical data, neighborhoods organize neurons by type (excitatory/inhibitory) and anatomical layer. In summary: the topology of neural organization reflects well the underlying anatomy and physiology of the retina and the visual cortex. 

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5. Emergence of direction-selective retinal cell types in task-optimized deep learning models

   Murray, K.; Wang, B.; Lynch, N. (January 2022, Journal of computational biology)

   Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. Previous research has shown task-optimized CNNs to be capable of providing explanations as to why the retina efficiently encodes natural stimuli and how certain retinal cell types are involved in efficient encoding. In our work, we sought to use task-optimized CNNs as a means of explaining computational mechanisms responsible for motion-selective retinal circuits. We designed a biologically constrained CNN and optimized its performance on a motion-classification task. We drew inspiration from psychophysics, deep learning, and systems neuroscience literature to develop a toolbox of methods to reverse engineer the computational mechanisms learned in our model. Through reverse engineering our model, we proposed a computational mechanism in which direction-selective ganglion cells and starburst amacrine cells, both experimentally observed retinal cell types, emerge in our model to discriminate among moving stimuli. This emergence suggests that direction-selective circuits in the retina are ecologically designed to robustly discriminate among moving stimuli. Our results and methods also provide a framework for how to build more interpretable deep learning models and how to understand them. 

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