Neural system identification for large populations separating "what" and "where"

Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of 'what' and 'where'. Learning deep convolutional feature spaces that are shared among many neurons provides an exciting path forward, but the architectural design needs to account for data limitations: While new experimental techniques enable recordings from thousands of neurons, experimental time is limited so that one can sample only a small fraction of each neuron's response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural data is the estimation of the individual receptive field locations, a problem that has been scratched only at the surface thus far. We propose a CNN architecture with a sparse readout layer factorizing the spatial (where) and feature (what) dimensions. Our network scales well to thousands of neurons and short recordings and can be trained end-to-end. We evaluate this architecture on ground-truth data to explore the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system identification models of mouse primary visual cortex.Comment: NIPS 201

A video-driven model of response statistics in the primate middle temporal area

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.neunet.2018.09.004 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Neurons in the primate middle temporal area (MT) encode information about visual motion and binocular disparity. MT has been studied intensively for decades, so there is a great deal of information in the literature about MT neuron tuning. In this study, our goal is to consolidate some of this information into a statistical model of the MT population response. The model accepts arbitrary stereo video as input. It uses computer-vision methods to calculate known correlates of the responses (such as motion velocity), and then predicts activity using a combination of tuning functions that have previously been used to describe data in various experiments. To construct the population response, we also estimate the distributions of many model parameters from data in the electrophysiology literature. We show that the model accounts well for a separate dataset of MT speed tuning that was not used in developing the model. The model may be useful for studying relationships between MT activity and behavior in ethologically relevant tasks. As an example, we show that the model can provide regression targets for internal activity in a deep convolutional network that performs a visual odometry task, so that its representations become more physiologically realistic.MitacsCrossWing In

Learning Equivariant Representations

State-of-the-art deep learning systems often require large amounts of data and computation. For this reason, leveraging known or unknown structure of the data is paramount. Convolutional neural networks (CNNs) are successful examples of this principle, their defining characteristic being the shift-equivariance. By sliding a filter over the input, when the input shifts, the response shifts by the same amount, exploiting the structure of natural images where semantic content is independent of absolute pixel positions. This property is essential to the success of CNNs in audio, image and video recognition tasks. In this thesis, we extend equivariance to other kinds of transformations, such as rotation and scaling. We propose equivariant models for different transformations defined by groups of symmetries. The main contributions are (i) polar transformer networks, achieving equivariance to the group of similarities on the plane, (ii) equivariant multi-view networks, achieving equivariance to the group of symmetries of the icosahedron, (iii) spherical CNNs, achieving equivariance to the continuous 3D rotation group, (iv) cross-domain image embeddings, achieving equivariance to 3D rotations for 2D inputs, and (v) spin-weighted spherical CNNs, generalizing the spherical CNNs and achieving equivariance to 3D rotations for spherical vector fields. Applications include image classification, 3D shape classification and retrieval, panoramic image classification and segmentation, shape alignment and pose estimation. What these models have in common is that they leverage symmetries in the data to reduce sample and model complexity and improve generalization performance. The advantages are more significant on (but not limited to) challenging tasks where data is limited or input perturbations such as arbitrary rotations are present

Neural system identification for large populations separating “what” and “where”

Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional neural system identification methods do not capitalize on this separation of “what” and “where”. Learning deep convolutional feature spaces shared among many neurons provides an exciting path forward, but the architectural design needs to account for data limitations: While new experimental techniques enable recordings from thousands of neurons, experimental time is limited so that one can sample only a small fraction of each neuron’s response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural data is the estimation of the individual receptive field locations – a problem that has been scratched only at the surface thus far. We propose a CNN architecture with a sparse pooling layer factorizing the spatial (where) and feature (what) dimensions. Our network scales well to thousands of neurons and short recordings and can be trained end-to-end. We explore this architecture on ground-truth data to explore the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system identification models in the mouse visual system

Neuromodulatory effects on early visual signal processing

Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brain’s ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells. In summary, I first present several experimental and computational methods that allow to study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning