pwc-archive/papers-with-abstracts huggingface dataset:
Significance of feedforward architectural differences between the ventral visual stream and DenseNet
Authors: Bryan Tripp
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Object Recognition

There are many differences between convolutional networks and the ventral visual streams of primates. For example, standard convolutional networks lack recurrent and lateral connections, cell dynamics, etc. However, their feedforward architectures are somewhat similar to the ventral stream, and warrant a more detailed comparison. A recent study found that the feedforward architecture of the visual cortex could be closely approximated as a convolutional network, but the resulting architecture differed from widely used deep networks in several ways. The same study also found, somewhat surprisingly, that training the ventral stream of this network for object recognition resulted in poor performance. This paper examines the performance of this network in more detail. In particular, I made a number of changes to the ventral-stream-based architecture, to make it more like a DenseNet, and tested performance at each step. I chose DenseNet because it has a high BrainScore, and because it has some cortex-like architectural features such as large in-degrees and long skip connections. Most of the changes (which made the cortex-like network more like DenseNet) improved performance. Further work is needed to better understand these results. One possibility is that details of the ventral-stream architecture may be ill-suited to feedforward computation, simple processing units, and/or backpropagation, which could suggest differences between the way high-performance deep networks and the brain approach core object recognition.
Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity
Authors: Daniel Zdeblick, Eric Shea-Brown, Daniela Witten, Michael Buice
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Diversity

Computational neuroscience aims to fit reliable models of in vivo neural activity and interpret them as abstract computations. Recent work has shown that functional diversity of neurons may be limited to that of relatively few cell types; other work has shown that incorporating constraints into artificial neural networks (ANNs) can improve their ability to mimic neural data. Here we develop an algorithm that takes as input recordings of neural activity and returns clusters of neurons by cell type and models of neural activity constrained by these clusters. The resulting models are both more predictive and more interpretable, revealing the contributions of functional cell types to neural computation and ultimately informing the design of future ANNs.
Evaluating biological plausibility of learning algorithms the lazy way
Authors: Owen Marschall, Kyunghyun Cho, Cristina Savin
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

To which extent can successful machine learning inform our understanding of biological learning? One popular avenue of inquiry in recent years has been to directly map such algorithms into a realistic circuit implementation. Here we focus on learning in recurrent networks and investigate a range of learning algorithms. Our approach decomposes them into their computational building blocks and discusses their abstract potential as biological operations. This alternative strategy provides a “lazy” but principled way of evaluating ML ideas in terms of their biological plausibility
The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units
Authors: Dean A. Pospisil, Wyeth Bair
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Object Recognition

A central goal in the study of the primate visual cortex and hierarchical models for object recognition is understanding how and why single units trade off invariance versus sensitivity to image transformations. For example, in both deep networks and visual cortex there is substantial variation from layer-to-layer and unit-to-unit in the degree of translation invariance. Here, we provide theoretical insight into this variation and its consequences for encoding in a deep network. Our critical insight comes from the fact that rectification simultaneously decreases response variance and correlation across responses to transformed stimuli, naturally inducing a positive relationship between invariance and dynamic range. Invariant input units then tend to drive the network more than those sensitive to small image transformations. We discuss consequences of this relationship for AI: deep nets naturally weight invariant units over sensitive units, and this can be strengthened with training, perhaps contributing to generalization performance. Our results predict a signature relationship between invariance and dynamic range that can now be tested in future neurophysiological studies.
Revealing computational mechanisms of retinal prediction via model reduction
Authors: Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen A. Baccus, Surya Ganguli
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Dimensionality Reduction, Prediction

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 {\it 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.
Convolutionary, Evolutionary, Revolutionary: What’s next for Bodies, Brains and AI?
Authors: Peter Stratton
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

In recent years we have made significant progress identifying computational principles that underlie neural function. While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity, and which could potentially be used to construct new AI technologies with unique capabilities. I discuss the relevant principles, the advantages they have for computation, and how they can benefit AI. Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.
Flexible degrees of connectivity under synaptic weight constraints
Authors: Gabriel Koch Ocker, Michael A. Buice
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

Biological neural networks face homeostatic and resource constraints that restrict the allowed configurations of connection weights. If a constraint is tight it defines a very small solution space, and the size of these constraint spaces determines their potential overlap with the solutions for computational tasks. We study the geometry of the solution spaces for constraints on neurons' total synaptic weight and on individual synaptic weights, characterizing the connection degrees (numbers of partners) that maximize the size of these solution spaces. We then hypothesize that the size of constraints' solution spaces could serve as a cost function governing neural circuit development. We develop analytical approximations and bounds for the model evidence of the maximum entropy degree distributions under these cost functions. We test these on a published electron microscopic connectome of an associative learning center in the fly brain, finding evidence for a developmental progression in circuit structure.
Spike Sorting using the Neural Clustering Process
Authors: Yueqi Wang, Ari Pakman, Catalin Mitelut, JinHyung Lee, Liam Paninski
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Bayesian Inference, Clustering, GPU, Spike Sorting

We present a novel approach to spike sorting for high-density multielectrode probes using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering. To optimally encode spike waveforms for clustering, we extended NCP by adding a convolutional spike encoder, which is learned end-to-end with the NCP network. Trained purely on labeled synthetic spikes from a simple generative model, the NCP spike sorting model shows promising performance for clustering multi-channel spike waveforms. The model provides higher clustering quality than an alternative Bayesian algorithm, finds more spike templates with clear receptive fields on real data and recovers more ground truth neurons on hybrid test data compared to a recent spike sorting algorithm. Furthermore, NCP is able to handle the clustering uncertainty of ambiguous small spikes by GPU-parallelized posterior sampling. The source code is publicly available.
Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory
Authors: Robert Kim, Terrence J. Sejnowski
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

Cortical neurons process and integrate information on multiple timescales. In addition, these timescales or temporal receptive fields display functional and hierarchical organization. For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields and long timescales to support reliable representations of stimuli. Despite of the recent advances in experimental techniques, the underlying mechanisms for the emergence of neuronal timescales long enough to support WM are unclear and challenging to investigate experimentally. Here, we demonstrate that spiking recurrent neural networks (RNNs) designed to perform a WM task reproduce previously observed experimental findings and that these models could be utilized in the future to study how neuronal timescales specific to WM emerge.
Adversarial Training of Neural Encoding Models on Population Spike Trains
Authors: Poornima Ramesh, Mohamad Atayi, Jakob H Macke
Date: 2019-09-13 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

Neural population responses to sensory stimuli can exhibit both nonlinear stimulus- dependence and richly structured shared variability. Here, we show how adversarial training can be used to optimize neural encoding models to capture both the deterministic and stochastic components of neural population data. To account for the discrete nature of neural spike trains, we use the REBAR method to estimate unbiased gradients for adversarial optimization of neural encoding models. We illustrate our approach on population recordings from primary visual cortex. We show that adding latent noise-sources to a convolutional neural network yields a model which captures both the stimulus-dependence and noise correlations of the population activity.
Coordinate-VAE: Unsupervised clustering and de-noising of peripheral nervous system data
Authors: Thomas J Hardcastle, Susannah Lee, Lorenz Wernisch, Pascal Fortier-Poisson, Sudha Shunmugam, Kalon Hewage, Tris Edwards, Oliver Armitage, Emil Hewage
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Clustering

The peripheral nervous system represents the input/output system for the brain. Cuff electrodes implanted on the peripheral nervous system allow observation and control over this system, however, the data produced by these electrodes have a low signal-to-noise ratio and a complex signal content. In this paper, we consider the analysis of neural data recorded from the vagus nerve in animal models, and develop an unsupervised learner based on convolutional neural networks that is able to simultaneously de-noise and cluster regions of the data by signal content.
Functional Annotation of Human Cognitive States using Graph Convolution Networks
Authors: Yu Zhang, Pierre Bellec
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Time Series, Time Series Analysis, Transfer Learning

A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is to study “brain states” dynamics using functional magnetic resonance imaging (fMRI). So far in the literature, brain states have typically been studied using 30 seconds of fMRI data or more, and it is unclear to which extent brain states can be reliably identified from very short time series. In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used. Starting with a populational brain graph with nodes defined by a parcellation of cerebral cortex and the adjacent matrix extracted from functional connectome, GCN takes a short series of fMRI volumes as input, generates high-level domain-specific graph representations, and then predicts the corresponding cognitive state. We investigated the performance of this GCN "cognitive state annotation" in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution task fMRI data. Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 89% (chance level 4.8%). As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable as a base model for other transfer learning applications, for instance, adapting to new task domains.
Inferring hierarchies of latent features in calcium imaging data
Authors: Luke Y. Prince, Blake A. Richards
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems. One example of this is in-vivo calcium imaging data, where observed calcium transients are driven by a combination of electro-chemical kinetics where hypothesized trajectories around manifolds determining the frequency of these transients. A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamic structure of reaching behaviour from spiking data modelled as a Poisson process. Here we extend this approach using a ladder method to infer the spiking events driving calcium transients along with the deeper latent dynamic system. We show strong performance of this approach on a benchmark synthetic dataset against a number of alternatives.
Unsupervised Discovery of Dynamic Neural Circuits
Authors: Colin Graber, Ryan Loh, Yurii Vlasov, Alexander Schwing
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

What can we learn about the functional organization of cortical microcircuits from large-scale recordings of neural activity?  To obtain an explicit and interpretable model of time-dependent functional connections between neurons and to establish the dynamics of the cortical information flow, we develop 'dynamic neural relational inference' (dNRI). We study  both synthetic and real-world neural spiking data and demonstrate that the developed method is able to uncover the dynamic relations between neurons more reliably than existing baselines.
Checking Functional Modularity in DNN By Biclustering Task-specific Hidden Neurons
Authors: Jialin Lu, Martin Ester
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

While real brain networks exhibit functional modularity, we investigate whether functional mod- ularity also exists in Deep Neural Networks (DNN) trained through back-propagation. Under the hypothesis that DNN are also organized in task-specific modules, in this paper we seek to dissect a hidden layer into disjoint groups of task-specific hidden neurons with the help of relatively well- studied neuron attribution methods. By saying task-specific, we mean the hidden neurons in the same group are functionally related for predicting a set of similar data samples, i.e. samples with similar feature patterns.
We argue that such groups of neurons which we call Functional Modules can serve as the basic functional unit in DNN. We propose a preliminary method to identify Functional Modules via bi- clustering attribution scores of hidden neurons.
We find that first, unsurprisingly, the functional neurons are highly sparse, i.e., only a small sub- set of neurons are important for predicting a small subset of data samples and, while we do not use any label supervision, samples corresponding to the same group (bicluster) show surprisingly coherent feature patterns. We also show that these Functional Modules perform a critical role in discriminating data samples through ablation experiment.
Disentangling the roles of dimensionality and cell classes in neural computations
Authors: Alexis M Dubreuil, Adrian Valente, Francesca Mastrogiuseppe, Srdjan Ostojic
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

The description of neural computations in the field of neuroscience relies on two competing views: (i) a classical single-cell view that relates the activity of individual neurons to sensory or behavioural variables, and focuses on how different cell classes map onto computations; (ii) a more recent population view that instead characterises computations in terms of collective neural trajectories, and focuses on the dimensionality of these trajectories as animals perform tasks. How the two key concepts of cell classes and low-dimensional trajectories interact to shape neural computations is however currently not understood. Here we address this question by combining machine-learning tools for training RNNs with reverse-engineering and theoretical analyses of network dynamics. We introduce a novel class of theoretically tractable recurrent networks: low-rank, mixture of Gaussian RNNs. In these networks, the rank of the connectivity controls the dimensionality of the dynamics, while the number of components in the Gaussian mixture corresponds to the number of cell classes. Using back-propagation, we determine the minimum rank and number of cell classes needed to implement neuroscience tasks of increasing complexity. We then exploit mean-field theory to reverse-engineer the obtained solutions and identify the respective roles of dimensionality and cell classes. We show that the rank determines the phase-space available for dynamics that implement input-output mappings, while having multiple cell classes allows networks to flexibly switch between different types of dynamics in the available phase-space. Our results have implications for the analysis of neuroscience experiments and the development of explainable AI.
Pattern recognition of labeled concepts by a single spiking neuron model.
Authors: Hannes Rapp, Martin Paul Nawrot, Merav Stern
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Cloud Computing, Decision Making, Multiple Instance Learning

Making an informed, correct and quick decision can be life-saving. It's crucial for animals during an escape behaviour or for autonomous cars during driving. The decision can be complex and may involve an assessment of the amount of threats present and the nature of each threat. Thus, we should expect early sensory processing to supply classification information fast and accurately, even before relying the information to higher brain areas or more complex system components downstream. Today, advanced convolution artificial neural networks can successfully solve such tasks and are commonly used to build complex decision making systems. However, in order to achieve excellent performance on these tasks they require increasingly complex, "very deep" model structure, which is costly in inference run-time, energy consumption and number of training samples, only trainable on cloud-computing clusters.
A single spiking neuron has been shown to be able to solve many of these required tasks for homogeneous Poisson input statistics, a commonly used model for spiking activity in the neocortex; when modeled as leaky integrate and fire with gradient decent learning algorithm it was shown to posses a wide variety of complex computational capabilities. Here we refine its learning algorithm. The refined gradient-based local learning rule allows for better and stable generalization. We take advantage of this improvement to solve a problem of multiple instance learning (MIL) with counting where labels are only available for collections of concepts. We use an MNIST task  to show that the neuron indeed exploits the improvements and performs on par with conventional ConvNet architecture with similar parameter space size and number of training epochs.
Evolving the Olfactory System
Authors: Robert Guangyu Yang, Peter Yiliu Wang, Yi Sun, Ashok Litwin-Kumar, Richard Axel, LF Abbott
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

Flies and mice are species separated by 600 million years of evolution, yet have evolved olfactory systems that share many similarities in their anatomic and functional organization. What functions do these shared anatomical and functional features serve, and are they optimal for odor sensing? In this study, we address the optimality of evolutionary design in olfactory circuits by studying artificial neural networks trained to sense odors. We found that artificial neural networks quantitatively recapitulate structures inherent in the olfactory system, including the formation of glomeruli onto a compression layer and sparse and random connectivity onto an expansion layer. Finally, we offer theoretical justifications for each result. Our work offers a framework to explain the evolutionary convergence of olfactory circuits, and gives insight and logic into the anatomic and functional structure of the olfactory system.