conferences_cleaned/neuroai19_papers.csv

paper_id,title,keywords,abstract,meta_review
1,"""Adversarial Training of Neural Encoding Models on Population Spike Trains""","['neural encoding models', 'neural variability', 'GANs', 'visual system', 'conditional GANs']","""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.""","""None"""
2,"""Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks""","['reservoir networks', 'recurrent neural networks', 'local rules', 'Hebbian rules', 'continuous attractors']","""Reservoir computing is a powerful tool to explain how the brain learns temporal sequences, such as movements, but existing learning schemes are either biologically implausible or too inefficient to explain animal performance. We show that a network can learn complicated sequences with a reward-modulated Hebbian learning rule if the network of reservoir neurons is combined with a second network that serves as a dynamic working memory and provides a spatio-temporal backbone signal to the reservoir. In combination with the working memory, reward-modulated Hebbian learning of the readout neurons performs as well as FORCE learning, but with the advantage of a biologically plausible interpretation of both the learning rule and the learning paradigm.""","""None"""
3,"""Coordinate-VAE: Unsupervised clustering and de-noising of peripheral nervous system data""","['Machine Learning', 'Peripheral Nervous System', 'Convolutional Neural Networks', 'Auto-encoder', 'Signal Processing']","""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.""","""None"""
4,"""Biologically-Inspired Spatial Neural Networks""","['deep learning', 'neuroscience', 'multi-task learning']","""We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions. To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space. Our experiments show that in the case where the network performs two different tasks, the neurons naturally split into clusters, where each cluster is responsible for processing a different task. This behavior not only corresponds to the biological systems, but also allows for further insight into interpretability or continual learning.""","""None"""
5,"""Whats in a functional brain parcellation?""","['brain atlases', 'functional units']","""To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain. Here we consider atlases used to parcellate the brain when studying brain function. We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various analytical tasks on popular functional brain parcellations.""","""None"""
6,"""Efficient rescue of damaged neural networks""","['neural networks', 'resilience', 'dynamical systems', 'attractors']","""Neural networks in the brain and in neuromorphic chips confer systems with the ability to perform multiple cognitive tasks. However, both kinds of networks experience a wide range of physical perturbations, ranging from damage to edges of the network to complete node deletions, that ultimately could lead to network failure. A critical question is to understand how the computational properties of neural networks change in response to node-damage and whether there exist strategies to repair these networks in order to compensate for performance degradation. Here, we study the damage-response characteristics of two classes of neural networks, namely multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) trained to classify images from MNIST and CIFAR-10 datasets respectively. We also propose a new framework to discover efficient repair strategies to rescue damaged neural networks. The framework involves defining damage and repair operators for dynamically traversing the neural networks loss landscape, with the goal of mapping its salient geometric features. Using this strategy, we discover features that resemble path-connected attractor sets in the loss landscape. We also identify that a dynamic recovery scheme, where networks are constantly damaged and repaired, produces a group of networks resilient to damage as it can be quickly rescued. Broadly, our work shows that we can design fault-tolerant networks by applying on-line retraining consistently during damage for real-time applications in biology and machine learning.""","""None"""
7,"""Functional Annotation of Human Cognitive States using Graph Convolution Networks""","['fMRI', 'functional connectivity', 'brain decoding', 'graph convolutional network', 'deep 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.""","""None"""
8,"""Revealing computational mechanisms of retinal prediction via model reduction""",[],"""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.""","""None"""
9,"""Convolutional neural networks with extra-classical receptive fields""","['lateral connections', 'convolutional neural networks', 'extra-classical receptive fields', 'mouse V1', 'supervised and unsupervised learning']","""In the visual system, neurons respond to a patch of the input known as their classical receptive field (RF), and can be modulated by stimuli in the surround. These interactions are often mediated by lateral connections, giving rise to extra-classical RFs. We use supervised learning via backpropagation to learn feedforward connections, combined with an unsupervised learning rule to learn lateral connections between units within a convolutional neural network. These connections allow each unit to integrate information from its surround, generating extra-classical receptive fields for the units in our new proposed model (CNNEx). We demonstrate that these connections make the network more robust and achieve better performance on noisy versions of the MNIST and CIFAR-10 datasets. Although the image statistics of MNIST and CIFAR-10 differ greatly, the same unsupervised learning rule generalized to both datasets. Our framework can potentially be applied to networks trained on other tasks, with the learned lateral connections aiding the computations implemented by feedforward connections when the input is unreliable.""","""None"""
10,"""Automated Animal Training and Iterative Inference of Latent Learning Policy""","['learning', 'neuroscience', 'behavior', 'automated training', 'latent learning', 'visual discrimination', 'automated analysis', 'reinforcement learning', 'behavior analysis', 'policy inference', 'behavior prediction']","""Progress in understanding how individual animals learn requires high-throughput standardized methods for behavioral training and ways of adapting training. During the course of training with hundreds or thousands of trials, an animal may change its underlying strategy abruptly, and capturing these changes requires real-time inference of the animals latent decision-making strategy. To address this challenge, we have developed an integrated platform for automated animal training, and an iterative decision-inference model that is able to infer the momentary decision-making policy, and predict the animals choice on each trial with an accuracy of ~80 even when the animal is performing poorly. We also combined decision predictions at single-trial resolution with automated pose estimation to assess movement trajectories. Analysis of these features revealed categories of movement trajectories that associate with decision confidence.""","""None"""
11,"""Translating neural signals to text using a Brain-Computer Interface""","['Brain-Computer interface', 'Speech decoding', 'Neural signal processing']","""Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. To this end, we aim to create a BCI that decodes text directly from neural signals. We implement a framework that initially isolates frequency bands in the input signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that feeds into an LSTM which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities incorporating prior knowledge of the English language to output text corresponding to the decoded word. Further, in producing an output, we abstain from constraining the reconstructed word to be from a given bag-of-words, unlike previous studies. The empirical success of our proposed approach, offers promise for the employment of such an interface by patients in unfettered, naturalistic environments.""","""None"""
12,"""Significance of feedforward architectural differences between the ventral visual stream and DenseNet""","['vision', 'primate', 'deep learning']","""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.""","""None"""
13,"""Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity""","['cell types', 'GLM', 'computational neuroscience', 'neural models']","""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.""","""None"""
14,"""Spike Sorting using the Neural Clustering Process""","['spike sorting', 'neural clustering process', 'bayesian clustering', 'amortization']","""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.""","""None"""
15,"""On the Adversarial Robustness of Neural Networks without Weight Transport""","['Neural networks without weight transport', 'gradient-based adversarial attacks']","""Neural networks trained with backpropagation, the standard algorithm of deep learning which uses weight transport, are easily fooled by existing gradient-based adversarial attacks. This class of attacks are based on certain small perturbations of the inputs to make networks misclassify them. We show that less biologically implausible deep neural networks trained with feedback alignment, which do not use weight transport, can be harder to fool, providing actual robustness. Tested on MNIST, deep neural networks trained without weight transport (1) have an adversarial accuracy of 98% compared to 0.03% for neural networks trained with backpropagation and (2) generate non-transferable adversarial examples. However, this gap decreases on CIFAR-10 but is still significant particularly for small perturbation magnitude less than 1 2.""","""None"""
16,"""Differentiating Granger Causal Influence and Stimulus-Related Information Flow""","['Information Flow', 'Granger Causality', 'Interpreting Network Activity', 'Connectivity']","""Information flow is becoming an increasingly popular term in the context of understanding neural circuitry, both in neuroscience and in Artificial Neural Networks. Granger causality has long been the tool of choice in the neuroscience literature for identifying functional connectivity in the brain, i.e., pathways along which information flows. However, there has been relatively little work on providing a fundamental theory for information flow, and as part of that, understanding whether Granger causality captures the intuitive direction of information flow in a computational circuit. Recently, Venkatesh et al. [2019] proposed a theoretical framework for identifying stimulus-related information paths in a computational graph. They also provided a counterexample showing that the direction of greater Granger causal influence can be opposite to that of information flow [Venkatesh and Grover, 2015]. Here, we reexamine and expand on this counterexample. In particular, we find that Granger Causal influence can be statistically insignificant in the direction of information flow, while being significant in the opposite direction. By examining the mutual- (and conditional-mutual-) information that each signal shares with the stimulus, we are able to gain a more nuanced understanding of the actual information flows in this system.""","""None"""
17,"""Learning Non-Parametric Invariances from Data with Permanent Random Connectomes """,[],"""One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data. Most prior art has focused on enforcing priors in the form of invariances to parametric nuisance transformations that are expected to be present in data. However, learning non-parametric invariances directly from data remains an important open problem. In this paper, we introduce a new architectural layer for convolutional networks which is capable of learning general invariances from data itself. This layer can learn invariance to non-parametric transformations and interestingly, motivates and incorporates permanent random connectomes there by being called Permanent Random Connectome Non-Parametric Transformation Networks (PRC-NPTN). PRC-NPTN networks are initialized with random connections (not just weights) which are a small subset of the connections in a fully connected convolution layer. Importantly, these connections in PRC-NPTNs once initialized remain permanent throughout training and testing. Random connectomes makes these architectures loosely more biologically plausible than many other mainstream network architectures which require highly ordered structures. We motivate randomly initialized connections as a simple method to learn invariance from data itself while invoking invariance towards multiple nuisance transformations simultaneously. We find that these randomly initialized permanent connections have positive effects on generalization, outperform much larger ConvNet baselines and the recently proposed Non-Parametric Transformation Network (NPTN) on benchmarks that enforce learning invariances from the data itself.""","""None"""
18,"""Foveated Downsampling Techniques""","['foveation', 'fovea', 'neuroscience', 'neural', 'network', 'downsampling', 'saliency', 'perception', 'brain']","""Foveation is an important part of human vision, and a number of deep networks have also used foveation. However, there have been few systematic comparisons between foveating and non-foveating deep networks, and between different variable-resolution downsampling methods. Here we define several such methods, and compare their performance on ImageNet recognition with a Densenet-121 network. The best variable-resolution method slightly outperforms uniform downsampling. Thus in our experiments, foveation does not substantially help or hinder object recognition in deep networks. ""","""None"""
19,"""Biologically Plausible Neural Networks via Evolutionary Dynamics and Dopaminergic Plasticity""","['biological plausibility', 'dopaminergic plasticity', 'allele frequency', 'neural net evolution']","""Artificial neural networks (ANNs) lack in biological plausibility, chiefly because backpropagation requires a variant of plasticity (precise changes of the synaptic weights informed by neural events that occur downstream in the neural circuit) that is profoundly incompatible with the current understanding of the animal brain. Here we propose that backpropagation can happen in evolutionary time, instead of lifetime, in what we call neural net evolution (NNE). In NNE the weights of the links of the neural net are sparse linear functions of the animals genes, where each gene has two alleles, 0 and 1. In each generation, a population is generated at random based on current allele frequencies, and it is tested in the learning task through minibatches. The relative performance of the two alleles of each gene is determined, and the allele frequencies are updated via the standard population genetics equations for the weak selection regime. We prove that, under assumptions, NNE succeeds in learning simple labeling functions with high probability, and with polynomially many generations and individuals per generation. NNE is also tested on MNIST with encouraging results. Finally, we explore a further version of biologically plausible ANNs (replacing backprop) inspired by the recent discovery of dopaminergic plasticity. ""","""None"""
20,"""Inferring hierarchies of latent features in calcium imaging data""","['calcium imaging', 'LFADS', 'variational autoencoders', 'dynamics', 'recurrent neural networks']","""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.""","""None"""
21,"""Learning to Learn with Feedback and Local Plasticity""","['biologically plausible learning', 'meta learning']","""Developing effective biologically plausible learning rules for deep neural networks is important for advancing connections between deep learning and neuroscience. To date, local synaptic learning rules like those employed by the brain have failed to match the performance of backpropagation in deep networks. In this work, we employ meta-learning to discover networks that learn using feedback connections and local, biologically motivated learning rules. Importantly, the feedback connections are not tied to the feedforward weights, avoiding any biologically implausible weight transport. It can be shown mathematically that this approach has sufficient expressivity to approximate any online learning algorithm. Our experiments show that the meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures. Moreover, we demonstrate empirically that this model outperforms a state-of-the-art gradient-based meta-learning algorithm for continual learning on regression and classification benchmarks. This approach represents a step toward biologically plausible learning mechanisms that can not only match gradient descent-based learning, but also overcome its limitations.""","""None"""
22,"""Eligibility traces provide a data-inspired alternative to backpropagation through time""","['neuroscience', 'plausible learning rules', 'spiking neurons', 'BPTT', 'recurrent neural networks', 'LSTM', 'RNN', 'computational neuroscience', 'backpropagation through time', 'online learning', 'real-time recurrent learning', 'RTRL', 'eligibility traces']","""Learning in recurrent neural networks (RNNs) is most often implemented by gradient descent using backpropagation through time (BPTT), but BPTT does not model accurately how the brain learns. Instead, many experimental results on synaptic plasticity can be summarized as three-factor learning rules involving eligibility traces of the local neural activity and a third factor. We present here eligibility propagation (e-prop), a new factorization of the loss gradients in RNNs that fits the framework of three factor learning rules when derived for biophysical spiking neuron models. When tested on the TIMIT speech recognition benchmark, it is competitive with BPTT both for training artificial LSTM networks and spiking RNNs. Further analysis suggests that the diversity of learning signals and the consideration of slow internal neural dynamics are decisive to the learning efficiency of e-prop.""","""None"""
23,"""Brain-inspired Robust Vision using Convolutional Neural Networks with Feedback""","['generative models', 'brain-inspired', 'robust vision']","""Humans have the remarkable ability to correctly classify images despite possible degradation. Many studies have suggested that this hallmark of human vision results from the interaction between feedforward signals from bottom-up pathways of the visual cortex and feedback signals provided by top-down pathways. Motivated by such interaction, we propose a new neuro-inspired model, namely Convolutional Neural Networks with Feedback (CNN-F). CNN-F extends CNN with a feedback generative network, combining bottom-up and top-down inference to perform approximate loopy belief propagation. We show that CNN-F's iterative inference allows for disentanglement of latent variables across layers. We validate the advantages of CNN-F over the baseline CNN. Our experimental results suggest that the CNN-F is more robust to image degradation such as pixel noise, occlusion, and blur. Furthermore, we show that the CNN-F is capable of restoring original images from the degraded ones with high reconstruction accuracy while introducing negligible artifacts.""","""None"""
24,"""Local Unsupervised Learning for Image Analysis""","['hebbian learning', 'local learning', 'orientation selectivity']","""We use a recently proposed biologically plausible local unsupervised training algorithm (Krotov & Hopfield, PNAS 2019) for learning convolutional filters from CIFAR-10 images. These filters combined with patch normalization and very steep non-linearities result in a good classification accuracy for shallow networks trained locally, as opposed to end-to-end. The filters learned by our algorithm contain both orientation selective units and unoriented color units, resembling the responses of pyramidal neurons located in the cytochrome oxidase interblob and blob regions in the primary visual cortex of primates. It is shown that convolutional networks with patch normalization significantly outperform standard convolutional networks on the task of recovering the original classes when shadows are superimposed on top of standard CIFAR-10 images. Patch normalization approximates the retinal adaptation to the mean light intensity, important for human vision. All these results taken together suggest a possibility that local unsupervised training might be a useful tool for learning general representations (without specifying the task) directly from unlabeled data. ""","""None"""
25,"""Evaluating biological plausibility of learning algorithms the lazy way""","['Machine learning', 'back propagation through time', 'biological plausibility', 'online learning']","""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""","""None"""
26,"""Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules""","['Hebbian learning', 'deep learning optimization', 'metalearning', 'learning to learn']","""The brain performs unsupervised learning and (perhaps) simultaneous supervised learning. This raises the question as to whether a hybrid of supervised and unsupervised methods will produce better learning. Inspired by the rich space of Hebbian learning rules, we set out to directly learn the unsupervised learning rule on local information that best augments a supervised signal. We present the Hebbian-augmented training algorithm (HAT) for combining gradient-based learning with an unsupervised rule on pre-synpatic activity, post-synaptic activities, and current weights. We test HAT's effect on a simple problem (Fashion-MNIST) and find consistently higher performance than supervised learning alone. This finding provides empirical evidence that unsupervised learning on synaptic activities provides a strong signal that can be used to augment gradient-based methods. We further find that the meta-learned update rule is a time-varying function; thus, it is difficult to pinpoint an interpretable Hebbian update rule that aids in training. We do find that the meta-learner eventually degenerates into a non-Hebbian rule that preserves important weights so as not to disturb the learner's convergence.""","""None"""
27,"""Learning to solve the credit assignment problem""","['biologically plausible deep learning', 'feedback alignment', 'REINFORCE', 'node perturbation']","""Backpropagation is driving today's artificial neural networks. However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative. However, the convergence rate of such learning scales poorly with the number of involved neurons. Here we propose a hybrid learning approach, in which each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide. We show that our approach learns to approximate the gradient, and can match the performance of gradient-based learning on fully connected and convolutional networks. Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules.""","""None"""
28,"""Predictive Coding, Variational Autoencoders, and Biological Connections""","['predictive coding', 'variational autoencoders', 'probabilistic models', 'variational inference']","""Predictive coding, within theoretical neuroscience, and variational autoencoders, within machine learning, both involve latent Gaussian models and variational inference. While these areas share a common origin, they have evolved largely independently. We outline connections and contrasts between these areas, using their relationships to identify new parallels between machine learning and neuroscience. We then discuss specific frontiers at this intersection: backpropagation, normalizing flows, and attention, with mutual benefits for both fields.""","""None"""
29,"""Recurrent neural networks learn robust representations by dynamically balancing compression and expansion""","['Recurrent Neural Network', 'Temporal Learning', 'Chaotic Dynamics', 'Dimensionality', 'Working Memory']","""Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics. What determines the dimensionality of activity in neural circuits? What is the functional role of dimensionality in behavior and task learning? In this work we address these questions using recurrent neural network (RNN) models. We find that, depending on the dynamics of the initial network, RNNs learn to increase and reduce dimensionality in a way that matches task demands. These findings shed light on fundamental dynamical mechanisms by which neural networks solve tasks with robust representations that generalize to new cases.""","""None"""
30,"""Learning a Convolutional Bilinear Sparse Code for Natural Videos""","['Unsupervised Learning', 'Spatio-Temporal Features', 'Sparse Coding', 'Equivariance', 'Capsules']","""In contrast to the monolithic deep architectures used in deep learning today for computer vision, the visual cortex processes retinal images via two functionally distinct but interconnected networks: the ventral pathway for processing object-related information and the dorsal pathway for processing motion and transformations. Inspired by this cortical division of labor and properties of the magno- and parvocellular systems, we explore an unsupervised approach to feature learning that jointly learns object features and their transformations from natural videos. We propose a new convolutional bilinear sparse coding model that (1) allows independent feature transformations and (2) is capable of processing large images. Our learning procedure leverages smooth motion in natural videos. Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner. The learned ""dynamic filters"" exhibit certain equivariance properties, resemble cortical spatiotemporal filters, and capture the statistics of transitions between video frames. Our model can be viewed as one of the first approaches to demonstrate unsupervised learning of primary ""capsules"" (proposed by Hinton and colleagues for supervised learning) and has strong connections to the Lie group approach to visual perception.""","""None"""
31,"""Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks""","['Network Neuroscience', 'neurons', 'brain', 'visual cortex', 'Deep Learning', 'mouse visual cortex', 'C. Elegans']","""The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understanding the remarkable efficacy of biological neural networks. Curiously, the deep learning revolution that revived neural networks has not paid much attention to topological aspects. The architectures of deep neural networks (DNNs) do not resemble their biological counterparts in the topological sense. We bridge this gap by presenting initial results of Deep Connectomics Networks (DCNs) as DNNs with topologies inspired by real-world neuronal networks. We show high classification accuracy obtained by DCNs whose architecture was inspired by the biological neuronal networks of C. Elegans and the mouse visual cortex.""","""None"""
32,"""Unsupervised Discovery of Dynamic Neural Circuits""","['dynamic neural relational inference', 'variational autoencoder', 'cortical processing', 'neural dynamics', 'brain computation']","""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.""","""None"""
33,"""Tracking momentary attention fluctuations with an EEG-based cognitive brain-machine interface""","['BCI', 'attention', 'dimensionality reduction', 'subspaces', 'EEG', 'SSVEP', 'DSS']","""Momentary fluctuations in attention (perceptual accuracy) correlate with neural activity fluctuations in primate visual areas. Yet, the link between such momentary neural fluctuations and attention state remains to be shown in the human brain. We investigate this link using a real-time cognitive brain machine interface (cBMI) based on steady state visually evoked potentials (SSVEPs): occipital EEG potentials evoked by rhythmically flashing stimuli. Tracking momentary fluctuations in SSVEP power, in real-time, we presented stimuli time-locked to when this power reached (predetermined) high or low thresholds. We observed a significant increase in discrimination accuracy (d') when stimuli were triggered during high (versus low) SSVEP power epochs, at the location cued for attention. Our results indicate a direct link between attentions effects on perceptual accuracy and and neural gain in EEG-SSVEP power, in the human brain. ""","""None"""
34,"""Modelling Working Memory using Deep Recurrent Reinforcement Learning""","['deep learning', 'working memory', 'recurrent neural networks', 'reinforcement learning', 'brain modelling']","""In cognitive systems, the role of a working memory is crucial for visual reasoning and decision making. Tremendous progress has been made in understanding the mechanisms of the human/animal working memory, as well as in formulating different frameworks of artificial neural networks. In the case of humans, the visual working memory (VWM) task is a standard one in which the subjects are presented with a sequence of images, each of which needs to be identified as to whether it was already seen or not. Our work is a study of multiple ways to learn a working memory model using recurrent neural networks that learn to remember input images across timesteps. We train these neural networks to solve the working memory task by training them with a sequence of images in supervised and reinforcement learning settings. The supervised setting uses image sequences with their corresponding labels. The reinforcement learning setting is inspired by the popular view in neuroscience that the working memory in the prefrontal cortex is modulated by a dopaminergic mechanism. We consider the VWM task as an environment that rewards the agent when it remembers past information and penalizes it for forgetting. We quantitatively estimate the performance of these models on sequences of images from a standard image dataset (CIFAR-100). Further, we evaluate their ability to remember and recall as they are increasingly trained over episodes. Based on our analysis, we establish that a gated recurrent neural network model with long short-term memory units trained using reinforcement learning is powerful and more efficient in temporally consolidating the input spatial information. This work is an initial analysis as a part of our ultimate goal to use artificial neural networks to model the behavior and information processing of the working memory of the brain and to use brain imaging data captured from human subjects during the VWM cognitive task to understand various memory mechanisms of the brain. ""","""None"""
35,"""How well do deep neural networks trained on object recognition characterize the mouse visual system?""","['mouse visual cortex', 'goal-driven modeling', 'object recognition', 'deep neural networks', 'hierarchical organization']","""Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream. However, we neither know whether such task-optimized networks enable equally good models of the rodent visual system, nor if a similar hierarchical correspondence exists. Here, we address these questions in the mouse visual system by extracting features at several layers of a convolutional neural network (CNN) trained on ImageNet to predict the responses of thousands of neurons in four visual areas (V1, LM, AL, RL) to natural images. We found that the CNN features outperform classical subunit energy models, but found no evidence for an order of the areas we recorded via a correspondence to the hierarchy of CNN layers. Moreover, the same CNN but with random weights provided an equivalently useful feature space for predicting neural responses. Our results suggest that object recognition as a high-level task does not provide more discriminative features to characterize the mouse visual system than a random network. Unlike in the primate, training on ethologically relevant visually guided behaviors -- beyond static object recognition -- may be needed to unveil the functional organization of the mouse visual cortex. ""","""None"""
36,"""Do deep neural networks possess concept space grid cells?""","['concept space', 'cognitive map', 'place cells', 'grid cells', 'memory retrieval']","""Place and grid-cells are known to aid navigation in animals and humans. Together with concept cells, they allow humans to form an internal representation of the external world, namely the concept space. We investigate the presence of such a space in deep neural networks by plotting the activation profile of its hidden layer neurons. Although place cell and concept-cell like properties are found, grid-cell like firing patterns are absent thereby indicating a lack of path integration or feature transformation functionality in trained networks. Overall, we present a plausible inadequacy in current deep learning practices that restrict deep networks from performing analogical reasoning and memory retrieval tasks.""","""None"""
37,"""Flexible degrees of connectivity under synaptic weight constraints""",[],"""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.""","""None"""
38,"""Unravelling the neural signatures of dream recall in EEG: a deep learning approach""","['CNN', 'EEG', 'sleep', 'dreamers', 'tSNE', 'guided-backpropagation']","""Dreams and our ability to recall them are among the most puzzling questions in sleep research. Specifically, putative differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood. In this study, we addressed this question as a classification problem where we applied deep convolutional networks (CNN) to sleep EEG recordings to predict whether subjects belonged to the high or low dream recall group (HDR and LDR resp.). Our model achieves significant accuracy levels across all the sleep stages, thereby indicating subtle signatures of dream recall in the sleep microstructure. We also visualized the feature space to inspect the subject-specificity of the learned features, thus ensuring that the network captured population level differences. Beyond being the first study to apply deep learning to sleep EEG in order to classify HDR and LDR, guided backpropagation allowed us to visualize the most discriminant features in each sleep stage. The significance of these findings and future directions are discussed.""","""None"""
39,"""Continual Learning via Neural Pruning""","['life-long learning', 'catastrophic forgetting']","""Inspired by the modularity and the life-cycle of biological neurons,we introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at lifelong learning in fixed capacity models based on the pruning of neurons of low activity. In this method, an L1 regulator is used to promote the presence of neurons of zero or low activity whose connections to previously active neurons is permanently severed at the end of training. Subsequent tasks are trained using these pruned neurons after reinitialization and cause zero deterioration to the performance of previous tasks. We show empirically that this biologically inspired method leads to state of the art results beating or matching current methods of higher computational complexity.""","""None"""
40,"""Reinforcement learning with a network of spiking agents""","['Reinforcement learning', 'multi-agent learning', 'spiking neurons']","""Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions (Schultz et al.). We build on this theory to propose a multi-agent learning framework with spiking neurons in the generalized linear model (GLM) formulation as agents, to solve reinforcement learning (RL) tasks. We show that a network of GLM spiking agents connected in a hierarchical fashion, where each spiking agent modulates its firing policy based on local information and a global prediction error, can learn complex action representations to solve RL tasks. We further show how leveraging principles of modularity and population coding inspired from the brain can help reduce variance in the learning updates making it a viable optimization technique.""","""None"""
41,"""Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders""","['neuroscience', 'reward processing', 'reinforcement learning', 'psychiatric disorders']","""Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For the AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems. ""","""None"""
42,"""Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning""","['neuroimaging', 'deep learning', 'transfer learning', 'audio', 'encoding models']","""The purpose of an encoding model is to predict brain activity given a stimulus. In this contribution, we attempt at estimating a whole brain encoding model of auditory perception in a naturalistic stimulation setting. We analyze data from an open dataset, in which 16 subjects watched a short movie while their brain activity was being measured using functional MRI. We extracted feature vectors aligned with the timing of the audio from the movie, at different layers of a Deep Neural Network pretrained on the classification of auditory scenes. fMRI data was parcellated using hierarchical clustering on 500 parcels, and encoding models were estimated using a fully connected neural network with one hidden layer, trained to predict the signals for each parcel from the DNN features. Individual encoding models were successfully trained and predicted brain activity on unseen data, in parcels located in the superior temporal lobe, as well as dorsolateral prefrontal regions, which are usually considered as areas involved in auditory and language processing. Taken together, this contribution extends previous attempts on estimating encoding models, by showing the ability to model brain activity using a generic DNN (ie not specifically trained for this purpose) to extract auditory features, suggesting a degree of similarity between internal DNN representations and brain activity in naturalistic settings. ""","""None"""
43,"""Learning to predict visual brain activity by predicting future sensory states""","['predictive coding', 'representational similarity analysis', 'visual brain', 'fmri']","""Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project (Cichy et al., 2019). In contrast to previous findings in the literature (Khaligh-Razavi & Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos without further fine-tuning may outperform supervised image classification baselines in terms of correlation to spatial (fMRI) and temporal (MEG) data.""","""None"""
44,"""Checking Functional Modularity in DNN By Biclustering Task-specific Hidden Neurons""","['DNN', 'modularity']","""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. ""","""None"""
45,"""The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging""","['connectomics', 'optimisation', 'state-space estimation', 'simulation', 'c. elegans']","""We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to ``complete the circle,'' where an anatomically grounded whole-connectome simulator is used to impute a time-varying ``brain'' state at single-cell fidelity from covariates that are measurable in practice. Using state of the art Bayesian machine learning methods to condition on readily obtainable data, our method paves the way for neuroscientists to recover interpretable connectome-wide state representations, automatically estimate physiologically relevant parameter values from data, and perform simulations investigating intelligent lifeforms in silico.""","""None"""
46,"""Pattern recognition of labeled concepts by a single spiking neuron model.""","['spiking neural networks', 'neual plasticity', 'pattern recognition', 'single neuron', 'classification']","""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.""","""None"""
47,"""Cellular neuromodulation in artificial networks""","['neuromodulation', 'deep learning', 'reinforcement learning']","""Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such adaptation property strongly relies on cellular neuromodulation, the biological mechanism that dynamically controls neuron intrinsic properties and response to external stimuli in a context dependent manner. In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours. The network adaptation capabilities are tested on navigation benchmarks in a meta-learning context and compared with state-of-the-art approaches. Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.""","""None"""
48,"""Are skip connections necessary for biologically plausible learning rules?""","['Credit Assignment', 'Biologically plausible learning rule', 'skip connections']","""Recognizing that backpropagation has been the workhorse of deep learning, it is time to explore other alternative learning methods. Several biologically motivated learning rules have been introduced, such as random feedback alignment and difference target propagation. However, none of these methods have produced competitive performance against backpropagation. In this paper, we show that biologically motivated learning rules with skip connections between intermediate layers can perform as well as backpropagation on the MNIST dataset and is robust to various sets of hyper-parameters.""","""None"""
49,"""EEG based Emotion Recognition of Image Stimuli ""","['Electroencephalography (EEG)', 'Brain computer interface (BCI)', 'machine learning', 'emotion recognition', 'image stimuli', 'neuromarketingg']","""Emotion is playing a great role in our daily lives. The necessity and importance of an automatic Emotion recognition system is getting increased. Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc. However, these features can potentially be changed to fake features. So to detect hidden and real features that is not controlled by the person are data measured from brain signals. There are various ways of measuring brain waves: EEG, MEG, FMRI, etc. On the bases of cost effectiveness and performance trade-offs, EEG is chosen for emotion recognition in this work. The main aim of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli. The approaches used were the selected visual stimuli were presented to 11 healthy target subjects and EEG signal were recorded in controlled situation to minimize artefacts (muscle or/and eye movements). The signals were filtered and type of frequency band was computed and detected. The proposed method predicts an emotion type (positive/negative) in response to the presented stimuli. Finally, the performance of the proposed approach was tested. The average accuracy of machine learning algorithms (i.e. J48, Bayes Net, Adaboost and Random Forest) are 78.86, 74.76, 77.82 and 82.46 respectively. In this study, we also applied EEG applications in the context of neuro-marketing. The results empirically demonstrated detection of the favourite colour preference of customers in response to the logo colour of an organization or Service. ""","""None"""
50,"""Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory""","['Working memory', 'recurrent neural networks', 'neuronal timescales']","""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.""","""None"""
51,"""Neocortical plasticity: an unsupervised cake but no free lunch""","['neocortex', 'local learning', 'dendrites', 'adversarial examples', 'generalisation']","""The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, important inspiration for the development of artificial intelligence systems has come from the study of natural systems of intelligence, the mammalian neocortex in particular. On the other, important inspiration for models and theories of the brain have emerged from artificial intelligence research. A central question at the intersection of these two areas is concerned with the processes by which neocortex learns, and the extent to which they are analogous to the back-propagation training algorithm of deep networks. Matching the data efficiency, transfer and generalisation properties of neocortical learning remains an area of active research in the field of deep learning. Recent advances in our understanding of neuronal, synaptic and dendritic physiology of the neocortex suggest new approaches for unsupervised representation learning, perhaps through a new class of objective functions, which could act alongside or in lieu of back-propagation. Such local learning rules have implicit rather than explicit objectives with respect to the training data, facilitating domain adaptation and generalisation. Incorporating them into deep networks for representation learning could better leverage unlabelled datasets to offer significant improvements in data efficiency of downstream supervised readout learning, and reduce susceptibility to adversarial perturbations, at the cost of a more restricted domain of applicability. ""","""None"""
52,"""Evolving the Olfactory System""","['evolution', 'perception', 'olfaction', 'connectivity']","""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.""","""None"""
53,"""Does the neuronal noise in cortex help generalization?""","['noise', 'trial-to-trial variability', 'subspace', 'generalization', 'dropout']","""Neural activity is highly variable in response to repeated stimuli. We used an open dataset, the Allen Brain Observatory, to quantify the distribution of responses to repeated natural movie presentations. A large fraction of responses are best fit by log-normal distributions or Gaussian mixtures with two components. These distributions are similar to those from units in deep neural networks with dropout. Using a separate set of electrophysiological recordings, we constructed a population coupling model as a control for state-dependent activity fluctuations and found that the model residuals also show non-Gaussian distributions. We then analyzed responses across trials from multiple sections of different movie clips and observed that the noise in cortex aligns better with in-clip versus out-of-clip stimulus variations. We argue that noise is useful for generalization when it moves along representations of different exemplars in-class, similar to the structure of cortical noise.""","""None"""
54,"""Insect Cyborgs: Bio-mimetic Feature Generators Improve ML Accuracy on Limited Data""","['feature selection', 'bio-mimesis', 'neural networks', 'insect olfaction', 'sparsity']","""We seek to auto-generate stronger input features for ML methods faced with limited training data. Biological neural nets (BNNs) excel at fast learning, implying that they extract highly informative features. In particular, the insect olfactory network learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; randomized, sparse connectivity into a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights. In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator. Attached as a front-end pre-processor, MothNet's readout neurons provide new features, derived from the original features, for use by standard ML classifiers. These ``insect cyborgs'' (part BNN and part ML method) have significantly better performance than baseline ML methods alone on vectorized MNIST and Omniglot data sets, reducing test set error averages 20% to 55%. The MothNet feature generator also substantially out-performs other feature generating methods including PCA, PLS, and NNs. These results highlight the potential value of BNN-inspired feature generators in the ML context.""","""None"""
55,"""Additive function approximation in the brain""","['sparse networks', 'random features', 'associative learning']","""Many biological learning systems such as the mushroom body, hippocampus, and cerebellum are built from sparsely connected networks of neurons. For a new understanding of such networks, we study the function spaces induced by sparse random features and characterize what functions may and may not be learned. A network with d inputs per neuron is found to be equivalent to an additive model of order d, whereas with a degree distribution the network combines additive terms of different orders. We identify three specific advantages of sparsity: additive function approximation is a powerful inductive bias that limits the curse of dimensionality, sparse networks are stable to outlier noise in the inputs, and sparse random features are scalable. Thus, even simple brain architectures can be powerful function approximators. Finally, we hope that this work helps popularize kernel theories of networks among computational neuroscientists.""","""None"""
56,"""The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units""","['deep networks', 'invariance', 'neuroscience']","""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.""","""None"""
57,"""Emergent Structures and Lifetime Structure Evolution in Artificial Neural Networks""","['emergent networks', 'structure evolution', 'architecture search']","""Motivated by the flexibility of biological neural networks whose connectivity structure changes significantly during their lifetime,we introduce the Unrestricted Recursive Network (URN) and demonstrate that it can exhibit similar flexibility during training via gradient descent. We show empirically that many of the different neural network structures commonly used in practice today (including fully connected, locally connected and residual networks of differ-ent depths and widths) can emerge dynamically from the same URN.These different structures can be derived using gradient descent on a single general loss function where the structure of the data and the relative strengths of various regulator terms determine the structure of the emergent network. We show that this loss function and the regulators arise naturally when considering the symmetries of the network as well as the geometric properties of the input data.""","""None"""
58,"""Convolutionary, Evolutionary, Revolutionary: Whats next for Bodies, Brains and AI?""","['spiking neural networks', 'self organization', 'feedback', 'oscillations', 'predictive models', 'dynamic correlations', 'stochastic sampling', 'RL', 'embodiment']","""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. ""","""None"""
59,"""Disentangling the roles of dimensionality and cell classes in neural computations""","['RNN', 'reverse-engineering', 'mean-field theory', 'dimensionality', 'cell classes']","""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.""","""None"""
60,"""Revisit Recurrent Attention Model from an Active Sampling Perspective""",[],"""We revisit the Recurrent Attention Model (RAM, Mnih et al. (2014)), a recurrent neural network for visual attention, from an active information sampling perspective. We borrow ideas from neuroscience research on the role of active information sampling in the context of visual attention and gaze (Gottlieb, 2018), where the author suggested three types of motives for active information sampling strategies. We find the original RAM model only implements one of them. We identify three key weakness of the original RAM and provide a simple solution by adding two extra terms on the objective function. The modified RAM 1) achieves faster convergence, 2) allows dynamic decision making per sample without loss of accuracy, and 3) generalizes much better on longer sequence of glimpses which is not trained for, compared with the original RAM. ""","""None"""
61,"""Contextual and neural representations of sequentially complex animal vocalizations""","['sequence learning', 'birdsong', 'auditory neuroscience', 'generative models', 'context']","""Holistically exploring the perceptual and neural representations underlying animal communication has traditionally been very difficult because of the complexity of the underlying signal. We present here a novel set of techniques to project entire communicative repertoires into low dimensional spaces that can be systematically sampled from, exploring the relationship between perceptual representations, neural representations, and the latent representational spaces learned by machine learning algorithms. We showcase this method in one ongoing experiment studying sequential and temporal maintenance of context in songbird neural and perceptual representations of syllables. We further discuss how studying the neural mechanisms underlying the maintenance of the long-range information content present in birdsong can inform and be informed by machine sequence modeling.""","""None"""
62,"""Towards learning principles of the brain and spiking neural networks""","['spiking neural networks', 'Spike-time dependent plasticity', 'network simulations']","""The brain, the only system with general intelligence, is a network of spiking neurons (i.e., spiking neural networks, SNNs), and several neuromorphic chips have been developed to implement SNNs to build power-efficient learning systems. Naturally, both neuroscience and machine learning (ML) scientists are attracted to SNNs operating principles. Based on biologically plausible network simulations, we propose that spatially nonspecific top-down inputs, projected into lower-order areas from high-order areas, can enhance the brains learning process. Our study raises the possibility that training SNNs need novel mechanisms that do not exist in conventional artificial neural networks (ANNs) including deep neural networks (DNNs). ""","""None"""