pwc-archive/papers-with-abstracts huggingface dataset:
Cellular neuromodulation in artificial networks
Authors: Vecoven Nicolas, Ernst Damien, Wehenkel Antoine, Drion Guillaume
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Meta-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.
What’s in a functional brain parcellation?
Authors: Gaël Varoquaux, Kamalakar Dadi, Arthur Mensch
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

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.
Translating neural signals to text using a Brain-Computer Interface
Authors: Janaki Sheth, Ariel Tankus, Michelle Tran, Nader Pouratian, Itzhak Fried, William Speier
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Brain Computer Interface

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.
Learning to predict visual brain activity by predicting future sensory states
Authors: Marcio Fonseca
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: image-classification, Image Classification

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.
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders
Authors: Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Decision Making, Q-Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning (RL)

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.
Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning
Authors: Nicolas Farrugia, Victor Nepveu, Deycy Camila Arias Villamil
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Transfer Learning

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.
Insect Cyborgs: Bio-mimetic Feature Generators Improve ML Accuracy on Limited Data
Authors: Charles B. Delahunt, J. Nathan Kutz
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

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.
Revisit Recurrent Attention Model from an Active Sampling Perspective
Authors: Jialin Lu
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Decision Making

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.
Contextual and neural representations of sequentially complex animal vocalizations
Authors: Anonymous
Date: 2019-09-11 00:00:00

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.
Learning Non-Parametric Invariances from Data with Permanent Random Connectomes
Authors: Anonymous
Date: 2019-09-11 00:00:00

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.
Towards learning principles of the brain and spiking neural networks
Authors: Anonymous
Date: 2019-09-11 00:00:00

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 brain’s 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).
Efficient rescue of damaged neural networks
Authors: Anonymous
Date: 2019-09-11 00:00:00

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.
Automated Animal Training and Iterative Inference of Latent Learning Policy
Authors: Anonymous
Date: 2019-09-11 00:00:00
Tasks: Decision Making, Pose Estimation

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 animal’s 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 animal’s 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.
Foveated Downsampling Techniques
Authors: Anonymous
Date: 2019-09-11 00:00:00
Tasks: Foveation, Object Recognition

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.
Do deep neural networks possess concept space grid cells?
Authors: Anonymous
Date: 2019-09-11 00:00:00
Tasks: 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.
Differentiating Granger Causal Influence and Stimulus-Related Information Flow
Authors: Anonymous
Date: 2019-09-11 00:00:00
Tasks: Functional 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.
Unravelling the neural signatures of dream recall in EEG: a deep learning approach
Authors: Anonymous
Date: 2019-09-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

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.
Tracking momentary attention fluctuations with an EEG-based cognitive brain-machine interface
Authors: Anonymous
Date: 2019-09-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), SSVEP

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 attention’s effects on perceptual accuracy and and neural gain in EEG-SSVEP power, in the human brain.
Causal Inductive Synthesis Corpus
Authors: Zenna Tavares, Ria Das, Elizabeth Weeks, Kate Lin, Joshua B. Tenenbaum, Armando Solar-Lezama
Date: 2020-10-13 00:00:00
Proceeding: NeurIPS Workshop CAP 2020 12
Tasks: Model Discovery

We introduce the Causal Inductive Synthesis Corpus (CISC) -- a manually constructed collection of interactive domains.
CISC domains abstract core causal concepts present in real world mechanisms and environments.  We formulate two synthesis challenges of causal model discovery: the passive discovery of a model of a CISC domain from observed data, and active discovery while interacting with the domain.   CISC problems are expressed in Autumn, a Turing-complete programming language for specifying causal probabilistic models.  Autumn allows succinct expression for models that vary dynamically through time, respond to external input, have internal state and memory, exhibit probabilistic non-determinism, and have complex causal dependencies between variables.
SampleFix: Learning to Correct Programs by Efficient Sampling of Diverse Fixes
Authors: Hossein Hajipour, Apratim Bhattacharyya, Mario Fritz
Date: 2020-10-13 00:00:00
Proceeding: NeurIPS Workshop CAP 2020 12

Automatic program correction holds the potential of dramatically improving the productivity of programmers. Recent advances in machine learning and NLP have rekindled the hope to eventually fully automate the process of repairing programs. A key challenge is ambiguity, as multiple codes -- or fixes -- can implement the same functionality, and there is uncertainty on the intention of the programmer. As a consequence, datasets by nature fail to capture the full variance introduced by such ambiguities. Therefore, we propose a deep generative model to automatically correct programming errors by learning a distribution over potential fixes. Our model is formulated as a deep conditional variational autoencoder that can efficiently sample diverse fixes for a given erroneous program. In order to account for inherent ambiguity and lack of representative datasets, we propose a novel regularizer to encourage the model to generate diverse fixes. Our evaluations on common programming errors show strong improvements over the state-of-the-art approaches.