Deep learning, computational neuroscience, and cognitive science have overlapping goals related to understanding intelligence such that perception and behaviour can be simulated in computational systems. In neuroimaging, machine learning methods have been used to test computational models of sensory information process…
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48 results for “sensory system”
Agents acting in the natural world aim at selecting appropriate actions based on noisy and partial sensory observations. Many behaviors leading to decision mak- ing and action selection in a closed loop setting are naturally phrased within a control theoretic framework. Within the framework of optimal Control Theory, o…
New method decomposes sensory information from neurons into specific stimuli and features.
problem Understanding how much and what specific information neurons encode.
method Introduced axioms for meaningful stimulus-wise decomposition and derived a tractable solution using diffusion models.
result Can efficiently estimate contributions of specific stimuli and features to encoded information.
Linear Readout of Object Manifoldscond-mat.dis-nn
Objects are represented in sensory systems by continuous manifolds due to sensitivity of neuronal responses to changes in physical features such as location, orientation, and intensity. What makes certain sensory representations better suited for invariant decoding of objects by downstream networks? We present a theory…
Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction a…
Spectral method speeds fitting of binary time series models.
problem Modeling binary time series data with latent linear dynamical systems.
method Spectral learning of probit-Bernoulli latent linear dynamical systems.
result Spectral method provides robust, fixed-cost estimator.
The paper develops models to understand sensory coding and cortical topography.
problem Understanding how visual cortical areas' receptive fields and topographic maps relate to environmental statistical structure.
method Energy-based models applied to probability density estimation, constrained by biological constraints.
result The models qualitatively reproduce receptive field and map properties found in vivo.
Perception Learning: A Formal Separation of Sensory Representation Learning from Decision Learningcs.LG
PeL separates sensory interface optimization from decision learning.
problem Optimizing sensory interfaces without task-specific information.
method Formal separation of perception and decision learning, using metrics for stability, informativeness, and geometry.
result Updates preserving invariants are orthogonal to decision gradients.
We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function. In particular, centroidal Voronoi partitions have to be computed. The difficulty of the task is that the sensory function is unknown and has to be reconstructed on line from noisy me…
Improved robustness in multi-modal sensor fusion with deep learning.
problem Inconsistency in fusion weights leading to poor performance under sensor failures.
method Proposes deep multi-modal sensor fusion architectures with fusion weight regularization and target learning.
result Proposed architectures outperform existing deep learning methods under sensor failures.
A deep learning framework assesses physical rehabilitation exercises.
problem Lack of versatile, robust, and practical assessment methods for rehabilitation exercises.
method Deep learning framework with metrics, scoring functions, and neural networks.
result First implementation of deep neural networks for rehabilitation performance assessment.
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algo…
No real-world reward function is perfect. Sensory errors and software bugs may result in RL agents observing higher (or lower) rewards than they should. For example, a reinforcement learning agent may prefer states where a sensory error gives it the maximum reward, but where the true reward is actually small. We formal…
A novel circuit motif uses sister cells for inference with correlated priors.
problem Structured priors in neural systems pose architectural challenges.
method Proposes a novel circuit motif using sister cells to implement correlated priors without direct interactions.
result Demonstrates the efficacy of correlated priors for inference in noisy environments.
This thesis optimizes neuromorphic systems by slowing down their dynamics, improving performance.
problem Timescale mismatch between analog neuromorphic circuits and real-time sensory inputs.
method Proposes and tests solutions to slow down the dynamics of spiking neural networks.
result Spiking neural networks on analog neuromorphic systems can achieve significant performance boosts.
We discuss memory models which are based on tensor decompositions using latent representations of entities and events. We show how episodic memory and semantic memory can be realized and discuss how new memory traces can be generated from sensory input: Existing memories are the basis for perception and new memories ar…
Study how actions affect perception in embodied agents using group theory.
problem Understanding how actions influence perception in autonomous agents.
method Mathematical formalism of group theory applied to sensory commutativity of action sequences.
result Introduced Sensory Commutativity Probability (SCP) to measure action effects on perception.
Algorithm balances learning and coverage for multi-robots over unknown fields.
problem Balancing learning and coverage for multi-robots over unknown, nonuniform sensory fields.
method DSLC algorithm that schedules learning and coverage epochs, using Gaussian Process modeling and coverage regret analysis.
result Upper bound on expected cumulative coverage regret provided for DSLC.
The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process p…
A model predicts visual motion by learning from natural videos.
problem Temporal prediction accuracy in visual perception.
method Self-supervised representation learning using Fourier shift theorem.
result Achieves better prediction performance than traditional methods.
This research improves multimodal systems by adding a second objective and regularisation methods.
problem Improving performance of multimodal systems with multiple objectives and regularisation.
method Introduces a second objective over multimodal fusion using variational inference and regularisation methods.
result Demonstrates potential for multiple objectives and probabilistic methods to lower variance and improve generalisation.
Large language models predict human sensory judgments across multiple modalities.
problem Determining the extent of perceptual information in language.
method State-of-the-art large language models were used to predict sensory judgments across six psychophysical datasets.
result Large language models can predict human sensory judgments across multiple modalities with significant correlation to human data.
This research converts visual information into audio for users to perceive.
problem Brevity in conveying visual information through spoken language.
method Pretrained image embedding network, GAN for metric space mapping, human subject testing.
result Users can accurately classify audio sonifications of faces.
We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions. Unlike dynamics models that operate over individual discrete timesteps, we learn the distribution over future state trajectories conditioned on past state, past acti…
ForGAN uses GANs for probabilistic forecasting of sensory data.
problem Challenges in traditional forecasting methods and difficulties in probabilistic methods.
method ForGAN combines GANs with conditional generative adversarial networks to learn data distributions and generate probabilistic forecasts.
result ForGAN outperforms traditional regression methods in probabilistic forecasting of sensory data.
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or r…
Hybrid model predicts flow and pressure in water systems.
problem Predicting flow and pressure in water distribution systems with complex spatial-temporal correlations.
method Hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN).
result Our model outperformed 9 baseline models in flow and pressure series prediction.
NeuralPrefix fills in missing sensor data without additional training.
problem Data intermittency in real-world sensing.
method NeuralPrefix is a task-agnostic, zero-shot imputation framework.
result NeuralPrefix accurately recovers missing samples and generalizes to unseen datasets.
Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute t…
Deep active inference learns policies from sensory inputs.
problem Learning policies in partially observable domains.
method Optimizes expected free energy with a variational autoencoder.
result Comparable or better performance than deep Q-learning.
RNNs are suboptimal at compressing past sensory inputs for future prediction.
problem RNNs do not optimally compress past sensory inputs for future prediction.
method Investigated RNNs trained with maximum likelihood and found they extract unnecessary information. Injected noise into hidden states to improve performance.
result Injecting noise into RNN hidden states improves predictive information, sample quality, likelihood, and classification performance.
Model learns and plans in real-time under constraints for robotic systems.
problem Challenges in applying reinforcement learning to robotic systems, especially handling continuous state and action spaces, time/resource budget, and hard constraints.
method Combines Gaussian Process regression and Receding Horizon Control. Uses sparse spectrum Gaussian Processes for incremental model updates from sensory data.
result Demonstrates benefits of online learning on autonomous racing tasks and reusability of learned dynamics.
Variational mean-field theory for training restricted Boltzmann machines with binary synapsesstat.ML
Unified theory for training neural networks with binary synapses.
problem Discrete nature of synapses and complex interactions in neural networks.
method Variational mean-field theory decomposing learning into maximization and expectation steps.
result Unified framework for unsupervised learning in neural networks.
Graph Convolutional Networks improve prosthetic sensation interpretation.
problem Improving neuroprosthetic performance and sensory information stability.
method Applied Graph Convolutional Networks (GCNs) to interpret neuronal spiking activity.
result GCN model achieved 73.5% performance on ordinal regression task.
We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge. The learned features are then fed to a Reinforcement Learning algorithm to learn a policy. We propose to …
The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring …
New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.
problem Tackles inverse optimal control for non-linear partially observable systems.
method Probabilistic approach using maximum causal entropy formulations and local linearization.
result Disentangles perceptual factors and behavioral costs in sequential decision-making.
Constellation learns group-level visual relationships for abstract reasoning.
problem Learning configurational properties of entire groups of objects.
method Introduces Constellation, a network that learns relational abstractions over static visual scenes.
result Offers a basis for abstract relational reasoning and sensory imagination.
An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially …
This work designs an active world model learning system with progress-based curiosity.
problem Learning compact world models from visual exploration.
method Constructs a curious agent in a 3D environment, using -Progress as a curiosity signal.
result The -Progress-driven controller outperforms state-of-the-art exploration strategies.
New method identifies flawed internal models of the world in animals.
problem How animals make decisions with partial sensory information.
method Generalizes Inverse Rational Control to continuous nonlinear dynamics and noise.
result Identifies the best internal model explaining an agent's actions.
Decussation improves robustness of 3D neural networks.
problem Understanding the functional advantage of decussation in neural wiring.
method Examined constraints of topology on 3D wiring.
result Decussated arrangements are more robust against wiring errors than same-sided schemes.
This review explores ML and DL techniques for detecting distracted driving across various modalities.
problem Improving detection of complex distraction patterns, especially cognitive distractions.
method Categorizes and evaluates studies based on modality, data accessibility, and methodology.
result Multimodal systems outperform single-modal systems in detecting complex distraction patterns.
Paper proposes CARL for better control in RL from sensory data.
problem Efficient control of complex systems from high-dimensional sensory inputs.
method Control-Aware Representation Learning (CARL) for model-based RL.
result CARL improves control performance in benchmark tasks.
S-TRIGGER learns state representations for continual learning.
problem Efficiently compress and maintain past knowledge in changing environments.
method Generative Replay with self-triggered environment change detection.
result S-TRIGGER enables fast and high-performing Reinforcement Learning without catastrophic forgetting.
A deep learning framework discovers causal relationships from incomplete data.
problem Discovering causal knowledge from incomplete observational data.
method Imputated Causal Learning (ICL) framework for iterative missing data imputation and causal structure discovery.
result ICL outperforms state-of-the-art methods in various missing data scenarios.
Artificial perception is traditionally handled by hand-designing task specific algorithms. However, a truly autonomous robot should develop perceptive abilities on its own, by interacting with its environment, and adapting to new situations. The sensorimotor contingencies theory proposes to ground the development of th…
Tensor models decode human perception and memory using SPO triples.
problem Understanding implicit and explicit perception and memory in the brain.
method Tensor models with SPO triples, dual representations, and four layers.
result Semantic memory is crucial for explicit perception and declarative memories.