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Found 30 papers, 6 papers with code
Analytical Characterization of Epileptic Dynamics in a Bistable System
In this paper, we employ a bistable model, where a stable equilibrium and a stable limit cycle coexist, to describe epileptic dynamics.
Effective Learning with Node Perturbation in Deep Neural Networks
Backpropagation (BP) is the dominant and most successful method for training parameters of deep neural network models.
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Predictive processing is a popular theoretical framework which maintains that the human brain is actively seeking to minimize surprise.
Learning Policies for Continuous Control via Transition Models
It is doubtful that animals have perfect inverse models of their limbs (e. g., what muscle contraction must be applied to every joint to reach a particular location in space).
How does artificial intelligence contribute to iEEG research?
Applying advanced AI models to these data carries the potential to further our understanding of many fundamental questions in neuroscience.
Constrained Parameter Inference as a Principle for Learning
Backpropagation of error (BP) is an example of such an approach and has proven to be a highly successful application of stochastic gradient descent to deep neural networks.
Hermitry Ratio: Evaluating the validity of perturbation methods for explainable deep learning
Perturbation methods are model-agnostic methods used to generate heatmaps to explain black-box algorithms such as deep neural networks.
Explainable Artificial Intelligence (XAI)
Image Classification
P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Predictive processing is a popular theoretical framework which maintains that the human brain is actively seeking to minimize surprise.
Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception
Unlike robots, humans learn, adapt and perceive their bodies by interacting with the world.
Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks
Many of the recent advances in the field of artificial intelligence have been fueled by the highly successful backpropagation of error (BP) algorithm, which efficiently solves the credit assignment problem in artificial neural networks.
Automatic variational inference with cascading flows
We evaluate the performance of the new variational programs in a series of structured inference problems.
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs
In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter requiring less parameters.
Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space
We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings.
A deep active inference model of the rubber-hand illusion
Understanding how perception and action deal with sensorimotor conflicts, such as the rubber-hand illusion (RHI), is essential to understand how the body adapts to uncertain situations.
Explainable Deep Learning: A Field Guide for the Uninitiated
The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) finally elaborates on user-oriented explanation designing and potential future directions on explainable deep learning.
Virtual staining for mitosis detection in Breast Histopathology
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa.
Automatic structured variational inference
However, the performance of the variational approach depends on the choice of an appropriate variational family.
The Indian Chefs Process
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes.
End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
We present a pixel-based deep active inference algorithm (PixelAI) inspired by human body perception and action.
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
A possible way of dealing with this problem is to use an ensemble of GANs, where (ideally) each network models a single mode.
The functional role of cue-driven feature-based feedback in object recognition
Visual object recognition is not a trivial task, especially when the objects are degraded or surrounded by clutter or presented briefly.
Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference
The solution of the resulting optimal transport problem provides both a particle approximation and a set of optimal transportation densities that map each particle to a segment of the posterior distribution.
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified.
Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability
This will be an upper bound that depends on the structure of the data.
Deep adversarial neural decoding
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning.
GP CaKe: Effective brain connectivity with causal kernels
Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity.
Estimating Nonlinear Dynamics with the ConvNet Smoother
Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering.
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus.
Deep disentangled representations for volumetric reconstruction
We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction.
Predicting and visualizing psychological attributions with a deep neural network
Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions.
