4,689 research outputs found
PredNet and Predictive Coding: A Critical Review
PredNet, a deep predictive coding network developed by Lotter et al.,
combines a biologically inspired architecture based on the propagation of
prediction error with self-supervised representation learning in video. While
the architecture has drawn a lot of attention and various extensions of the
model exist, there is a lack of a critical analysis. We fill in the gap by
evaluating PredNet both as an implementation of the predictive coding theory
and as a self-supervised video prediction model using a challenging video
action classification dataset. We design an extended model to test if
conditioning future frame predictions on the action class of the video improves
the model performance. We show that PredNet does not yet completely follow the
principles of predictive coding. The proposed top-down conditioning leads to a
performance gain on synthetic data, but does not scale up to the more complex
real-world action classification dataset. Our analysis is aimed at guiding
future research on similar architectures based on the predictive coding theory
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Large-scale Foundation Models and Generative AI for BigData Neuroscience
Recent advances in machine learning have made revolutionary breakthroughs in
computer games, image and natural language understanding, and scientific
discovery. Foundation models and large-scale language models (LLMs) have
recently achieved human-like intelligence thanks to BigData. With the help of
self-supervised learning (SSL) and transfer learning, these models may
potentially reshape the landscapes of neuroscience research and make a
significant impact on the future. Here we present a mini-review on recent
advances in foundation models and generative AI models as well as their
applications in neuroscience, including natural language and speech, semantic
memory, brain-machine interfaces (BMIs), and data augmentation. We argue that
this paradigm-shift framework will open new avenues for many neuroscience
research directions and discuss the accompanying challenges and opportunities
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)
How does the brain represent different modes of information? Can we design a
system that automatically understands what the user is thinking? Such questions
can be answered by studying brain recordings like functional magnetic resonance
imaging (fMRI). As a first step, the neuroscience community has contributed
several large cognitive neuroscience datasets related to passive
reading/listening/viewing of concept words, narratives, pictures and movies.
Encoding and decoding models using these datasets have also been proposed in
the past two decades. These models serve as additional tools for basic research
in cognitive science and neuroscience. Encoding models aim at generating fMRI
brain representations given a stimulus automatically. They have several
practical applications in evaluating and diagnosing neurological conditions and
thus also help design therapies for brain damage. Decoding models solve the
inverse problem of reconstructing the stimuli given the fMRI. They are useful
for designing brain-machine or brain-computer interfaces. Inspired by the
effectiveness of deep learning models for natural language processing, computer
vision, and speech, recently several neural encoding and decoding models have
been proposed. In this survey, we will first discuss popular representations of
language, vision and speech stimuli, and present a summary of neuroscience
datasets. Further, we will review popular deep learning based encoding and
decoding architectures and note their benefits and limitations. Finally, we
will conclude with a brief summary and discussion about future trends. Given
the large amount of recently published work in the `computational cognitive
neuroscience' community, we believe that this survey nicely organizes the
plethora of work and presents it as a coherent story.Comment: 16 pages, 10 figure
Model for Estimation of Bounds in Digital Coding of Seabed Images
This paper proposes the novel model for estimation of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density function of the quantizer input signal, SQNR gains are calculated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution
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