Criticality Analysis: Bio-inspired Nonlinear Data Representation
- URL: http://arxiv.org/abs/2305.14361v1
- Date: Thu, 11 May 2023 19:02:09 GMT
- Title: Criticality Analysis: Bio-inspired Nonlinear Data Representation
- Authors: Tjeerd V. olde Scheper
- Abstract summary: Criticality Analysis (CA) is a bio-inspired method of information representation within a controlled self-organised critical system.
The input can be reduced dimensionally to a projection output that retains the features of the overall data, yet has much simpler dynamic response.
The CA method allows for a biologically relevant encoding mechanism of arbitrary input to biosystems, creating a suitable model for information processing in varying complexity of organisms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The representation of arbitrary data in a biological system is one of the
most elusive elements of biological information processing. The often
logarithmic nature of information in amplitude and frequency presented to
biosystems prevents simple encapsulation of the information contained in the
input. Criticality Analysis (CA) is a bio-inspired method of information
representation within a controlled self-organised critical system that allows
scale-free representation. This is based on the concept of a reservoir of
dynamic behaviour in which self-similar data will create dynamic nonlinear
representations. This unique projection of data preserves the similarity of
data within a multidimensional neighbourhood. The input can be reduced
dimensionally to a projection output that retains the features of the overall
data, yet has much simpler dynamic response. The method depends only on the
rate control of chaos applied to the underlying controlled models, that allows
the encoding of arbitrary data, and promises optimal encoding of data given
biological relevant networks of oscillators. The CA method allows for a
biologically relevant encoding mechanism of arbitrary input to biosystems,
creating a suitable model for information processing in varying complexity of
organisms and scale-free data representation for machine learning.
Related papers
- Heterogeneous quantization regularizes spiking neural network activity [0.0]
We present a data-blind neuromorphic signal conditioning strategy whereby analog data are normalized and quantized into spike phase representations.
We extend this mechanism by adding a data-aware calibration step whereby the range and density of the quantization weights adapt to accumulated input statistics.
arXiv Detail & Related papers (2024-09-27T02:25:44Z) - Synthetic data generation for system identification: leveraging
knowledge transfer from similar systems [0.3749861135832073]
This paper introduces a novel approach for the generation of synthetic data, aimed at enhancing model generalization and robustness in scenarios characterized by data scarcity.
Synthetic data is generated through a pre-trained meta-model that describes a broad class of systems to which the system of interest is assumed to belong.
The efficacy of the approach is shown through a numerical example that highlights the advantages of integrating synthetic data into the system identification process.
arXiv Detail & Related papers (2024-03-08T09:09:15Z) - Hyperdimensional computing: a fast, robust and interpretable paradigm
for biological data [9.094234519404907]
New algorithms for processing diverse biological data sources have revolutionized bioinformatics.
Deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses.
Hyperdimensional computing has emerged as an intriguing alternative.
arXiv Detail & Related papers (2024-02-27T15:09:20Z) - A topological classifier to characterize brain states: When shape
matters more than variance [0.0]
topological data analysis (TDA) is devoted to study the shape of data clouds by means of persistence descriptors.
We introduce a novel TDA-based classifier that works on the principle of assessing quantifiable changes on topological metrics caused by the addition of new input to a subset of data.
arXiv Detail & Related papers (2023-03-07T20:45:15Z) - Capturing Actionable Dynamics with Structured Latent Ordinary
Differential Equations [68.62843292346813]
We propose a structured latent ODE model that captures system input variations within its latent representation.
Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
arXiv Detail & Related papers (2022-02-25T20:00:56Z) - Discriminative Singular Spectrum Classifier with Applications on
Bioacoustic Signal Recognition [67.4171845020675]
We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently.
Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces.
The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
arXiv Detail & Related papers (2021-03-18T11:01:21Z) - Using Data Assimilation to Train a Hybrid Forecast System that Combines
Machine-Learning and Knowledge-Based Components [52.77024349608834]
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements.
We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
arXiv Detail & Related papers (2021-02-15T19:56:48Z) - Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature
Selection [138.97647716793333]
We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization.
We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
arXiv Detail & Related papers (2020-12-29T04:08:38Z) - Compact representation of temporal processes in echosounder time series
via matrix decomposition [0.7614628596146599]
We develop a methodology that builds compact representation of long-term echosounder time series using intrinsic features in the data.
This work forms the basis for constructing robust time series analytics for large-scale, acoustics-based biological observation in the ocean.
arXiv Detail & Related papers (2020-07-06T17:33:42Z) - A Trainable Optimal Transport Embedding for Feature Aggregation and its
Relationship to Attention [96.77554122595578]
We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference.
Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
arXiv Detail & Related papers (2020-06-22T08:35:58Z) - Active Learning for Nonlinear System Identification with Guarantees [102.43355665393067]
We study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs.
We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data.
We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.
arXiv Detail & Related papers (2020-06-18T04:54:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.