Optimal algorithm selects biological models without prior info.
problem Determining the correct biological model without prior knowledge.
method Systems biology models and likelihood-free inference.
result Improved model selection performance over conventional methods.
Spatially positioned neurons in neural networks mimic biological systems.
problem Creating neural networks that can perform multiple tasks efficiently.
method Added spatial positions and proximity penalties to artificial neurons.
result Neurons naturally cluster, each responsible for a specific task.
New methods improve uncertainty quantification in dynamic biological systems.
problem Uncertainty in dynamic biological models due to nonlinearity and parameter sensitivity.
method Conformal inference methods for non-asymptotic guarantees.
result Enhanced robustness and scalability for diverse biological data structures.
Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of …
New method identifies key genes affecting phenotypes in biological systems.
problem Identifying genes that drive specific phenotypes in complex biological systems.
method Data-driven observability decomposition using Koopman operators.
result Koopman operator representation identifies genes that drive phenotypes.
Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research. In the area of deep reinforcement learning, deep learning functional architectures are combined with incremental learning schemes for sequential …
Proposes a novel network-based neighborhood regression for biological systems.
problem Lack of comprehensive analysis on biological modules using both global and local network data.
method Develops a community-wise least square optimization approach to analyze gene modules and their regulatory strength.
result Achieves exact minimax optimality and linear consistency in identifying gene module associations.
New learning algorithm mimics biological neural networks.
problem Biologically implausible backpropagation for directed neural networks.
method Introduces new neuronal dynamics and learning rule for arbitrary architectures, sparsity-inducing pruning method, and dynamical-systems characterization.
result Prunes irrelevant connections and improves learning efficiency.
Optimizes biomanufacturing processes with a new digital twin calibration method.
problem Lack of interpretability and sample efficiency in traditional DoE methods.
method Developed a computational approach to calibrate Bio-SoS digital twin model.
result Guides sample-efficient and interpretable DoEs by quantifying sub-model parameter estimation errors.
DBNs improve accuracy of biological ODE models with missing data.
problem Uncertainty in biological ODE models with missing data.
method Converted ODE models to DBNs and used Particle Filtering for parameter estimation.
result DBNs can accurately infer model variables with missing data.
In systems biology, it is common to measure biochemical entities at different levels of the same biological system. One of the central problems for the data fusion of such data sets is the heterogeneity of the data. This thesis discusses two types of heterogeneity. The first one is the type of data, such as metabolomic…
Model captures system input variations in latent space for actionable dynamics.
problem Learning dynamical systems from data without prescribing a mathematical model.
method Structured latent ODE model with stochastic factors of variation for each input.
result Improves generation of time-series data and inference of system inputs over baselines.
Deep learning applied to biological data mining.
problem Mining complex biological data from diverse sources.
method Artificial neural networks, deep learning architectures.
result Deep learning techniques improve pattern recognition in biological data.
SENA-discrepancy-VAE interprets latent causal factors in biological pathways.
problem Interpreting latent causal factors in biological pathways.
method SENA-discrepancy-VAE, a model based on discrepancy-VAE, that produces interpretable latent causal factors.
result Sena-discrepancy-VAE achieves comparable predictive performance with non-interpretable counterparts while providing biologically meaningful causal factors.
There is an abundance of complex dynamic systems that are critical to our daily lives and our society but that are hardly understood, and even with today's possibilities to sense and collect large amounts of experimental data, they are so complex and continuously evolving that it is unlikely that their dynamics will ev…
Fault-tolerant neural networks inspired by biological error correction codes.
problem Achieving reliable computation with unreliable neurons.
method Using biological error correction codes from grid cells in the mammalian cortex to develop a fault-tolerant neural network.
result Noisy biological neurons operate below a fault-tolerance threshold, suggesting a mechanism for reliable computation in the brain.
Hexnet framework enhances image processing with hexagonal structures.
problem Improve image processing systems with biological-inspired hexagonal structures.
method Develops a hexagonal deep learning framework (Hexnet) for image processing.
result Hexnet surpasses current hexagonal image processing systems and artificial neural networks.
New insights link memory loss to system stability in dynamical systems.
problem Understanding the relationship between dynamics and computation, especially stability and memory loss.
method Analyzing driven dynamical systems responding to temporal inputs.
result Memory loss in driven systems leads to consistent responses to similar inputs and affects stability.
Paper explores why compositionality is not the right approach for emergent communication.
problem Lack of interaction between biological and computational models of emergent communication.
method Exploring the claim that compositionality is the wrong target for explaining natural language emergence.
result Suggests reflexivity as a better target for explaining natural language emergence.
New metric compares noisy neural trajectories using optimal transport.
problem Existing metrics fail to capture differences in noisy, dynamic neural responses.
method Proposed an optimal transport distance metric for Gaussian processes.
result Metric effectively compares neural dynamics in different systems.
The relationships between game theory and quantum mechanics let us propose certain quantization relationships through which we could describe and understand not only quantum but also classical, evolutionary and the biological systems that were described before through the replicator dynamics. Quantum mechanics could be…
Automated tests detect interactions in unstructured data.
problem Detecting interactions between latent variables in low-dimensional systems.
method Derive two interaction tests based on pairwise interventions and integrate them into an active learning pipeline.
result Tests can identify more known biological interactions than random search and standard active learning baselines.
ODBAE detects complex phenotypes in biological data.
problem Challenges in identifying complex phenotypes from high-dimensional biological data.
method ODBAE (Outlier Detection using Balanced Autoencoders) identifies influential and high leverage points in latent relationships among multiple physiological parameters.
result ODBAE reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators.
New method integrates sparse parametric and nonparametric techniques for complex system modeling.
problem Lack of accurate modeling for complex biological systems due to nonlinearities.
method Sparse nonparametric estimation framework combining parametric and nonparametric techniques.
result Accurately captures nonlinearities in complex systems without prior information.
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks…
Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynam…
Deep neural networks (DNNs) transform stimuli across multiple processing stages to produce representations that can be used to solve complex tasks, such as object recognition in images. However, a full understanding of how they achieve this remains elusive. The complexity of biological neural networks substantially exc…
This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repea…
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, …
New estimator corrects bias in CKA for sparsely sampled neurons.
problem Bias in CKA for sparsely sampled neurons.
method Novel estimator that corrects for input and feature sampling.
result Reliable model-to-brain alignment with sparsely sampled neurons.
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience. We leverage current understanding of biological memory transfer to arrive at AI algorithms for memory consolidation and repl…
Perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease computationa…
Symmetry principles help in creating better AI representations.
problem Creating efficient and generalizable AI representations.
method Using symmetry transformations to guide representation learning.
result Symmetry principles improve data efficiency and generalizability in AI.
Deep learning detects bifurcations in dynamical systems.
problem Predicting catastrophic changes in dynamical systems across sciences.
method Data-driven, physically-informed deep-learning framework for classifying dynamical regimes and characterizing bifurcation boundaries.
result Extracts topologically invariant features to detect bifurcation boundaries in unseen systems.
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as…
VAEs help in learning latent variables for cryo-EM applications.
problem Learning latent variables for cryo-EM data.
method Used VAEs for latent variable learning, focusing on the encoder's role.
result The encoder of the VAE in cryo-EM applications resembles traditional latent variable representations.
Inspired by the importance of diversity in biological system, we built an heterogeneous system that could achieve this goal. Our architecture could be summarized in two basic steps. First, we generate a diverse set of classification hypothesis using both Convolutional Neural Networks, currently the state-of-the-art tec…
Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundame…
New algorithm uses random projections for robust, sparse data classification.
problem Improving robustness and sparsity in data classification.
method Randomly projects data into a high-dimensional space, truncates small entries, and applies a cap operation.
result The method enhances classification accuracy with minimal loss, especially in noisy conditions.
Unified platform for statistical and machine learning in bioinformatics.
problem Workflow inefficiencies in using multiple tools for data analysis.
method Automated hyperparameter optimization, feature importance analysis, statistical tests.
result Accelerates biological discovery workflows with methodological soundness.
The study analyzes stochastic Lie systems and their applications in various models.
problem Analyzing stochastic differential equations on manifolds.
method Coalgebra method for Hamiltonian stochastic Lie systems.
result New examples of stochastic Lie systems and Hamiltonian stochastic Lie systems are analyzed.
Automated digital twin discovery from biological data improves drug discovery and personalized medicine.
problem Developing reliable digital twins from noisy, incomplete biological data.
method Symbolic and sparse regression, Bayesian frameworks, deep learning, and large language models.
result Sparse regression generally outperforms symbolic regression, especially with Bayesian frameworks.
Study counterfactuals in cyclic systems with shifts and scales.
problem Counterfactual inference in cyclic systems with shifts and scales.
method Shift-scale interventions in cyclic SCMs.
result Valid inference in cyclic systems with shifts and scales.
A new model uses 'ghost units' to enable efficient backpropagation in deep neural networks.
problem How to achieve efficient backpropagation in deep neural networks with biological plausibility.
method Introduces 'ghost units' to cancel feedback, enabling efficient error backpropagation.
result Demonstrates that the model can approximate error gradients and achieve good performance on classification tasks.
Shannon's mathematical theory of communication defines fundamental limits on how much information can be transmitted between the different components of any man-made or biological system. This paper is an informal but rigorous introduction to the main ideas implicit in Shannon's theory. An annotated reading list is pro…
This paper explores adaptive neural activation in RNNs for better learning.
problem Fixed neural activation functions limit the performance and adaptability of RNNs.
method Developed a novel parametric family of nonlinear activation functions inspired by biological neurons.
result Adaptive neural activation improves learning speed and performance in RNNs.
Researchers create exact minimal surfaces with helical motifs in biological structures.
problem Analyzing helical motifs in minimal surfaces of biological structures.
method Developed a method to construct exact minimal surfaces with arbitrary helical motifs.
result Exact minimal surfaces with helical motifs can be created and analyzed.
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelli…