Life, learning and emergence
Developing mathematical foundations for life and artificial life, machine intelligence, and other emergent phenomena that defy reductionism.
What is life? Darwin’s theory provides a qualitative understanding of evolution. But from a physics perspective, we don’t know how life got started in the first place. We investigate the thermodynamic basis for emergent self-replication and adaptation, of which biology is just one instance. Can this be used to engineer artificial digital life? Can evolution itself be made a predictive science?
How do we make intelligent machines? Far from approaching artificial general intelligence, AI is stuck in high-dimensional curve-fitting. We seek mathematical insights that could lead to more intelligent AI, such as causal reasoning, reusable functional modules, and a representation of the environment. We investigate ways to use computation and AI to automate the search for new mathematical insights. Are there fundamental limits to AI, and what might this tell us about human intelligence?
What are the emergent properties of digital and neural computation, and might this shed light on autonomy and free will? We study information processing at the genetic level and the functional architecture of gene regulatory networks. We seek a theoretical understanding of cell programming and how to infer programming sets. Is causality itself an emergent phenomenon, as we traverse across different organisational length scales?
LCP
Related papers
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The bipartite nature of regulatory networks means gene-gene logics are composed, which severely restricts which ones can show up in life.
Cell soup in screens
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Optimal electronic reservoirs
Balancing memory from linear components with nonlinearities from memristors optimises the computational capacity of electronic reservoirs.
Flowers of immortality
The eigenvalues of the mortality equation fall into two classes—the flower and the stem—but only the stem eigenvalues control the dynamics.
Structure of genetic computation
The structural and functional building blocks of gene regulatory networks correspond, which tell us how genetic computation is organised.
True scale-free networks
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Coexistence in diverse ecosystems
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Quick quantum neural nets
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I want to be forever young
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Deep layered machines
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Replica analysis of overfitting
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Replica clustering
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Memristive networks
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Physics of networks
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Information asymmetry
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Hierarchies in directed networks
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Dirac cones in 2D borane
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Quantum neural networks
We generalise neural networks into a quantum framework, demonstrating the possibility of quantum auto-encoders and teleportation.
Debunking in a world of tribes
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Memristive networks and learning
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Dynamics of memristors
Exact equations of motion provide an analytical description of the evolution and relaxation properties of complex memristive circuits.
3d grains from 2d slices
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Disentangling links in networks
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Quantum jumps in thermodynamics
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Worst-case work entropic equality
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Spectral partitioning
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Optimal growth rates
An extension of the Kelly criterion maximises the growth rate of multiplicative stochastic processes when limited resources are available.
Photonic Maxwell's demon
With inspiration from Maxwell’s classic thought experiment, it is possible to extract macroscopic work from microscopic measurements of photons.
Eigenvalues of neutral networks
The principal eigenvalue of small neutral networks determines their robustness, and is bounded by the logarithm of the number of vertices.
Self-organising adaptive networks
An adaptive network of oscillators in fragmented and incoherent states can re-organise itself into connected and synchronized states.
Optimal heat exchange networks
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Instability in complex ecosystems
The community matrix of a complex ecosystem captures the population dynamics of interacting species and transitions to unstable abundances.
Form and function in gene networks
The structural properties of a network motif predict its functional versatility and relate to gene regulatory networks.
Clusters of neurons
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Bootstrap percolation models
A subset of bootstrap percolation models, which stabilise systems of cells on infinite lattices, exhibit non-trivial phase transitions.
Protein interaction experiments
Properties of protein interaction networks test the reliability of data and hint at the underlying mechanism with which proteins recruit each other.
A measure of majorization
Single-shot information theory inspires a new formulation of statistical mechanics which measures the optimal guaranteed work of a system.
Structure and stability of salts
The stable structures of calcium and magnesium carbonate at high pressures are crucial for understanding the Earth's deep carbon cycle.
From memory to scale-free
A local model of preferential attachment with short-term memory generates scale-free networks, which can be readily computed by memristors.
Maximum percolation time
A simple formula gives the maximum time for an n x n grid to become entirely infected having undergone a bootstrap percolation process.
Easily repairable networks
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Entanglement typicality
A review of the achievements concerning typical bipartite entanglement for random quantum states involving a large number of particles.
Percolation on Galton-Watson trees
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Random close packing fractions
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Multitasking immune networks
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Scales in weighted networks
Information theory fixes weighted networks’ degeneracy issues with a generalisation of binary graphs and an optimal scale of link intensities.
Multi-tasking in immune networks
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Bootstrapping topology and risk
Information about 10% of the links in a complex network is sufficient to reconstruct its main features and resilience with the fitness model.
Weighted network evolution
A statistical procedure identifies dominant edges within weighted networks to determine whether a network has reached its steady state.
Hierarchical space frames
A systematic way to vary the power-law scaling relations between loading parameters and volume of material aids the hierarchical design process.
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Network analysis of diagnostic data identifies combinations of the key factors which cause Class III malocclusion and how they evolve over time.
Robust and assortative
Spectral analysis shows that disassortative networks exhibit a higher epidemiological threshold and are therefore easier to immunize.
Clustering inverted
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What you see is not what you get
Methods from tailored random graph theory reveal the relation between true biological networks and the often-biased samples taken from them.
Shear elastic deformation in cells
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Dynamics of Ising chains
A transfer operator formalism solves the macroscopic dynamics of disordered Ising chain systems which are relevant for ageing phenomena.
Diffusional liquid-phase sintering
A Monte Carlo model simulates the microstructural evolution of metallic and ceramic powders during the consolidation process liquid-phase sintering.
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The information needed to self-assemble a structure quantifies its modularity and explains the prevalence of certain structures over others.
Random cellular automata
Of the 256 elementary cellular automata, 28 of them exhibit random behavior over time, but spatio-temporal currents still lurk underneath.