Biology subjects

Czegel, D.

Publications and source records attributed to Czegel, D..

7 recordsLinked to original sources

bioRxiv2023

Emergence of Darwinian evolution in Bayesian collectives

Building on the algorithmic equivalence between finite population replicator dynamics and particle filtering based approximation of Bayesian inference, we design a computational model to demonstrate the emergence of Darwinian evolution over representational units when collectives of units are selected to infer statistics of high-dimensional combinatorial environments. The non-Darwinian starting point is two units undergoing a few cycles of noisy, selection-dependent information transmission, corresponding to a serial (one comparison per cycle), non-cumulative process without heredity. Selection for accurate Bayesian inference at the collective level induces an adaptive path to the emergence of Darwinian evolution within the collectives, capable of maintaining and iteratively improving upon complex combinatorial information. When collectives are themselves Darwinian, this mechanism amounts to a top-down (filial) transition in individuality. We suggest that such a selection mechanism can explain the hypothesized emergence of fast timescale Darwinian dynamics over a population of neural representations within animal and human brains, endowing them with combinatorial planning capabilities. Further possible physical implementations include prebiotic collectives of non-replicating molecules and reinforcement learning agents with parallel policy search.

evolutionary biology↗

bioRxiv2022

General solution to biological signalling games: costly signalling and beyond

Explaining signal reliability poses a central problem in animal communication. According to Zahavis Handicap Principle (HP), signals are honest only when they are costly at the evolutionary equilibrium - hence the term handicap; otherwise, deception evolves, and communication breaks down. The HP has no theoretical or empirical support, despite claims to the contrary, and yet this idea remains immensely popular. Theoretical evaluations of the HP are difficult, however, because finding the equilibrium cost function in signalling games is notoriously complicated. Here we show how cost functions can be calculated for any arbitrary pairwise asymmetric signalling game at the evolutionary equilibrium. We clarify the relationship between signalling costs at equilibrium and the conditions for reliable signalling. We show that these two terms are independent and the costs of signalling at honest equilibrium have no effect on the stability of communication. We show that honest signals can take any cost value, even negative, being beneficial for the signaller independently of the receivers response at equilibrium, without requiring further constraints. Our results are general, and apply to seminal signalling models, including Grafens model of sexual selection and Godfrays model of parent-offspring communication. Our results refute the claim that signals must be costly at the evolutionary equilibrium to be reliable, as predicted by the HP and so-called "costly signalling" theory. The handicap paradigm can thus be fully rejected. We provide testable predictions to help advance the field and establish a better explanation for honest signals.

evolutionary biology↗

bioRxiv2020

Darwinian dynamics over recurrent neural computations for combinatorial problem solving

Efficient search in enormous combinatorial spaces is an essential component of intelligence. Humans, for instance, are often found searching for optimal action sequences, linguistic structures and causal explanations. Is there any computational domain that provides good-enough and fast-enough solutions to such a diverse set of problems, yet can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of informational patterns, is a promising candidate. It is, in effect, a stochastic parallel search that i) does not need local gradient-like information and ii) redistributes its computational resources from globally bad to globally good solution candidates automatically. Here we demonstrate these concepts in a proof-of-principle model based on dynamical output states of reservoir computers as units of evolution. We show that a population of reservoir computing units, arranged in one or two-dimensional topologies, is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also provide a detailed analysis of how neural quantities, such as noise and topology, translate to evolutionary ones, such as mutation rate and population structure. We demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We point at the importance of neural representation, akin to genotype-phenotype maps, in determining the efficiency of any evolutionary search in the brain. Novel analysis methods are developed, including neural firing pattern phylogenies that display the unfolding of the process.

neuroscience↗

bioRxiv2020

Constructing Graphs from Genetic Encodings

Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic principles that guide brain connectivity, we derive a network encoding process that can utilize wiring rules to reproducibly generate specific topologies. To illustrate the representational power of this approach, we propose stochastic and deterministic processes for generating a wide range of network topologies. Specifically, we detail network heuristics that generate structured graphs, such as feed-forward and hierarchical networks. In addition, we characterize a Random Genetic (RG) family of networks, which, like Erd[o]s-Renyi graphs, display critical phase transitions, however their modular underpinnings lead to markedly different behaviors under targeted attacks. The proposed framework provides a relevant null-model for social and biological systems, where diverse metrics of identity underpin a nodes preferred connectivity.

neuroscience↗

bioRxiv2019

Evolutionary implementation of Bayesian computations

The Bayesian framework offers a flexible language for the consistent modular assembly of statistical models used by both minds and machines. Another algorithmic domain capable of adaptation in potentially high-dimensional and uncertain environments is Darwinian evolution. The equivalence of their fundamental dynamical equations, replicator dynamics and Bayesian update, hints at a deeper algorithmic analogy. Here we show, based on a unified mathematical discussion of evolutionary dynamics and statistical learning in terms of Bayesian graphical models, that this is indeed the case. Building blocks of Bayesian computations, such as inference in hierarchical models, filtering in hidden Markov models, gradient likelihood optimization, and expectation-maximization dynamics of mixture models, map naturally to fundamental concepts of evolution: multilevel selection, quasispecies dynamics, phenotypic adaptation and ecological competition, respectively. We believe that these correspondences point towards a more comprehensive understanding of flavors of adaptive computation observed in Nature, as well as suggesting new ways to combine insights from the two domains in engineering applications.

evolutionary biology↗

bioRxiv2018

Major evolutionary transitions as Bayesian structure learning

Complexity of life forms on Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level evolutionary units. Although this process has been likened to the recursive combination of pre-adapted subsolutions in the framework of learning theory, no general mathematical formalization of this analogy has been provided yet. Here we show, building on former results connecting replicator dynamics and Bayesian update, that (i) evolution of a hierarchical population under multilevel selection is equivalent to Bayesian inference in hierarchical Bayesian models, and (ii) evolutionary transitions in individuality, driven by synergistic fitness interactions, is equivalent to learning the structure of hierarchical models via Bayesian model comparison. These correspondences support a learning theory oriented narrative of evolutionary complexification: the complexity and depth of the hierarchical structure of individuality mirrors the amount and complexity of data that has been integrated about the environment through the course of evolutionary history.

evolutionary biology↗

bioRxiv2017

One problem, too many solutions: How costly is honest signalling of need?

The \"cost of begging\" is a prominent prediction of costly signalling theory, suggesting that offspring begging has to be costly in order to be honest. More specifically, it predicts that there is a single cost function for the offspring (depending on e.g. offspring quality) that maintains honesty and it must be proportional to parents fitness loss. Here we show another interpretation of the cost. We demonstrate that cost, proportional to the fitness gain of the offspring, also results in honest signalling. Since the loss of the parent does not necessarily coincide with the gain of the offspring, it is provable that any linear combination of the two cost functions (one proportional to parents loss, one to offsprings gain) also leads to honest signalling. Our results, applied for a specific model, support the previous general conclusion that signalling games have different cost functions for different equilibria. Consequently, costly signalling theory cannot predict a unique equilibrium cost in signalling games especially in case of parent-offspring conflicts. As an important consequence, any measured equilibrium cost in real cases has to be compared both to the parents fitness loss and to the offsprings fitness gain in order to provide meaningfully interpretation.

evolutionary biology↗