R. Beau Lotto

Floral Morphogenesis: Stochastic Explorations of a Gene Network Epigenetic Landscape

In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5–10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different genetic configurations during development.

The effects of color on brightness

Observation of human subjects shows that the spectral returns of equiluminant colored surrounds govern the apparent brightness of achromatic test targets. The influence of color on brightness provides further evidence that perceptions of luminance are generated according to the empirical frequency of the possible sources of visual stimuli, and suggests a novel way of understanding color contrast and constancy.

The empirical basis of color perception

Rationalizing the perceptual effects of spectral stimuli has been a major challenge in vision science for at least the last 200 years. Here we review evidence that this otherwise puzzling body of phenomenology is generated by an empirical strategy of perception in which the color an observer sees is entirely determined by the probability distribution of the possible sources of the stimulus. The rationale for this strategy in color vision, as in other visual perceptual domains, is the inherent ambiguity of the real-world origins of any spectral stimulus.

Sensitivity to numerosity is not a unique visuospatial psychophysical predictor of mathematical ability.

Highlights • Individual differences in numerosity acuity predict mathematical ability.• We tested 300+ participants to see if this relationship is unique to numerosity.• Visual numerosity and orientation task performance predicted mathematics scores.• Performance improved with age, and males significantly outperformed females.• This highlights links between mathematics and multiple visuospatial abilities.

An Empirical Explanation of the Chubb Illusion

The perceived difference in brightness between elements of a patterned target is diminished when the target is embedded in a similar surround of higher luminance contrast (the Chubb illusion). Here we show that this puzzling effect can be explained by the degree to which imperfect transmittance is likely to have affected the light that reaches the eye. These observations indicate that this illusion is yet another signature of the fundamentally empirical strategy of visual perception, in this case generated by the typical influence of transmittance on inherently ambiguous stimuli.

A systemic computation platform for the modelling and analysis of processes with natural characteristics

Computation in biology and in conventional computer architectures seem to share some features, yet many of their important characteristics are very different. To address this, [1] introduced systemic computation, a model of interacting systems with natural characteristics. Following this work, here we introduce the first platform implementing such computation, including programming language, compiler and virtual machine. To investigate their use we then provide an implementation of a genetic algorithm applied to the travelling salesman problem and also explore how SC enables self-adaptation with the minimum of additional code.

Analysing the evolvability of neural network agents through structural mutations

This paper investigates evolvability of artificial neural networks within an artificial life environment. Five different structural mutations are investigated, including adaptive evolution, structure duplication, and incremental changes. The total evolvability indicator, Etotal, and the evolvability function through time, are calculated in each instance, in addition to other functional attributes of the system. The results indicate that incremental modifications to networks, and incorporating an adaptive element into the evolution process itself, significantly increases neural network evolvability within open-ended artificial life simulations.

Visual development: Experience puts the colour in life

Recent findings show that colour processing, like most other sensory attributes, is shaped by experience. While such studies can reveal the mechanisms of development, can they also help uncover the mechanisms of perception?

Exploiting Natural Asynchrony and Local Knowledge within Systemic Computation to Enable Generic Neural Structures

Bio-inspired processes are involved more and more in today’s technologies, yet their modelling and implementation tend to be taken away from their original concept because of the limitations of the classical computation paradigm. To address this, systemic computation (SC), a model of interacting systems with natural characteristics, followed by a modelling platform with a bio-inspired system implementation were introduced. In this paper, we investigate the impact of local knowledge and asynchronous computation: significant natural properties of biological neural networks (NN) and naturally handled by SC. We present here a bio-inspired model of artificial NN, focussing on agent interactions, and show that exploiting these built-in properties, which come for free, enables neural structure flexibility without reducing performance.

Modular thinking: evolving modular neural networks for visual guidance of agents

This paper investigates whether replacing non-modular artificial neural network brains of visual agents with modular brains improves their ability to solve difficult tasks, specifically, survive in a changing environment. A set of experiments was conducted and confirmed that agents with modular brains are in fact better. Further analysis of the evolved modules characterised their function and determined their mechanism of operation. The results indicate that the greater survival ability is obtained due to functional specialisation of the evolved modules; good agents do well because their modules are more specialised at tasks such as reproduction and consumption. Overall, the more specialised the modules, the fitter the agents.