Swarms of birds and fish produce well-organized behaviors even though each individual only interacts with their neighbors. Previous studies attempted to derive individual interaction rules using heuristic assumptions from data on captured animals. We propose a machine learning method to obtain the sensorimotor mapping mechanism of individuals directly from captured data. Data on swarm behaviors in fish was captured, and individual positions are determined. The sensory inputs and motor outputs are estimated and used as training data. A simple feedforward neural network is trained to learn the sensorimotor mapping of individuals. The trained network is implemented in the simulated environment and resulting swarm behaviors are investigated. As a result, our trained neural network could reproduce the swarm behavior better than the Boids model. The reproduced swarm behaviors are evaluated in terms of three different measures, and the difference from the Boids model is discussed.

This paper presents a novel application of agent-based simulation software to tune real greenhouse infrastructure containing flowering seed or vegetable crop plants and their insect pollinators. Greenhouses provide controlled environments for the growth of high-value crops. As global climate and weather become more unpredictable, we are becoming more dependent upon technologically sophisticated greenhouses for reliable crop production. For crop pollination in a greenhouse, although manual or technological alternatives have been explored, pollination by bees is still required in many crops for the best seed yields and food quality. However, the design of greenhouses is driven primarily by the requirements of the plants rather than the pollinators. In light of this, we have designed simulations to explore improvements to greenhouse conditions and layout that benefit the insect pollinators and assist them to pollinate the crop. The software consists of an agent-based model of insect behaviour that is used to predict pollination outcomes under a range of conditions. The best parameters discovered in simulation can be used to adjust real greenhouse layouts. We present a key test case for our method, and discuss future work in which the technique has the potential to be applied in a continuous feedback loop providing predictions of greenhouse re-configurations that can be made by real-time control systems in a modern greenhouse. This is a novel approach linking simulation behaviour to real techno-ecological systems to improve crop and seed yield from valuable greenhouse infrastructure.

Artificial life has been developing a behavior-based perspective on the origins of life, which emphasizes the adaptive potential of agent-environment interaction even at that initial stage. So far this perspective has been closely aligned to metabolism-first theories, while most researchers who study life’s origins tend to assign an essential role to RNA. An outstanding challenge is to show that a behavior-based perspective can also address open questions related to the genetic system. Accordingly, we have recently applied this perspective to one of science’s most fascinating mysteries: the origins of the standard genetic code. We modeled horizontal transfer of cellular components in a population of protocells using an iterated learning approach and found that it can account for the emergence of several key properties of the standard code. Here we further investigated the diachronic emergence of artificial codes and discovered that the model’s most frequent sequence of amino acid assignments overlaps significantly with the predictions in the literature. Our explorations of the factors that favor early incorporation into an emerging artificial code revealed two aspects: an amino acid’s relative probability of horizontal transfer, and its relative ease of discriminability in chemical space.

The predictive processing theory of cognition and neural encoding dictates that hierarchical regions in the neocortex learn and encode predictive hypotheses of current and future stimuli. To better handle uncertainty these regions must also learn, infer, and encode the precision of stimuli. In this treatment we investigate the potential of handling uncertainty within a single learned predictive model. We exploit the rich predictive models formed by the learning of temporal sequences within a Hierarchical Temporal Memory (HTM) framework, a cortically inspired connectionist system of self-organizing predictive cells. We weight a cell’s feedforward response by the degree of its own temporal expectations of a response. We test this model on data that has been saturated with temporal or spatial noise, and show significant improvements over traditional HTM systems. In addition we perform an experiment based on the Posner cuing task and show that the system displays phenomena consistent with attention and biased competition. We conclude that the observed effects are similar to those of precision encoding.

The assumption that action and perception can be investigated independently is entrenched in theories, models and experimental approaches across the brain and mind sciences. In cognitive science, this has been a central point of contention between computationalist and 4Es (enactive, embodied, extended and embedded) theories of cognition, with the former embracing the “classical sandwich”, modular, architecture of the mind and the latter actively denying this separation can be made. In this work we suggest that the modular independence of action and perception strongly resonates with the separation principle of control theory and furthermore that this principle provides formal criteria within which to evaluate the implications of the modularity of action and perception. We will also see that real-time feedback with the environment, often considered necessary for the definition of 4Es ideas, is not however a sufficient condition to avoid the “classical sandwich”. Finally, we argue that an emerging framework in the cognitive and brain sciences, active inference, extends ideas derived from control theory to the study of biological systems while disposing of the separation principle, describing non-modular models of behaviour strongly aligned with 4Es theories of cognition.

The concept of autonomy is fundamental for understanding biological organization and the evolutionary transitions of living systems. Understanding how a system constitutes itself as an individual, cohesive, self-organized entity is a fundamental challenge for the understanding of life. However, it is generally a difficult task to determine whether the system or its environment has generated the correlations that allow an observer to trace the boundary of a living system as a coherent unit. Inspired by the framework of integrated information theory, we propose a measure of the level of integration of a system as the response of a system to partitions that introduce perturbations in the interaction between subsystems, without assuming the existence of a stationary distribution. With the goal of characterizing transitions in integrated information in the thermodynamic limit, we apply this measure to kinetic Ising models of infinite size using mean field techniques. Our findings suggest that, in order to preserve the integration of causal influences of a system as it grows in size, a living entity must be poised near critical points maximizing its sensitivity to perturbations in the interaction between subsystems. Moreover, we observe how such a measure is able to delimit an agent and its environment, being able to characterize simple instances of agent-environment asymmetries in which the agent has the ability to modulate its coupling with the environment.

Animals develop spatial recognition through visuomotor integrated experiences. In nature, animals change their behavior during development and develop spatial recognition. The developmental process of spatial recognition has been previously studied. However, it is unclear how behavior during development affects the development of spatial recognition. To investigate the effect of movement pattern (behavior) on spatial recognition, we simulated the development of spatial recognition using controlled behaviors. Hierarchical recurrent neural networks (HRNNs) with multiple time scales were trained to predict visuomotor sequences of a simulated mobile agent. The spatial recognition developed with HRNNs was compared for various values of randomness of the agent’s movement. The experimental results show that spatial recognition was not developed for movements with a randomness that was too small or too large but for movements with intermediate randomness.

The “synthetic method” is the methodological approach that guides current scientific attempts of understanding natural processes by the construction of hardware, software, and/or wetware models from scracth. It focuses the scientific inquiry on the generative mechanisms of the target processes, with the goal of testing and improving scientific hypotheses about them. This article presents an application of the synthetic method based on cutting-edge technology: the construction of “synthetic cells” (also known as “artificial cells”) capable of exchanging chemical signals (and, in this sense, of ‘communicating’) with biological cells.

Simulating phenomenological aspects of altered states of consciousness provides an important experimental tool for consciousness science and psychiatry. Here we describe the Hallucination Machine, which comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display. The Hallucination Machine enables the simulation of visual hallucinatory experiences in a biologically plausible and ecologically valid way. We show that the system induces visual phenomenology qualitatively similar to classical psychedelics. The Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.

Our previous study showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) can learn a behavior as they avoid stimulation from outside. In a sense, the embodied neural network can autonomously change their activity to avoid external stimuli. In this paper, as a result of our experiments using cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: if the network cannot learn to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes as if to ignore the input neurons. We also show such a behavior is reproduced by spiking neural networks with asymmetric-STDP. We consider that these properties can be regarded as autonomous regulation of self and non-self for the network.

In this paper we report the first results of evolving bio-hybrid societies. Our goal is to have robots that are integrated in an animal society, and here we evolve robot controllers using animals as fitness providers, directly judging the success of integration. In particular, we are using juvenile honeybees and robots that are able to produce vibration patterns. Previous studies have shown that honeybees react to different vibration patterns, such as exhibiting freezing or stopping behaviours. In this paper we investigate whether we are able to evolve a vibration pattern that acts as a locally acting ‘stop signal’ for bees. Honeybees were placed in two containers with no communication between them: one with an active, vibrating robot, and a second with a passive robot. Post-hoc evaluations of key evolved digital genotypes generally confirm fitness values obtained during evolution. We also tested the transferability of key genotypes to a single container, in which bees are free to visit one vibrating and two dummy robots. Encouragingly, most genotypes are able to selectively stop bees, i.e., only in the vicinity of the vibrating robot, despite having been evolved under the more constrained setup. These results speak to the value of an evolutionary approach for discovering how to interact with animals.