Minimal Cognition

Situated and embodied reactive agents solve relatively complex tasks by coordinating action and perception. Reactive agents are generally believed to be incapable of dealing with identical perceptual states that require different responses (i.e., perceptual ambiguity). In contrast to reactive agents, non-reactive agents can deal with perceptual ambiguity by storing and integrating sensory information over time. This paper investigates how and to what extend reactive and non-reactive agents cope with perceptual ambiguity. An active categorical perception model is introduced in which agents with different neuro-controllers categorise objects by coordinating action and perception. The agent’s neuro-controllers are evolutionary optimised for the task. Our results show that reactive agents can cope with perceptual ambiguity despite their incapability to store perceptual information over time. An analysis of behaviour reveals that while non-reactive agents use internal memory to represent...

The 'simulation hypothesis' is an intriguing explanation for cognition, and holds that 'thinking consists of simulated interaction with the environment' ([4], p.242). However, the neuroscientific proof for a simulation mechanism in the brain is indirect. In this paper we present a minimal-model approach to investigate the 'simulation hypothesis'. Our minimal model is called ACP and is an extension of the Active Categorical Perception model (ACP) presented in . In ACP , robots have a neurocontroller with an output-input feedback mechanism that allows them to simulate perception and behaviour internally. Our experiments focus on the performance of robots with three different types of neurocontroller (two feedforward and one recurrent type of neurocontroller). Their performance is compared over three experimental conditions in which the output-input feedback mechanism is functional for variable durations. The results show that feedforward-neurocontrolled robots benefit from output-input feedback, while recurrent-neurocontrolled robots do not. Based on these results, two closely related conclusions are drawn: (1) the 'simulation hypothesis' may be too specific, and (2) predicting future perception may depend on neural recurrency (i.e., internal feedback) in general, rather than on the ability to simulate perception by feeding back actions.

In his doctoral thesis, L'individu et sa genèse physico-biologique, Simondon formulates three key philosophical claims that we consider crucial. First, in complex physical or biological systems, individuation is not merely a property of an individual, rather, individuality always results from an individuation process. Second, a biological individuated system has a distinctive feature: it acts on its own stage. Third, there must be some non-trivial recursive procedure through which a physical individuated system can switch into a biological one. In this chapter, we propose a philosophical model to further develop this conceptual scheme. This will allow us to characterize biological individuation as a second-order organisation, and to directly connect it with the two concepts of heteronomy and adaptability. We will show that, unlike physical individuation, biological individuation is self-specifying. However it does not do so within a logic of maintenance, as proposed by Francisco Varela, who used a symbolism similar to us. Instead, it follows a logic of heteronomy and adaptability, the logic of life. Varela's model therefore appears to be a special case within a broader theoretical framework, which is the only way to understand how new constraints/norms/functions can emerge in a biological system, whether at the evolutionary, ontogenetic or behavioural level. Finally, to say that biological individuation is self-specifying is to say that it can be interpreted, and that we cannot understand how a biological system works if we remain fixated on a strictly causal mode of explanation, and on the search for mechanisms, as if it were a watch, a radiator or a computer. We fail to see that, through and by specific biological repair, immune and perceptive devices, it works in a world of signs, in such a way that biological organisation and cognition are always already coupled at the outset. It is only evolution that will gradually uncouple what has been coupled.

This chapter delves into the profound journey from raw data to meaningful knowledge in the realm of artificial intelligence (AI) from a biosemiotic standpoint. It begins by scrutinizing Claude Shannon’s Information theory, the bedrock of data quantification, storage, and transmission, underlining the significance of bits as the fundamental units of information. The biosemiotic approach to AI is then explored, driven by inspiration from the human brain’s workings in AI and machine learning. Despite its merits, the approach is critically examined for its anthropocentric bias, disregarding the rich diversity of cognitive systems in the natural world. Drawing from biosemiotics, this chapter further investigates biocommunication and information processing in simpler organisms, emphasizing the concept of “Umwelt” and the exchange of information within an organism’s perceptual sphere. Additionally, the role of genetic codes and the hierarchical structure of meaning in biological information are explored. In conclusion, this chapter advocates for a holistic understanding of information processing, merging insights from biosemiotics to bridge the chasm between data and meaning in AI. By embracing a broader perspective that incorporates diverse cognitive systems and semiotic processes, this approach paves the way for more robust and context-aware AI systems, with far-reaching applications across various domains.

This paper outlines an approach to analysing minimal cognition that brings out its social and historical dimensions. It proposes a model, the coordinated systems approach (CSA), which understands cognition as a coordinated coalition of loosely autonomous processes responsible for goal-directedness in a system. On this view, even individual cognition has something of a social flavour to it. The central concept of the paper is stigmergy: a process where the material 1 trace of actions of system elements in their environment is a sign that coordinates a group of semi-autonomous processes in future actionsthis is the social dimension. The historical dimension refers to longer term processes which establish the coordinative power of the sign and endow it with normative force. According to this proposal, a full explanation of cognitive capabilities should reference both dimensions. In the second half of the paper the CSA is let loose on some puzzles in 4E cognition. Can the model deal with old problems such as that of cognitive bloat, or new problems such as the supposed external memory of the slime mould Physarum polycephalum? Potentially, the approach could be used to analyse minimal cognitive phenomena over a range of scales from bacteria to human beings.

We study the dynamic categorization ability of an autonomous agent that distinguishes rectangular and triangular objects. The objects are distributed on a two-dimensional space and the agent is equipped with a recurrent neural network that controls its navigation dynamics. As the agent moves through the environment, it develops neural states which, while not symbolic representations of rectangles or triangles, allow it to distinguish these objects. As a result, it decides to avoid triangles and remain for longer periods of time at rectangles. A significant characteristic of the network is its plasticity, which enables the agent to switch from one navigation mode to another. Diversity of this switching behavior will be discussed.

Synaptic homeostasis, a fundamental mechanism in biological neural networks, plays a crucial role in maintaining stability and adaptability in response to environmental changes. In this project, we explore the application of synaptic homeostasis in the design of a line-following robotic system capable of autonomously tracing a predefined path at constant speed, despite various disturbances or changes in the line's path. Synaptic homeostasis will dynamically adjust the network's synaptic weights to maintain stable performance. Unlike typical line-following behaviours that may involve varying acceleration, particularly in complex paths such as curves with steep curvature, this study aims to achieve a constant velocity, mirroring the regulatory functions observed in biological organisms. The initial phase involves testing in a simplified environment, typically a straight path, to fine-tune the synaptic homeostasis mechanism for speed regulation. Subsequently, the project escalates the complexity of environmental conditions-introducing obstacles, curve paths, and disturbances-to evaluate the adaptive capabilities of the system under diverse challenges. Through this hierarchical testing approach, the project not only investigates the potential of synaptic homeostasis in refining robotic navigation and control but also contributes to the broader discourse on the application of neurobiological principles in robotics. This study anticipates demonstrating that synaptic homeostasis can significantly enhance robotic adaptability, offering insights into designing more resilient and efficient autonomous systems.

Eating is a fundamental behavior in which all organisms must engage in order to procure the material and energy from their environment that they need to maintain themselves. Since controlling eating requires procuring, processing, and assessing information, it constitutes a cognitive activity that provides a productive domain for pursuing cognitive biology as proposed by Ladislav Kováč. In agreement with Kováč, we argue that cognition is fundamentally grounded in chemical signaling and processing. To support this thesis, we adopt Cisek’s strategy of phylogenetic refinement, focusing on two animal phyla, Porifera and Placozoa, organisms that do not have neurons, muscles, or an alimentary canal, but nonetheless need to coordinate the activity of cells of multiple types in order to eat. We review what research has revealed so far about how these animals gather and process information to control their eating behavior.

This is a research project submitted to the DAAD Re-invitation Programme for former scholarship-holders. The scholarship was granted in 2024.

This research project serves a two-fold purpose: our primary goal is to reconstruct the main theses regarding culture and nature in the new edition of Husserl’s Ideas II; our secondary goal is to articulate those theses with contemporary discussions in the philosophy of Cognitive Science regarding the role of culture. Originally published in 1952 by Marly Biemel, Ideas II is currently being re-edited in a new critical edition by Dirk Fonfara at the Husserl Archiv at the Universität zu Köln and is expected to be published by 2024. This new edition sheds new light on central issues of Husserlian phenomenology – especially, on themes such as nature, selfhood, the role of the body, sociality, action, empathy, and culture. I propose to connect the textual findings on the latter topic with contemporary discussions in cognitive science regarding cultural permeation – as opposed to cognitive penetration – and sociocultural scaffolding. After a section stating the problem and the project’s justification (sect. 2), I present the project’s main Hypothesis, Research Questions and Objectives in section 3; in section 4, I develop my proposal while discussing both the exegetical literature on Husserl and the systematic literature on culture; sections 5 and 6 introduce and contextualize the theoretical framework and methodology employed in this research; section 7 points out to the main results of the research. References (sect. 8) include only cited material, while the actual schedule of the research stay is to be found in a separate file.

We offer a framework for the design and use of Ambient Smart Environments (ASEs) for preventive mental health care support. Drawing from Complex Systems Theory (CST) and 'E' Cognitive Science (ECS), we claim that ASEs have the potential to act in a preventive capacity in support of good mental health, i.e. supporting dynamics that avoid so-called "struck states" (which are, according to CST, thought generally to underpin forms of psychopathology). Here, we frame our discussion with what has recently been termed the "mind-technology problem". We define and characterise ASE systems, present some examples, and briefly survey some existing theoretical work. After introducing the essential CST terminology, the paper goes on to apply CST to explain developmental adaptation to continuously changing (smart) environments. Understanding the ASE's navigation in terms of a dynamic geometry between attracting and repelling points (or local minima/local maxima), allows us to develop neurotechnology that can augment clinical interventions by predicting upcoming shifts for good symptomatic outcomes, i.e. when a preventive intervention (i.e. destabilisation) should take place. We further offer clear directions for the development and design of such neurotechnology.

Fungal organisms can perceive the outer world in a way similar to what animals sense. Does that mean that they have full awareness of their environment and themselves? Is a fungus a conscious entity? In laboratory experiments we found that fungi produce patterns of electrical activity, similar to neurons. There are low and high frequency oscillations and convoys of spike trains. The neural-like electrical activity is yet another manifestation of the fungal intelligence. In this paper we discuss fungal cognitive capabilities and intelligence in evolutionary perspective, and question whether fungi are conscious and what does fungal consciousness mean, considering their exhibiting of complex behaviours, a wide spectrum of sensory abilities, learning, memory and decision making. We overview experimental evidences of consciousness found in fungi. Our conclusions allow us to give a positive answer to the important research questions of fungal cognition, intelligence and forms of conscious...

Exploring free space (scouting) efficiently is a non-trivial task for organisms of limited perception, such as the amoeboid Physarum polycephalum. However, the strategy behind its exploratory behaviour has not yet been characterised. In this organism, as the extension of the frontal part into free space is directly supported by the transport of body mass from behind, the formation of transport channels (routing) plays the main role in that strategy. Here, we study the organism's exploration by letting it expand through a corridor of constant width. When turning at a corner of the corridor, the organism constructed a main transport vein tracing a centre-in-centre line. We argue that this is efficient for mass transport due to its short length, and check this intuition with a new algorithm that can predict the main vein's position from the frontal tip's progression. We then present a numerical model that incorporates reaction-diffusion dynamics for the behaviour of the organism's growth front and current reinforcement dynamics for the formation of the vein network in its wake, as well as interactions between the two. The accuracy of the model is tested against the behaviour of the real organism and the importance of the interaction between growth tip dynamics and vein network development is analysed by studying variants of the model. We conclude by offering a biological interpretation of the well-known current reinforcement rule in the context of the natural exploratory behaviour of Physarum polycephalum.

The research leading to the results of Professor Oscar Castro has received funding from the European Regional Development Fund (IUT 2-44, Semiotic modeling of self-description mechanisms: theory and applications) and the Estonian Research Council (MOBJD213, Mobilitas Pluss Postdoctoral Researcher Grant)" 3. on page 60, in the review of "Von Uexküll and Mackinonn's book, xxxx" appears and not the date of the book. I have gone to the bibliography, p. 70, and have "2018" (false). The book's reference is von Uexküll, J. (auth.

Both metabolism and behavior play a key role in biological theory and artificial life modelling. Yet, despite their centrality there has been very little exploration of the relationship between these concepts and almost no exploration of how the interaction between the two could impact on evolution or instantiate alternative mechanisms for evolutionary processes. We present a simulation model of bacteria capable of metabolism-based chemotaxis: a minimal metabolic system capable of modulating behavior by influencing the probability of flagellar rotation (like in E. coli chemotaxis). We perform two illustrative experiments. In the first, the incorporation of a chemical compound into metabolism qualitatively improves the chemotactic strategy. In the second, an encounter with a specific chemical compound leads to a reaction that opens up a new metabolic pathway while automatically regulating chemotaxis towards that same compound. Both experiments illustrate the adaptive potential of metabolism-based behavior and can be used to explore the idea of "Behavioral Metabolution," a co-evolutionary synergy between behavior and metabolism. We abstract some principles of behavioral metabolution and discuss its application to early prebiotic evolution.

According to the sensorimotor approach, perception is a form of embodied know-how, constituted by lawful regularities in the sensorimotor flow or in sensorimotor contingencies (SMCs) in an active and situated agent. Despite the attention that this approach has attracted, there have been few attempts to define its core concepts formally. In this paper, we examine the idea of SMCs and argue that its use involves notions that need to be distinguished. We introduce four distinct kinds of SMCs, which we define operationally.These are the notions of sensorimotor environment (open-loop motor-induced sensory variations), sensorimotor habitat (closed-loop sensorimotor trajectories), sensorimotor coordination (reliable sensorimotor patterns playing a functional role), and sensorimotor strategy (normative organization of sensorimotor coordinations). We make use of a minimal dynamical model of visually guided categorization to test the explanatory value of the different kinds of SMCs. Finally, we discuss the impact of our definitions on the conceptual development and empirical as well as model-based testing of the claims of the sensorimotor approach.

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis.

This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual info...

Both metabolism and behavior play a key role in biological theory and artificial life modelling. Yet, despite their centrality there has been very little exploration of the relationship between these concepts and almost no exploration of how the interaction between the two could impact on evolution or instantiate alternative mechanisms for evolutionary processes. We present a simulation model of bacteria capable of metabolism-based chemotaxis: a minimal metabolic system capable of modulating behavior by influencing the probability of flagellar rotation (like in E. coli chemotaxis). We perform two illustrative experiments. In the first, the incorporation of a chemical compound into metabolism qualitatively improves the chemotactic strategy. In the second, an encounter with a specific chemical compound leads to a reaction that opens up a new metabolic pathway while automatically regulating chemotaxis towards that same compound. Both experiments illustrate the adaptive potential of metabolism-based behavior and can be used to explore the idea of "Behavioral Metabolution," a co-evolutionary synergy between behavior and metabolism. We abstract some principles of behavioral metabolution and discuss its application to early prebiotic evolution.

According to the sensorimotor approach, perception is a form of embodied know-how, constituted by lawful regularities in the sensorimotor flow or in sensorimotor contingencies (SMCs) in an active and situated agent. Despite the attention that this approach has attracted, there have been few attempts to define its core concepts formally. In this paper, we examine the idea of SMCs and argue that its use involves notions that need to be distinguished. We introduce four distinct kinds of SMCs, which we define operationally.These are the notions of sensorimotor environment (open-loop motor-induced sensory variations), sensorimotor habitat (closed-loop sensorimotor trajectories), sensorimotor coordination (reliable sensorimotor patterns playing a functional role), and sensorimotor strategy (normative organization of sensorimotor coordinations). We make use of a minimal dynamical model of visually guided categorization to test the explanatory value of the different kinds of SMCs. Finally, we discuss the impact of our definitions on the conceptual development and empirical as well as model-based testing of the claims of the sensorimotor approach.

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis.

Mathematical modeling of the emergent dynamics of gene regulatory networks (GRN) faces a double challenge of (a) dependence of model dynamics on parameters, and (b) lack of reliable experimentally determined parameters. In this paper we compare two complementary approaches for describing GRN dynamics across unknown parameters: (1) parameter sampling and resulting ensemble statistics used by RACIPE (RAndom CIrcuit PErturbation), and (2) use of rigorous analysis of combinatorial approximation of the ODE models by DSGRN (Dynamic Signatures Generated by Regulatory Networks). We find a very good agreement between RACIPE simulation and DSGRN predictions for four different 2- and 3-node networks typically observed in cellular decision making. This observation is remarkable since the DSGRN approach assumes that the Hill coefficients of the models are very high while RACIPE assumes the values in the range 1-6. Thus DSGRN parameter domains, explicitly defined by inequalities between systems p...

This paper analyses the recent debates on the emerging science of plant neurobiology, which claims that the individual green plant should be considered as an intelligent organism. Plant neurobiology tries to use elements from animal physiology as elegant metaphors to trigger the imagination in solving complex plant physiological elements of signalling, internal and external plant communication and whole-plant organisation. Plant neurobiology proposes useful concepts that stimulate discussions on plant behaviour. To be considered a new science, its added value to existing plant biology needs to be presented and critically evaluated. A general, scientific approach is to follow the so-called 'parsimony principle', which calls for simplest ideas and the least number of assumptions for plausible explanation of scientific phenomena. The extent to which plant neurobiology agrees with or violates this general principle needs to be examined. Nevertheless, innovative ideas on the complex mechanisms of signalling, communication, patterning and organisation in higher plants are badly needed. We present current views on these mechanisms and the specific role of auxins in regulating them.

This paper develops models for single recurrent neural circuits, known as autapses, where the axon of a neuron forms synapses onto its own dendrites. Once thought to be curiosities or artefacts of growing cells in vitro, autapses play a key role in the operation of Central Pattern Generators and the cortex where they may function as a simple short-term memory. Biologically plausible, idealized models of the autapse are able to produce arrhythmic, sustained behaviours in 'neural vehicles'. Detailed models are developed to show how excitatory autapses may support both bistability and monostability.

The main proposal of this paper is that boundary objects and the trading zones in which they occur are the analogue of pheromone trails in the foraging of a termite colony. The colony can be construed as a stigmergic system where the traces of the actions of individual termites coordinate their further actions without the existence of any central control or planning structures. The coordinated systems approach proposed by this paper lends support to the idea that such a system is minimally cognitive
in the sense that it is responsible for goal-directed behaviour. Boundary objects and trading zones in scientific practice play a similar functional role to pheromone traces because they are responsible for the same kind of coordination. This approach therefore provides a cognitive account of the social notions of boundary object and trading
zone without making representationalist or computationalist assumptions. Moreover, it is scale-invariant—the same analytical technique can be applied at multiple scales simultaneously. It therefore provides a framework for an understanding of the complementarity of cognitive and social approaches to scientific investigation and points to areas for further ethnographic research.

Dynamical analysis, chaos suppression and electronic implementation of the synchronous reluctance motor (SynRM) without external inputs are investigated in this paper. e different dynamical behaviors (including monostable periodic behaviors, bistable periodic behaviors, monostable chaotic behaviors, and bistable chaotic behaviors) found in the SynRM without external inputs are illustrated in the two parameters largest Lyapunov exponent (LLE) diagrams, one parameter bifurcation diagram, and phase portraits. e three single controllers are designed to suppress the chaotic behaviors found in SynRM without external inputs. e three proposed single controllers are simple and easy to implement. Numerical simulation results show that the three proposed single controllers are effective. Finally, the dynamical behaviors found in the SynRM without external inputs and the physical feasibility of the three proposed single controllers are validated through circuit implementation on OrCAD-PSpice software.

This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual info...

This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search.

This article discusses the possibility of plant decision making. We contend that recent work on bacteria provides a pertinent perspective for thinking about whether plants make choices. Specifically, the analogy between certain patterns of plant behaviour and apparent decision making in bacteria provides principled grounds for attributing decision making to the former. Though decision making is our focus, the discussion has implications for the wider issue of whether and why plants (and non-neural organisms more generally) are appropriate targets for cognitive abilities. Moreover, decision making is especially relevant to the issue of plant intelligence as it is commonly taken to be characteristic of cognition.

The 'simulation hypothesis' is an intriguing explanation for cognition, and holds that 'thinking consists of simulated interaction with the environment' ([4], p.242). However, the neuroscientific proof for a simulation mechanism in the brain is indirect. In this paper we present a minimal-model approach to investigate the 'simulation hypothesis'. Our minimal model is called ACP and is an extension of the Active Categorical Perception model (ACP) presented in [8]. In ACP , robots have a neurocontroller with an output-input feedback mechanism that allows them to simulate perception and behaviour internally. Our experiments focus on the performance of robots with three different types of neurocontroller (two feedforward and one recurrent type of neurocontroller). Their performance is compared over three experimental conditions in which the output-input feedback mechanism is functional for variable durations. The results show that feedforward-neurocontrolled robots benefit from output-input feedback, while recurrent-neurocontrolled robots do not. Based on these results, two closely related conclusions are drawn: (1) the 'simulation hypothesis' may be too specific, and (2) predicting future perception may depend on neural recurrency (i.e., internal feedback) in general, rather than on the ability to simulate perception by feeding back actions.

Dynamical analysis, chaos suppression and electronic implementation of the synchronous reluctance motor (SynRM) without external inputs are investigated in this paper. The different dynamical behaviors (including monostable periodic behaviors, bistable periodic behaviors, monostable chaotic behaviors, and bistable chaotic behaviors) found in the SynRM without external inputs are illustrated in the two parameters largest Lyapunov exponent (LLE) diagrams, one parameter bifurcation diagram, and phase portraits. The three single controllers are designed to suppress the chaotic behaviors found in SynRM without external inputs. The three proposed single controllers are simple and easy to implement. Numerical simulation results show that the three proposed single controllers are effective. Finally, the dynamical behaviors found in the SynRM without external inputs and the physical feasibility of the three proposed single controllers are validated through circuit implementation on OrCAD-PSp...

In this paper, we introduce a novel approach to the online evolution of robotic controllers. We propose accelerating and scaling online evolution to more complex tasks by giving the evolutionary process direct access to behavioural building blocks prespecified in the neural architecture as macro-neurons. During task execution, both the structure and the parameters of macro-neurons and of the entire neural network are under evolutionary control. We perform a series of simulation-based experiments in which an e-puck-like robot must learn to solve a deceptive and dynamic phototaxis task with three light sources. We show that: (i) evolution is able to progressively complexify controllers by using the behavioural building blocks as a substrate, (ii) macro-neurons, either evolved or preprogrammed, enable a significant reduction in the adaptation time and the synthesis of high performing solutions, and (iii) evolution is able to inhibit the execution of detrimental task-unrelated behaviours and adapt non-optimised macro-neurons.

This article discusses the possibility of plant decision making. We contend that recent work on bacteria provides a pertinent perspective for thinking about whether plants make choices. Specifically, the analogy between certain patterns of plant behaviour and apparent decision making in bacteria provides principled grounds for attributing decision making to the former. Though decision making is our focus, the discussion has implications for the wider issue of whether and why plants (and non-neural organisms more generally) are appropriate targets for cognitive abilities. Moreover, decision making is especially relevant to the issue of plant intelligence as it is commonly taken to be characteristic of cognition.

Generally speaking, the behavioural strategies of a multirobot system can be defined as scalable if the performance of the system does not drop by increasing the cardinality of the group. The research work presented in this paper studies the issue of scalability in artificial neural network controllers designed by evolutionary algorithms. The networks are evolved to control homogeneous group of autonomous robots required to solve a navigation task in an open arena. This work shows that, the controllers designed to solve the task, generate navigation strategies which are potentially scalable. However, through an analysis of the dynamics of the single robot controller we identify elements that significantly hinder the scalability of the system. The analysis we present in this paper helps to understand the principles underlying the concepts of scalability in this kind of multi-robot systems and to design more scalable solutions.

We aimed at exploring the plant functional traits whose stress-induced plasticity is altered by the presence of AM fungi, considering the direction of their changes. We also sought for a coordinated variation of plant biomass and functional traits, during plant adaptation to environmental stressors, and the role of AM status on the variation. We performed a meta-analysis across 114 articles spanning 110 plant species or cultivars. We quantified the size effect of AM symbiosis on the stress-induced plasticity of several reported and calculated functional traits, and using linear mixed model analysis (LMM). Correlation between traits plasticity and total biomass variation were also performed through LMM. The literature search and further selection yielded seven functional traits, extracted from 114 laboratory studies, including 888 observations and 110 plant species/cultivars. Evidence for significant effects of predictor variables (type of stress, AM symbiosis and/or their interaction) on plasticity were found for three of these functional traits: leafarea ratio (LAR), root mass fraction (RMF) and root-shoot (R:S) ratio. Our results provided evidence to accept the hypothesis that AM fungal inoculation may reduce the phenotypic plasticity of important plant functional traits leaf area ratio (LAR), root mass fraction (RMF) and root-shoot (R:S) ratio, by decreasing its magnitude. We also found a weak correlation between traits plasticity and total biomass variation. Although our literature search and data collection were intensive and our results robust, the scope of our conclusions is limited by the agronomical bias of plant species targeted by the meta-analysis. Further knowledge on non-cultivable plant species and better understanding of the mechanisms ruling resources allocation in plants would allow more generalized conclusions.

Fungal organisms can perceive the outer world in a way similar to what animals sense. Does that mean that they have full awareness of their environment and themselves? Is a fungus a conscious entity? In laboratory experiments we found that fungi produce patterns of electrical activity, similar to neurons. There are low and high frequency oscillations and convoys of spike trains. The neural-like electrical activity is yet another manifestation of the fungal intelligence. In this paper we discuss fungal cognitive capabilities and intelligence in evolutionary perspective, and question whether fungi are conscious and what does fungal consciousness mean, considering their exhibiting of complex behaviours, a wide spectrum of sensory abilities, learning, memory and decision making. We overview experimental evidences of consciousness found in fungi. Our conclusions allow us to give a positive answer to the important research questions of fungal cognition, intelligence and forms of conscious...

The study of evolutionary patterns of cognitive convergence would be greatly helped by a clear demarcation of cognition. Cognition is often used as an equivalent of mind, making it difficult to pin down empirically or to apply it confidently beyond the human condition. Recent developments in embodied cognition and philosophy of biology now suggest an interpretation that dissociates cognition from this mental context. Instead it anchors cognition in a broad range of biological cases of intelligence, provisionally marked by a basic cognitive toolkit. This conception of cognition as an empirically based phenomenon provides a suitable and greatly expanded domain for studies of evolutionary convergence. This paper first introduces this wide, biologically embodied interpretation of cognition. Second, it discusses examples drawn from studies on bacteria, plants and fungi that all provide cases fulfilling the criteria for this wide interpretation. Third, the field of early nervous system ev...

A habit is formed through the repeated enactment of sensorimotor regularities created and maintained by means of plastic changes on the mechanism that brings them about. This precarious, self-maintaining sensorimotor organization is known as sensorimotor autonomy. One can imagine how some habits would be better suited to the maintenance of a biological individual. Evolution can bias the parameters of the plastic medium over which sensorimotor autonomy emerge so as to be beneficial to biological autonomy. In this work, we show that varying some parameters that bring about plastic changes in the behavior-generating medium, different sensorimotor individuals emerge. The simulation consists of a simple robot coupled with a habit-based controller with a random-based exploratory phase in a one-dimensional environment. The results show that, varying the parameters of such a phase, qualitative different habits emerge characterized by static, monotonic and oscillatory behaviors. Quantitative variations of the oscillatory behavior are also shown using the frequencies distribution obtained from the motor time series of the formed habits. The results are interpreted in terms of how the sensorimotor habitat could emerge from the random traversing of the sensorimotor environment. Finally, a comparison between this model and the skin brain thesis is presented.

The iterant deformable sensorimotor medium (IDSM) is a controller that has been used to study habits construed as self-sustaining patters of sensorimotor activity. To understand the dynamics of this controller, we investigate a heavily simplified variation of it called a node-based sensorimotor-to-motor map (NB-SMM). This deterministic, stateless, continuous-time controller coupled to a minimalistic robot and environment demonstrates six distinct categories of behaviour, including an ability to distinguish between the two sides of a symmetric stimulus, suggesting that controllers based purely on sensorimotor-state to motor mappings may be more capable than intuition first suggests. As the number of nodes increases, the potential behavioural complexity also increases. With two nodes, cycles become possible, along with systems which produce multiple behaviours depending upon initial conditions. This hints at the potential behavioural complexity of a system with many more nodes and pro...

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis.

Van Duijn and colleagues propose that minimal cognition can be understood in terms of sensorimotor coordination – that is the coordination between sensory apparatus of an organism and the motor processes that move it around the environment (2006). Despite there being much to recommend this account it still faces some challenges. For example, it does not accord with the intuition that there are cognitive processes that have little to do with sensorimotor coupling, it relies on a strong distinction between metabolic and cognitive processes, and perhaps counterintuitively it denies cognition to plants but grants it to the bacteria on their roots that perform various functions for them. Moreover, it is difficult to see how to account for cognition over the transition from single to multicellular organisms. This paper proposes taking a more radical view of cognition to be what happens in swarms – the coordination of multiple processes through the traces of their actions in the environment. Key to this approach is the observation that the structure of the environment plays an active coordinative role and that this structure results in part from the actions of the system that it helps coordinate. Systems develop sensitivity to the appropriate trace variables in the environment. Viewed as collections of processes bacteria, biofilms, plants, animals can be viewed as cognitive systems in this framework and it has the potential to be applied to the social world.

Computational modeling is an essential approach in neuroscience for linking neural mechanisms to experimental observations. Recent advanced machine learning techniques, such as deep learning, leverage synthetic data generated from computational models to reveal underlying neural mechanisms from experimental data. However, despite significant progress, one unsolved problem in these methods is that the synthetic data differ substantially from experimental data, leading to severely biased results. To this end, we introduce the Domain Adaptive Neural Inference framework to construct synthetic data that closely resemble the distribution of experimental data and use the matching synthetic data to predict the neural mechanisms of experimental data. We demonstrate the accuracy, efficiency, and versatility of our framework in various experimental observations, including inferring single-neuron biophysics across mouse brain regions from intracellular recordings in the Allen Cell Types Databas...

The Escherichia coli is a bacterium that comfortingly lives in the human gut and one of the best known living organisms. The sensitivity of this cell to environmental changes is reflected in two kind of movements that can be observed in a swimming bacterium: "run" towards an attractant, for example food, and "tumbling", in which a new direction is chosen randomly for the next "run". This simple bimodal behavior of the E. coli constitutes in itself a paradigm of adaptation in which roboticists and cognitive psychologists have found inspiration. We present a new approach to synaptic plasticity in the nervous system by scrutinizing Escherichia coli's motility and the signaling pathways that mediate its adaptive behavior. The formidable knowledge achieved in the last decade on bacterial chemotaxis, serve as the basis for a theory of a simple form of learning called habituation, that is applicable to biological and other systems. In this paper we try to establish a new framework that helps to explain what signals mean to the organisms, how these signals are integrated in patterns of behavior, and how they are sustained by an internal model of the world. The concepts of adaptation, synaptic plasticity and learning will be revisited within a new perspective, providing a quantitative basis for the understanding of how brains cope with a changing environment.