Biology subjects

Buckley, C. L.

Publications and source records attributed to Buckley, C. L..

7 recordsLinked to original sources

bioRxiv2024

Natural Induction: Spontaneous adaptive organisation without natural selection

Evolution by natural selection is believed to be the only possible source of spontaneous adaptive organisation in the natural world. This places strict limits on the kinds of systems that can exhibit adaptation spontaneously, i.e. without design. Physical systems can show some properties relevant to adaptation without natural selection or design. 1) The relaxation, or local energy minimisation, of a physical system constitutes a natural form of optimisation insomuch as it finds locally optimal solutions to the frustrated forces acting on it or between its components. 2) When internal structure gives way or accommodates to a pattern of forcing on a system this constitutes learning insomuch as it can store, recall and generalise past configurations. Both these effects are quite natural and general, but in themselves insufficient to constitute non-trivial adaptation. However, here we show that the recurrent interaction of physical optimisation and physical learning together results in significant spontaneous adaptive organisation. We call this adaptation by natural induction. The effect occurs in dynamical systems described by a network of viscoelastic connections subject to occasional disturbances. When the internal structure of such a system accommodates slowly across many disturbances and relaxations, it spontaneously learns to preferentially visit solutions of increasingly greater quality (exceptionally low energy). We show that adaptation by natural induction thus produces network organisations that improve problem-solving competency with experience. We note that the conditions for adaptation by natural induction, and its adaptive competency, are different from those of natural selection. We therefore suggest that natural selection is not the only possible source of spontaneous adaptive organisation in the natural world.

evolutionary biology↗

bioRxiv2024

An ability to respond begins with inner alignment: How phase synchronisation effects transitions to higher levels of agency

How do multiple active components at one level of organisation create agential wholes at higher levels of organisation? For example, in organismic development, how does the multi-scale autonomy of the organism arise from the interactions of the molecules, cells and tissues that an organism contains? And, in the major evolutionary transitions, how does a multicellular organism, for example, arise as an evolutionary unit from the selective interests of its unicellular ancestors? We utilise computational models as a way to think about this general question. We take a deliberately minimalistic notion of an agent: a competency to take one of two possible actions to minimise stress. Helping ourselves to this behaviour at the microscale, we focus on conditions where this same type of agency appears spontaneously at a higher level of organisation. We find that a simple process of positive feedback on the timing of individual responses, loosely analogous to the natural phase synchronisation of weakly coupled oscillators, causes such a transition in behaviour. The emergent collectives that arise become, quite suddenly, able to respond to their external stresses in the same (minimal) sense as the original microscale units. This effects a dramatic rescaling of the system behaviour, and a quantifiable increase in problem-solving competency, serving as a model of how higher-level agency emerges from a pool of lower-level agents or active matter. We discuss how this dynamical waking-up of higher-level collectives, through the alignment of their internal dynamics, might relate to reproductive/cell-cycle synchronisation in evolutionary transitions and development.

evolutionary biology↗

bioRxiv2022

Control algorithms underlying the translational optomotor response

The optomotor response (OMR) is central to the locomotory behavior in diverse animal species including insects, fish and mammals. Furthermore, the study of the OMR in larval zebrafish has become a key model system for investigating the neural basis of sensorimotor control. However, a comprehensive understanding of the underlying control algorithms is still outstanding. In fish it is often assumed that the OMR, by reducing average optic flow across the retina, serves to stabilize position with respect to the ground. Yet the degree to which this assumption is true, and how this could emerge from the intermittent burst dynamics of larval zebrafish swimming is unclear. Here, we combine detailed computational modeling with a new approach to free-swimming experiments, in which feedback gain - i.e., the degree of sensory feedback produced by swimming - is manipulated by varying the height of larval zebrafish above a moving stimulus. We develop an account of underlying feedback control mechanisms that describes not only bout initiation but also the control of swim speed during bouts. We observe that the degree to which fish stabilize their position is only partial and gain-dependent, suggesting that the OMR may not primarily function in fish to prevent drift. We find the speed profile during bouts follows a fixed temporal pattern independent of bout intensity, suggesting that bout termination and bout duration are not explicitly controlled. We also find that the reverse optic flow, experienced when the fish is swimming faster than the stimulus, plays a minimal role in control of the OMR despite carrying most of the sensory information about self-movement. These results shed new light on the underlying dynamics of the OMR in larval zebrafish and will be crucial for future work aimed at identifying the neural basis of this behavior. Author SummaryIn many animals vision is central to the control of locomotory behaviors. In particular, innate motor responses to optic flow allow flying animals to react to gusts of air and fish to changes of current. As fish are washed downstream, for example, movement of the riverbed image across the retina evokes forward movement in bouts (short periods of swimming followed by rest periods). It is typically assumed that this translational optomotor response stabilizes the fishs position to prevent it drifting downstream. In larval zebrafish, this response has become a key model system for investigating the neural basis of sensorimotor behaviors in a vertebrate. Here, we combine behavioral experiments and computational modeling to elucidate the underlying control algorithms. We unpick the detailed relationship between visual stimuli and the initiation, termination and intensity of swim bouts. We find initiation and intensity are controlled separately, and that termination is not explicitly controlled. Surprisingly we also found that the degree of stabilization is only partial and varies systematically with height above ground, raising questions of the function of this response. These findings shed new light on the underlying dynamics which will be crucial for future work to identify the neural basis of this behavior.

animal behavior and cognition↗

bioRxiv2021

Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using Active Inference

The adaptive regulation of bodily and interoceptive parameters, such as body temperature, thirst and hunger is a central problem for any biological organism. Here, we present a series of simulations using the framework of Active Inference to formally characterize interoceptive control and some of its dysfunctions. We start from the premise that the goal of interoceptive control is to minimize a discrepancy between expected and actual interoceptive sensations (i.e., a prediction error or free energy). Importantly, living organisms can achieve this goal by using various forms of interoceptive control: homeostatic, allostatic and goal-directed. We provide a computationally-guided analysis of these different forms of interoceptive control, by showing that they correspond to distinct generative models within Active Inference. Furthermore, we illustrate how these generative models may support empirical research, by predicting physiological and brain signals that may accompany both adaptive and maladaptive interoceptive control. HighlightsO_LIWe use Active Inference to provide formal models of interoceptive control C_LIO_LIWe model homeostatic, allostatic and goal-directed forms of interoceptive control C_LIO_LIOur simulations illustrate both adaptive interoceptive control and its dysfunctions C_LIO_LIWe discuss how the models can aid empirical research on interoception C_LI

neuroscience↗

bioRxiv2020

μSPIM: A Software Platform for Selective Plane Illumination Microscopy

Selective Plane Illumination Microscopy (SPIM) is a fluorescence imaging technique that allows volumetric imaging at high spatio-temporal resolution to monitor neural activity in live organisms such as larval zebrafish. A major challenge in the construction of a custom SPIM microscope is the control and synchronization of the various hardware components. Here we present a control toolset, SPIM, built around the open-source MicroManager platform that has already been widely adopted for the control of microscopy hardware. Installation of SPIM is relatively straightforward, involving a single C++ executable and a Java-based extension to Micro-Manager. Imaging protocols are defined through the SPIM extension to Micro-Manager. The extension then synchronizes the camera shutter with the galvanometer mirrors to create a light-sheet that is scanned in the z-dimension, in synchrony with the imaging objective, to produce volumetric recordings. A key advantage of SPIM is that a series of calibration procedures optimizes acquisition for a given set-up making it relatively independent of the optical design of the microscope, or the hardware used to build it. Two laser illumination arms can be used while also allowing for the introduction of illumination masks. SPIM allows imaging of calcium activity throughout the brain of larval zebrafish at rates of 100 planes per second with single cell resolution as well as slower imaging to reconstruct cell populations, for example, in the cleared brains of mice.

neuroscience↗

bioRxiv2019

Learning action-oriented models through active inference

AO_SCPLOWBSTRACTC_SCPLOWConverging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

animal behavior and cognition↗

bioRxiv2018

A probabilistic interpretation of PID controllers using active inference

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition within general mathematical frameworks derived from information theory, statistical physics and machine learning. Furthermore, it has been argued that one such proposal, active inference, combines both information and control theory and has its roots in cybernetics studies of the brain. The connections between information and control theory have been discussed since the 1950s by scientists like Shannon and Kalman and have recently risen to prominence in modern stochastic optimal control theory. How-ever, the implications of the confluence of these two theoretical frame-works for the biological sciences have been slow to emerge. Here we argue that if the active inference proposal is to be taken as a general process theory for biological systems, we need to consider how existing control theoretical approaches to biological systems relate to it. In this work we will focus on PID (Proportional-Integral-Derivative) controllers, one of the most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation once we use only simple linear generative models.

systems biology↗