A nonlinear dynamic artificial neural network model of memory

Nonlinearity and dynamics in psychology are found in various domains such as neuroscience, cognitive science, human development, etc. However, the models that have been proposed are mostly of a computational nature and ignore dynamics. In those models that do include dynamic properties, only fixed points are used to store and retrieve information, leaving many principles of nonlinear dynamic systems (NDS) aside; for instance, chaos is often perceived as a nuisance. This paper considers a nonlinear dynamic artificial neural network (NDANN) that implements NDS principles while also complying with general neuroscience constraints. After a theoretical presentation, simulation results will show that the model can exhibit multi-valued, fixed-point, region-constrained attractors and aperiodic (including chaotic) behaviors. Because the capabilities of NDANN include the modeling of spatiotemporal chaotic activities, it may be an efficient tool to help bridge the gap between biological memory neural models and behavioral memory models.     

A dynamical systems analysis of spontaneous movements in newborn infants

The authors evaluated the characteristics of infants' spontaneous movements by using dynamical systems analysis. Participants were 6 healthy 1-month-old full-term newborn infants (3 males, 3 females). They used a triaxial accelerometer to measure limb acceleration in 3-dimensional space. Acceleration signals were recorded during 200 s from the right wrist when the infant was in an active alert state and lying supine (sampling rate 200 Hz). and was stored in the system's memory. Digitized data were transferred to a PC for subsequent processing with analysis software. The acceleration time series data were analyzed linearly and nonlinearly. Nonlinear time series analysis suggested that the infants' spontaneous movements are characterized by a nonlinear chaotic dynamics with 5 or 6 embedding dimensions. The production of infants'spontaneous movements involves chaotic dynamic systems that are capable of generating voluntary skill movements.     

Interpersonal Pattern Dynamics and Adaptive Behavior in Multiagent Neurobiological Systems: Conceptual Model and Data

ABSTRACT

Ecological dynamics characterizes adaptive behavior as an emergent, self-organizing property of interpersonal interactions in complex social systems. The authors conceptualize and investigate constraints on dynamics of decisions and actions in the multiagent system of team sports. They studied coadaptive interpersonal dynamics in rugby union to model potential control parameter and collective variable relations in attacker–defender dyads. A videogrammetry analysis revealed how some agents generated fluctuations by adapting displacement velocity to create phase transitions and destabilize dyadic subsystems near the try line. Agent interpersonal dynamics exhibited characteristics of chaotic attractors and informational constraints of rugby union boxed dyadic systems into a low dimensional attractor. Data suggests that decisions and actions of agents in sports teams may be characterized as emergent, self-organizing properties, governed by laws of dynamical systems at the ecological scale. Further research needs to generalize this conceptual model of adaptive behavior in performance to other multiagent populations.     

Equilibria in Personality States: A Conceptual Primer for Dynamics in Personality States

We provide a basic, step-by-step introduction to the core concepts and mathematical fundamentals of dynamic systems modelling through applying the Change as Outcome model, a simple dynamical systems model, to personality state data. This model characterizes changes in personality states with respect to equilibrium points, estimating attractors and their strength in time series data. Using data from the Personality and Interpersonal Roles study, we find that mean state is highly correlated with attractor position but weakly correlated with attractor strength, suggesting strength provides added information not captured by summaries of the distribution. We then discuss how taking a dynamic systems approach to personality states also entails a theoretical shift. Instead of emphasizing partitioning trait and state variance, dynamic systems analyses of personality states emphasize characterizing patterns generated by mutual, ongoing interactions. Change as Outcome modelling also allows for estimating nuanced effects of personality development after significant life changes, separating effects on characteristic states after the significant change and how strongly she or he is drawn towards those states (an aspect of resiliency). Estimating this model demonstrates core dynamics principles and provides quantitative grounding for measures of ‘repulsive’ personality states and ‘ambivert’ personality structures. © 2020 European Association of Personality Psychology     

Psychodynamic Formulation in the Age of Neuroscience: A Dynamical Systems Model

The dichotomy between biological and psychosocial psychiatry has stood as a deep divide in psychiatric treatment and training since the advent of psychoanalysis. It is now complicated by the proliferation of diverse theoretical perspectives, both psychotherapeutic and neurochemical. Our deepening understanding of neural network dynamics can provide empirical constraints in validating psychotherapeutic approaches, while the appreciation of the patient–therapist intersubjective matrix can inform biological treatment. The Cartesian mind–body fallacy can now be analyzed as a unified complex dynamical system in the same light as Einstein’s integration of the seemingly divergent concepts of space and time into a unified fabric of spacetime.

Dynamical systems approach to neural network functioning offers the most comprehensive foundation for psychotherapy available to us today. Recurrent patterns of thinking, feeling, and relating can be analyzed by modeling the dynamical landscape of cortical and subcortical network processes. Dynamical Systems Therapy (DST) stands as a trans-theoretical model with the explanatory power to integrate systems of synaptic networks with systems of meaning. It powerfully argues for shifting the clinical emphasis from our patient’s symptomatic presentation as the focus of clinical attention to conceptualizing psychopathology as fixed patterns of adaptive attractors in response to the dysfunctional developmental environment. Patients come to be seen as active agents who create the meaning of their experiences based on their unique implicit templates. In this view, DST-informed psychodynamic formulation helps us chart the patient’s dysfunctional attractor basins, and therapeutic relationship becomes our tool in reshaping the dynamical landscape topology to reestablish self-organizing process.     

Coordination of multiple memory systems

On the basis of lesions of different brain areas, several neural systems appear to be important for processing information regarding different types of learning and memory. This paper examines the development of pharmacological and neurochemical approaches to multiple memory systems from past studies of modulation of memory formation. The findings suggest that peripheral neuroendocrine mechanisms that regulate memory processing may target their actions toward those neural systems most engaged in the processing of learning and memory. In addition, measurements of acetylcholine release in different memory systems reveals extensive interactions between memory systems, some cooperative and some competitive. These results imply that many neural systems, often characterized as relatively independent, may in fact interact extensively, blurring the dependencies of different memory tasks on specific neural systems.     

Grand Unified Theories of the Brain Need Better Understanding of Behavior: The Two-Tiered Emergence of Function

Abstract

Over the last few decades, neuroscience and various associated disciples have expanded enormously in terms of output, tools, methods, concepts, and large-scale projects. In spite of these developments, the principles underlying brain function and behavior are yet only partially understood. We claim that brain functioning requires the elucidation of the rules associated with all possible task realizations, rather than targeting the activity underlying a specific realization. A first step in that direction was taken by approaches focusing on dynamical structures underlying task performances, as exemplified by coordination dynamics. Its theoretical foundation owes much to Haken’s synergetics, which provides a formalism through which the degrees of freedom associated with high-dimensional systems may be effectively reduced to one or a few functional variables in the vicinity of phase transitions. The recent theoretical development of structured flows on manifolds (SFM) allows the employment to a potentially broader range of applications. Here we expand the SFM framework and propose that the emergent two-tiered fast–slow dynamics may be a basic mathematical organization underlying the architecture of brain and behavior dynamics. Finally, along a few examples, we illustrate how this framework allows for the incorporation of notions cardinal to ecological psychology.     

振动触觉频率信息的工作记忆容量及存储机制

工作记忆可以同时保存多个信息并且容量有限, 这一内在机制是工作记忆研究的重点问题。视觉和言语等研究领域都发现工作记忆能够存储多个信息单元, 但对振动触觉工作记忆是否能存储多个频率信息目前尚无相关研究。由于振动触觉频率刺激和视觉刺激具有不同的神经编码机制, 以及振动频率信息是通过躯体感觉产生的模拟的、单维的、参数化信息, 振动触觉工作记忆容量及其加工存储机制的研究也必不可少。首先, 本项目将采用新的实验范式, 探究不同的刺激呈现方式以及不同反应报告方式下, 振动触觉工作记忆的容量及其认知机制。其次, 本项目也将同时运用功能磁共振成像(fMRI)技术, 来阐述振动触觉工作记忆加工存储的神经机制。探究基于触觉频率信息的参数工作记忆容量及其神经机制是完善工作记忆模型的重要补充, 将有助于提高我们对工作记忆系统的理解, 并为视觉、听觉、触觉多模态感知觉信息的跨通道研究奠定基础。     

Précis of Neural organization: structure, function, and dynamics

NEURAL ORGANIZATION: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) STRUCTURE: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) FUNCTION: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) DYNAMICS: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.     

Working memory retention systems: a state of activated long-term memory

High temporal resolution event-related brain potential and electroencephalographic coherence studies of the neural substrate of short-term storage in working memory indicate that the sustained coactivation of both prefrontal cortex and the posterior cortical systems that participate in the initial perception and comprehension of the retained information are involved in its storage. These studies further show that short-term storage mechanisms involve an increase in neural synchrony between prefrontal cortex and posterior cortex and the enhanced activation of long-term memory representations of material held in short-term memory. This activation begins during the encoding/comprehension phase and evidently is prolonged into the retention phase by attentional drive from prefrontal cortex control systems. A parsimonious interpretation of these findings is that the long-term memory systems associated with the posterior cortical processors provide the necessary representational basis for working memory, with the property of short-term memory decay being primarily due to the posterior system. In this view, there is no reason to posit specialized neural systems whose functions are limited to those of short-term storage buffers. Prefrontal cortex provides the attentional pointer system for maintaining activation in the appropriate posterior processing systems. Short-term memory capacity and phenomena such as displacement of information in short-term memory are determined by limitations on the number of pointers that can be sustained by the prefrontal control systems.     

Cortical coordination dynamics and cognition

New imaging techniques in cognitive neuroscience have produced a deluge of information correlating cognitive and neural phenomena. Yet our understanding of the inter-relationship between brain and mind remains hampered by the lack of a theoretical language for expressing cognitive functions in neural terms. We propose an approach to understanding operational laws in cognition based on principles of coordination dynamics that are derived from a simple and experimentally verified theoretical model. When applied to the dynamical properties of cortical areas and their coordination, these principles support a mechanism of adaptive inter-area pattern constraint that we postulate underlies cognitive operations generally.     

Social dynamics in the preschool

In this paper, we consider how concepts from dynamic systems (such as attractors, repellors, and self-organization) can be applied to the study of young children’s peer relationships. We also consider how these concepts can be used to explore basic issues involving early peer processes. We use the dynamical systems approach called state space grid (SSG) analysis and consider how it can be expanded beyond the study of dyads to the study of larger social groups and networks. In particular, we explore the role of homophily—that is, behavioral and sex similarity—as factors in the self-organization of young children’s social groups. A dynamic systems approach allows for consideration of peer processes difficult to assess using more traditional approaches.     

The dynamics of perception and action

How might one account for the organization in behavior without attributing it to an internal control structure? The present article develops a theoretical framework called behavioral dynamics that integrates an information-based approach to perception with a dynamical systems approach to action. For a given task, the agent and its environment are treated as a pair of dynamical systems that are coupled mechanically and informationally. Their interactions give rise to the behavioral dynamics, a vector field with attractors that correspond to stable task solutions, repellers that correspond to avoided states, and bifurcations that correspond to behavioral transitions. The framework is used to develop theories of several tasks in which a human agent interacts with the physical environment, including bouncing a ball on a racquet, balancing an object, braking a vehicle, and guiding locomotion. Stable, adaptive behavior emerges from the dynamics of the interaction between a structured environment and an agent with simple control laws, under physical and informational constraints.     

Understanding social motor coordination

Recently there has been much interest in social coordination of motor movements, or as it is referred to by some researchers, joint action. This paper reviews the cognitive perspective’s common coding/mirror neuron theory of joint action, describes some of its limitations and then presents the behavioral dynamics perspective as an alternative way of understanding social motor coordination. In particular, behavioral dynamics’ ability to explain the temporal coordination of interacting individuals is detailed. Two experiments are then described that demonstrate how dynamical processes of synchronization are apparent in the coordination underlying everyday joint actions such as martial art exercises, hand-clapping games, and conversations. The import of this evidence is that emergent dynamic patterns such as synchronization are the behavioral order that any neural substrate supporting joint action (e.g., mirror systems) would have to sustain.     

Dynamics of human postural transitions

In the present study, the authors examined transitions between postural coordination modes involved in human stance. The analysis was motivated by dynamical theories of pattern formation, in which coordination modes and transitions between modes are emergent, self-organized properties of the dynamics of animal-environment systems. In 2 experiments, standing participants tracked a moving target with the head. Results are consistent with the hypothesis that changes in body coordination follow typical nonequilibrium phase transitions, exhibiting multistability, bifurcation, critical fluctuations, hysteresis, and critical slowing down. The findings suggest that posture may be organized in terms of dynamical principles and favor the existence of general and common principles governing pattern formation and flexibility in complex systems.     

Perspective-taking in dialogue as self-organization under social constraints

We present a dynamical systems account of how simple social information influences perspective-taking. Our account is motivated by the notion that perspective-taking may obey common dynamic principles with perceptuomotor coupling. We turn to the prominent HKB dynamical model of motor coordination, drawing from basic principles of self-organization to describe how conversational perspective-taking unfolds in a low-dimensional attractor landscape. We begin by simulating experimental data taken from a simple instruction-following task, in which participants have different expectations about their interaction partner. By treating belief states as different values of a control parameter, we show that data generated by a basic dynamical process fits overall egocentric and other-centric response distributions, the time required for participants to enact a response on a trial-by-trial basis, and the action dynamics exhibited in individual trials. We end by discussing the theoretical significance of dynamics in dialog, arguing that high-level coordination such as perspective-taking may obey similar dynamics as perceptuomotor coordination, pointing to common principles of adaptivity and flexibility during dialog.     

Moving to higher ground: The dynamic field theory and the dynamics of visual cognition

In the present report, we describe a new dynamic field theory that captures the dynamics of visuo-spatial cognition. This theory grew out of the dynamic systems approach to motor control and development, and is grounded in neural principles. The initial application of dynamic field theory to issues in visuo-spatial cognition extended concepts of the motor approach to decision making in a sensori-motor context, and, more recently, to the dynamics of spatial cognition. Here we extend these concepts still further to address topics in visual cognition, including visual working memory for non-spatial object properties, the processes that underlie change detection, and the ‘binding problem’ in vision. In each case, we demonstrate that the general principles of the dynamic field approach can unify findings in the literature and generate novel predictions. We contend that the application of these concepts to visual cognition avoids the pitfalls of reductionist approaches in cognitive science, and points toward a formal integration of brains, bodies, and behavior.