Neural dynamic foundations of a theory of higher cognition: the case of grounding nested phrases.

Because cognitive competences emerge in evolution and development from the sensory-motor domain, we seek a neural process account for higher cognition in which all representations are necessarily grounded in perception and action. The challenge is to understand how hallmarks of higher cognition, productivity, systematicity, and compositionality, may emerge from such a bottom-up approach. To address this challenge, we present key ideas from Dynamic Field Theory which postulates that neural populations are organized by recurrent connectivity to create stable localist representations. Dynamic instabilities enable the autonomous generation of sequences of mental states. The capacity to apply neural circuitry across broad sets of inputs that emulates the function call postulated in symbolic computation emerges through coordinate transforms implemented in neural gain fields. We show how binding localist neural representations through a shared index dimension enables conceptual structure, in which the interdependence among components of a representation is flexibly expressed. We demonstrate these principles in a neural dynamic architecture that represents and perceptually grounds nested relational and action phrases. Sequences of neural processing steps are generated autonomously to attentionally select the referenced objects and events in a manner that is sensitive to their interdependencies. This solves the problem of 2 and the massive binding problem in expressions such as "the small tree that is to the left of the lake which is to the left of the large tree". We extend earlier work by incorporating new types of grammatical constructions and a larger vocabulary. We discuss the DFT framework relative to other neural process accounts of higher cognition and assess the scope and challenges of such neural theories.

Inverting a model of neuro-muscular control to estimate descending activation patterns that generate fast reaching movements.

Reaching movements generally show smooth kinematic profiles that are invariant across varying movement speeds even as interaction torques and muscle properties vary non-linearly with speed. How the brain brings about these invariant profiles is an open question.We develop an analytical inverse dynamics method to estimate descending activation patterns directly from observed joint angle trajectories based on a simple model of the stretch reflex, and of muscle and biomechanical dynamics. We estimate descending activation patterns for experimental data from eight different planar two-joint movements performed at two movement times (fast: 400 msec; slow: 800 msec). The temporal structure of descending activation differed qualitatively across speeds, consistent with the idea that the nervous system uses an internal model to generate anticipatory torques during fast movement. This temporal structure also depended on the co-contraction level of antagonistic muscle groups. Comparing estimated muscle activation and descending activation revealed the contribution of the stretch reflex to movement generation that was found to set in after about 20 percent of movement time.

The stabilization of visibility for sequentially presented, low-contrast objects: Experiments and neural field model.

In any environment, events transpire in temporal sequences. The general principle governing such sequences is that each instance of the event is influenced by its predecessors. It is shown here that this principle is true for a fundamental aspect of visual perception: visibility. A series of nine psychophysical experiments and associated neural dynamic simulations provide evidence that two non-stimulus factors, self-excitation and short-term memory, stabilize the visibility of a simple low-contrast object (a line segment) as it moves over a sequence of unpredictable locations. Stabilization was indicated by the very low probability of visible-to-invisible switches, and dependence on preceding visibility states was indicated by hysteresis as the contrast of the object was gradually decreased or increased. The contribution of self-excitation to stabilization was indicated by increased visible-to-invisible switching (decreased hysteresis) following adaptation of the visibility state, and the contribution of memory to stabilization was indicated by visibility "bridging" long blank intervals separating each relocation of the object. Because of the unpredictability of the relocations of the object, its visibility at one location pre-shapes visibility at its next location via persisting subthreshold activation of detectors surrounding the low-contrast object. All effects were modeled, including contributions from adaptation and recurrent inhibition, with a single set of parameter values.

A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases.

We present a neural dynamic model that perceptually grounds nested noun phrases, that is, noun phrases that contain further (possibly also nested) noun phrases as parts. The model receives input from the visual array and a representation of a noun phrase from language processing. It organizes a search for the denoted object in the visual scene. The model is a neural dynamic architecture of interacting neural populations which has clear interfaces with perceptual processes. It solves a set of theoretical challenges, including the problem of keeping a nested structure in short-term memory in a way that solves the problem of 2 and massive binding problem emphasized by Jackendoff. The model organizes a search for the objects that are referenced in that structure. We motivate the model, demonstrate simulation results, and discuss how it differs from related models.

Estimating the time structure of descending activation that generates movements at different speeds.

In targeted movements of the hand, descending activation patterns must not only generate muscle activation but also adjust spinal reflexes from stabilizing the initial to stabilizing the final postural state. We estimate descending activation patterns that change minimally while generating a targeted movement within a given movement time based on a model of the biomechanics, the muscle dynamics, and the stretch reflex. The estimated descending activation patterns predict human movement trajectories quite well. Their temporal structure varies across workspace and with movement speed, from monotonic profiles for slow movements to nonmonotonic profiles for fast movements. Descending activation patterns at different speeds thus do not result from a mere rescaling of invariant templates but reflect varying needs to compensate for interaction torques and muscle dynamics. The virtual attractor trajectories, on which active muscle torques are zero, lie within reachable workspace and are largely invariant when represented in end-effector coordinates. Their temporal structure along movement direction changes from linear ramps to "N-shaped" profiles with increasing movement speed.NEW & NOTEWORTHY The descending activation patterns driving movement must be integrated with spinal reflexes, which would resist movement if left unchanged. We estimate the descending activation patterns at different movement speeds using a model of the stretch reflex and of muscle and limb dynamics. The descending activation patterns we find are temporally structured to compensate for interaction torques as predicted by internal models but also shift the reflex threshold, solving the posture-movement problem.

A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements.

Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles.

Habituation and Dishabituation in Motor Behavior: Experiment and Neural Dynamic Model.

Does motor behavior early in development have the same signatures of habituation, dishabituation, and Spencer-Thompson dishabituation known from infant perception and cognition? And do these signatures explain the choice preferences in A not B motor decision tasks? We provide new empirical evidence that gives an affirmative answer to the first question together with a unified neural dynamic model that gives an affirmative answer to the second question.In the perceptual and cognitive domains, habituation is the weakening of an orientation response to a stimulus over perceptual experience. Switching to a novel stimulus leads to dishabituation, the re-establishment of the orientation response. In Spencer-Thompson dishabituation, the renewed orientation response transfers to the original (familiar) stimulus. The change in orientation responses over perceptual experience explains infants' behavior in preferential looking tasks: Familiarity preference (looking longer at familiar than at novel stimuli) early during exposure and novelty preference (looking longer at novel than at familiar stimuli) late during exposure. In the motor domain, perseveration in the A not B task could be interpreted as a form of familiarity preference. There are hints that this preference reverses after enough experience with the familiar movement. We provide a unified account for habituation and patterns of preferential selection in which neural dynamic fields generate perceptual or motor representations. The build-up of activation in excitatory fields leads to familiarity preference, the build-up of activation in inhibitory fields leads to novelty preference. We show that the model accounts for the new experimental evidence for motor habituation, but is also compatible with earlier accounts for perceptual habituation and motor perseveration. We discuss how excitatory and inhibitory memory traces may regulate exploration and exploitation for both orientation to objects and motor behaviors.

A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations.

How does the human brain link relational concepts to perceptual experience? For example, a speaker may say "the cup to the left of the computer" to direct the listener's attention to one of two cups on a desk. We provide a neural dynamic account for both perceptual grounding, in which relational concepts enable the attentional selection of objects in the visual array, and for the generation of descriptions of the visual array using relational concepts. In the model, activation in neural populations evolves dynamically under the influence of both inputs and strong interaction as formalized in dynamic field theory. Relational concepts are modeled as patterns of connectivity to perceptual representations. These generalize across the visual array through active coordinate transforms that center the representation of target objects in potential reference objects. How the model perceptually grounds or generates relational descriptions is probed in 104 simulations that systematically vary the spatial and movement relations employed, the number of feature dimensions used, and the number of matching and nonmatching objects. We explain how sequences of decisions emerge from the time- and state-continuous neural dynamics, how relational hypotheses are generated and either accepted or rejected, followed by the selection of new objects or the generation of new relational hypotheses. Its neural realism distinguishes the model from information processing accounts, its capacity to autonomously generate sequences of processing steps distinguishes it from deep neural network accounts. The model points toward a neural dynamic theory of higher cognition.

How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory.

There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Scene memory and spatial inhibition in visual search : A neural dynamic process model and new experimental evidence.

Any object-oriented action requires that the object be first brought into the attentional foreground, often through visual search. Outside the laboratory, this would always take place in the presence of a scene representation acquired from ongoing visual exploration. The interaction of scene memory with visual search is still not completely understood. Feature integration theory (FIT) has shaped both research on visual search, emphasizing the scaling of search times with set size when searches entail feature conjunctions, and research on visual working memory through the change detection paradigm. Despite its neural motivation, there is no consistently neural process account of FIT in both its dimensions. We propose such an account that integrates (1) visual exploration and the building of scene memory, (2) the attentional detection of visual transients and the extraction of search cues, and (3) visual search itself. The model uses dynamic field theory in which networks of neural dynamic populations supporting stable activation states are coupled to generate sequences of processing steps. The neural architecture accounts for basic findings in visual search and proposes a concrete mechanism for the integration of working memory into the search process. In a behavioral experiment, we address the long-standing question of whether both the overall speed and the efficiency of visual search can be improved by scene memory. We find both effects and provide model fits of the behavioral results. In a second experiment, we show that the increase in efficiency is fragile, and trace that fragility to the resetting of spatial working memory.

The Dynamics of Neural Populations Capture the Laws of the Mind.

What would it mean to explain the mind in neural terms? Neural accounts of the mind are often sought in a reductionistic spirit in which neural mechanisms explain cognition. Because an individual's thoughts and behaviors are not reproducible without careful control of task, stimulus, and behavioral history, laws of the mind are the currency of psychology. Reduction may thus have to take the form familiar from physics: deriving macroscopic laws from microscopic laws. I argue that the metaphor of reduction from non-equilibrium physics may be the most appropriate. Macroscopic patterns of neural activity, which cause behavior and thought, are slow dynamical variables that dominate the fast microscopic dynamics of individual neurons and synapses. I outline a theoretical framework in which strongly recurrent neural networks, described by neural dynamics, generate neural representations as attractor states that are embedded in low-dimensional feature spaces. Instabilities of these states are instrumental in decision-making and the generation of sequences of mental states that are the basis for higher cognition. Networks of such neural population dynamics form neural cognitive architectures that capture the laws of the mind.

Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement.

Neurally inspired robotics already has a long history that includes reactive systems emulating reflexes, neural oscillators to generate movement patterns, and neural networks as trainable filters for high-dimensional sensory information. Neural inspiration has been less successful at the level of cognition. Decision-making, planning, building and using memories, for instance, are more often addressed in terms of computational algorithms than through neural process models. To move neural process models beyond reactive behavior toward cognition, the capacity to autonomously generate sequences of processing steps is critical. We review a potential solution to this problem that is based on strongly recurrent neural networks described as neural dynamic systems. Their stable states perform elementary motor or cognitive functions while coupled to sensory inputs. The state of the neural dynamics transitions to a new motor or cognitive function when a previously stable neural state becomes unstable. Only when a neural robotic system is capable of acting autonomously does it become a useful to a human user. We demonstrate how a neural dynamic architecture that supports autonomous sequence generation can engage in such interaction. A human user presents colored objects to the robot in a particular order, thus defining a serial order of color concepts. The user then exposes the system to a visual scene that contains the colored objects in a new spatial arrangement. The robot autonomously builds a scene representation by sequentially bringing objects into the attentional foreground. Scene memory updates if the scene changes. The robot performs visual search and then reaches for the objects in the instructed serial order. In doing so, the robot generalizes across time and space, is capable of waiting when an element is missing, and updates its action plans online when the scene changes. The entire flow of behavior emerges from a time-continuous neural dynamics without any controlling or supervisory algorithm.

Computer mouse tracking reveals motor signatures in a cognitive task of spatial language grounding.

In a novel computer mouse tracking paradigm, participants read a spatial phrase such as "The blue item to the left of the red one" and then see a scene composed of 12 visual items. The task is to move the mouse cursor to the target item (here, blue), which requires perceptually grounding the spatial phrase. This entails visually identifying the reference item (here, red) and other relevant items through attentional selection. Response trajectories are attracted toward distractors that share the target color but match the spatial relation less well. Trajectories are also attracted toward items that share the reference color. A competing pair of items that match the specified colors but are in the inverse spatial relation increases attraction over-additively compared to individual items. Trajectories are also influenced by the spatial term itself. While the distractor effect resembles deviation toward potential targets in previous studies, the reference effect suggests that the relevance of the reference item for the relational task, not its role as a potential target, was critical. This account is supported by the strengthened effect of a competing pair. We conclude, therefore, that the attraction effects in the mouse trajectories reflect the neural processes that operate on sensorimotor representations to solve the relational task. The paradigm thus provides an experimental window through motor behavior into higher cognitive function and the evolution of activation in modal substrates, a longstanding topic in the area of embodied cognition.

A process account of the uncontrolled manifold structure of joint space variance in pointing movements.

In many situations, the human movement system has more degrees of freedom than needed to achieve a given movement task. Martin et al. (Neural Comput 21(5):1371-1414, 2009) accounted for signatures of such redundancy like self-motion and motor equivalence in a process model in which a neural oscillator generated timed end-effector virtual trajectories that a neural dynamics transformed into joint virtual trajectories while decoupling task-relevant and task-irrelevant combinations of joint angles. Neural control of muscle activation and the biomechanical dynamics of the arm were taken into account. The model did not address the main signature of redundancy, however, the UCM structure of variance: Many experimental studies have shown that across repetitions, variance of joint configuration trajectories is structured. Combinations of joint angles that affect task variables (lying in the uncontrolled manifold, UCM) are much more variable than combinations of joint angles that do not. This finding has been robust across movement systems, age, and tasks and is often preserved in clinical populations as well. Here, we provide an account for the UCM structure of variance by adding four types of noise sources to the model of Martin et al. (Neural Comput 21(5):1371-1414, 2009). Comparing the model to human pointing movements and systematically examining the role of each noise source and mechanism, we identify three causes of the UCM effect, all of which, we argue, contribute: (1) the decoupling of motor commands across the task-relevant and task-irrelevant subspaces together with "neural" noise at the level of these motor commands; (2) "muscle noise" combined with imperfect control of the limb; (3) back-coupling of sensed joint configurations into the motor commands which then yield to the sensed joint configuration within the UCM.

Anticipatory coarticulation in non-speeded arm movements can be motor-equivalent, carry-over coarticulation always is.

In a sequence of arm movements, any given segment could be influenced by its predecessors (carry-over coarticulation) and by its successor (anticipatory coarticulation). To study the interdependence of movement segments, we asked participants to move an object from an initial position to a first and then on to a second target location. The task involved ten joint angles controlling the three-dimensional spatial path of the object and hand. We applied the principle of the uncontrolled manifold (UCM) to analyze the difference between joint trajectories that either affect (non-motor equivalent) or do not affect (motor equivalent) the hand's trajectory in space. We found evidence for anticipatory coarticulation that was distributed equally in the two directions in joint space. We also found strong carry-over coarticulation, which showed clear structure in joint space: More of the difference between joint configurations observed for different preceding movements lies in directions in joint space that leaves the hand's path in space invariant than in orthogonal directions in joint space that varies the hand's path in space. We argue that the findings are consistent with anticipatory coarticulation reflecting processes of movement planning that lie at the level of the hand's trajectory in space. Carry-over coarticulation may reflect primarily processes of motor control that are governed by the principle of the UCM, according to which changes that do not affect the hand's trajectory in space are not actively delimited. Two follow-up experiments zoomed in on anticipatory coarticulation. These experiments strengthened evidence for anticipatory coarticulation. Anticipatory coarticulation was motor-equivalent when visual information supported the steering of the object to its first target, but was not motor equivalent when that information was removed. The experiments showed that visual updating of the hand's path in space when the object approaches the first target only affected the component of the joint difference vector orthogonal to the UCM, consistent with the UCM principle.

A multi-joint model of quiet, upright stance accounts for the "uncontrolled manifold" structure of joint variance.

The upright body in quiet stance is usually modeled as a single-link inverted pendulum. This agrees with most of the relevant sensory organs being at the far end of the pendulum, i.e., the eyes and the vestibular system in the head. Movement of the body in quiet stance has often been explained in terms of the "ankle strategy," where most movement is generated by the ankle musculature, while more proximal muscle groups are only rarely activated for faster movements or in response to perturbations, for instance, by flexing at the hips in what has been called the "hip strategy." Recent empirical evidence, however, shows that instead of being negligible in quiet stance, the movement in the knee and hip joints is even larger on average than the movement in the ankle joints (J Neurophysiol 97:3024-3035, 2007). Moreover, there is a strong pattern of covariation between movements in the ankle, knee and hip joints in a way that most of the observed movements leave the anterior-posterior position of the whole-body center of mass (CoM) invariant, i.e., only change the configuration of the different body parts around the CoM, instead of moving the body as a whole. It is unknown, however, where this covariation between joint angles during quiet stance originates from. In this paper, we aim to answer this question using a comprehensive model of the biomechanical, muscular and neural dynamics of a quietly standing human. We explore four different possible feedback laws for the control of this multi-link pendulum in upright stance that map sensory data to motor commands. We perform simulation studies to compare the generated inter-joint covariance patterns with experimental data. We find that control laws that actively coordinate muscle activation between the different joints generate correct variance patterns, while control laws that control each joint separately do not. Different specific forms of this coordination are compatible with the data.

Dynamic Neural Fields with Intrinsic Plasticity.

Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building blocks in architectures to model sensorimotor embedding of cognitive processes. Typically, the parameters of a DNF in an architecture are manually tuned in order to achieve a specific dynamic behavior (e.g., decision making, selection, or working memory) for a given input pattern. This manual parameters search requires expert knowledge and time to find and verify a suited set of parameters. The DNF parametrization may be particular challenging if the input distribution is not known in advance, e.g., when processing sensory information. In this paper, we propose the autonomous adaptation of the DNF resting level and gain by a learning mechanism of intrinsic plasticity (IP). To enable this adaptation, an input and output measure for the DNF are introduced, together with a hyper parameter to define the desired output distribution. The online adaptation by IP gives the possibility to pre-define the DNF output statistics without knowledge of the input distribution and thus, also to compensate for changes in it. The capabilities and limitations of this approach are evaluated in a number of experiments.

A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity.

Handling objects or interacting with a human user about objects on a shared tabletop requires that objects be identified after learning from a small number of views and that object pose be estimated. We present a neurally inspired architecture that learns object instances by storing features extracted from a single view of each object. Input features are color and edge histograms from a localized area that is updated during processing. The system finds the best-matching view for the object in a novel input image while concurrently estimating the object's pose, aligning the learned view with current input. The system is based on neural dynamics, computationally operating in real time, and can handle dynamic scenes directly off live video input. In a scenario with 30 everyday objects, the system achieves recognition rates of 87.2% from a single training view for each object, while also estimating pose quite precisely. We further demonstrate that the system can track moving objects, and that it can segment the visual array, selecting and recognizing one object while suppressing input from another known object in the immediate vicinity. Evaluation on the COIL-100 dataset, in which objects are depicted from different viewing angles, revealed recognition rates of 91.1% on the first 30 objects, each learned from four training views.

A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating.

Reaching for objects and grasping them is a fundamental skill for any autonomous robot that interacts with its environment. Although this skill seems trivial to adults, who effortlessly pick up even objects they have never seen before, it is hard for other animals, for human infants, and for most autonomous robots. Any time during movement preparation and execution, human reaching movement are updated if the visual scene changes (with a delay of about 100 ms). The capability for online updating highlights how tightly perception, movement planning, and movement generation are integrated in humans. Here, we report on an effort to reproduce this tight integration in a neural dynamic process model of reaching and grasping that covers the complete path from visual perception to movement generation within a unified modeling framework, Dynamic Field Theory. All requisite processes are realized as time-continuous dynamical systems that model the evolution in time of neural population activation. Population level neural processes bring about the attentional selection of objects, the estimation of object shape and pose, and the mapping of pose parameters to suitable movement parameters. Once a target object has been selected, its pose parameters couple into the neural dynamics of movement generation so that changes of pose are propagated through the architecture to update the performed movement online. Implementing the neural architecture on an anthropomorphic robot arm equipped with a Kinect sensor, we evaluate the model by grasping wooden objects. Their size, shape, and pose are estimated from a neural model of scene perception that is based on feature fields. The sequential organization of a reach and grasp act emerges from a sequence of dynamic instabilities within a neural dynamics of behavioral organization, that effectively switches the neural controllers from one phase of the action to the next. Trajectory formation itself is driven by a dynamical systems version of the potential field approach. We highlight the emergent capacity for online updating by showing that a shift or rotation of the object during the reaching phase leads to the online adaptation of the movement plan and successful completion of the grasp.

A Neural Dynamic Model Generates Descriptions of Object-Oriented Actions.

Describing actions entails that relations between objects are discovered. A pervasively neural account of this process requires that fundamental problems are solved: the neural pointer problem, the binding problem, and the problem of generating discrete processing steps from time-continuous neural processes. We present a prototypical solution to these problems in a neural dynamic model that comprises dynamic neural fields holding representations close to sensorimotor surfaces as well as dynamic neural nodes holding discrete, language-like representations. Making the connection between these two types of representations enables the model to describe actions as well as to perceptually ground movement phrases-all based on real visual input. We demonstrate how the dynamic neural processes autonomously generate the processing steps required to describe or ground object-oriented actions. By solving the fundamental problems of neural pointing, binding, and emergent discrete processing, the model may be a first but critical step toward a systematic neural processing account of higher cognition.