A hierarchical neural-network model for control and learning of voluntary movement

In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum — magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum — parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural netowrk model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available. Consequently, the model not only accounts for the learning and control capability of the CNS, but also provides a promising parallel-distributed control scheme for a large-scale complex object whose dynamics are only partially known.     

Modelling the control of interceptive actions

In recent years, several phenomenological dynamical models have been formulated that describe how perceptual variables are incorporated in the control of motor variables. We call these short-route models as they do not address how perception-action patterns might be constrained by the dynamical properties of the sensory, neural and musculoskeletal subsystems of the human action system. As an alternative, we advocate a long-route modelling approach in which the dynamics of these subsystems are explicitly addressed and integrated to reproduce interceptive actions. The approach is exemplified through a discussion of a recently developed model for interceptive actions consisting of a neural network architecture for the online generation of motor outflow commands, based on time-to-contact information and information about the relative positions and velocities of hand and ball. This network is shown to be consistent with both behavioural and neurophysiological data. Finally, some problems are discussed with regard to the question of how the motor outflow commands (i.e. the intended movement) might be modulated in view of the musculoskeletal dynamics.     

Neural progenitor diversity and their therapeutic potential for spinal cord repair

Development of the central nervous system (CNS) requires progressive differentiation of neural stem cells, which generate a variety of neural progenitors with distinct properties and differentiation potentials in a spatiotemporally restricted manner. The underlying mechanisms of neural progenitor diversification during development started to be unraveled over the past years. We have addressed these questions by v-myc immortalization method and generation of neural progenitor clones. These clones are served as in vitro models of neural differentiation and cellular tools for transplantation in animal models of neurological disorders including spinal cord injury. In this review, we will discuss features of two neural progenitor types (radial glia and GABAergic interneuron progenitor) and diversification even within each progenitor type. We will also discuss pathophysiology of spinal cord injury and our ongoing research to address both motor and sensory malfunctions by transplantation of these neural progenitors.     

Mapping information flow in sensorimotor networks

Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior.     

Evolving experience-dependent robust behaviour in embodied agents

In this work, based on behavioural and dynamical evidence, a study of simulated agents with the capacity to change feedback from their bodies to accomplish a one-legged walking task is proposed to understand the emergence of coupled dynamics for robust behaviour. Agents evolve with evolutionary-defined biases that modify incoming body signals (sensory offsets). Analyses on whether these agents show further dependence to their environmental coupled dynamics than others with no feedback control is described in this article. The ability to sustain behaviours is tested during lifetime experiments with mutational and sensory perturbations after evolution. Using dynamical systems analysis, this work identifies conditions for the emergence of dynamical mechanisms that remain functional despite sensory perturbations. Results indicate that evolved agents with evolvable sensory offset depends not only on where in neural space the state of the neural system operates, but also on the transients to which the inner-system was being driven by sensory signals from its interactions with the environment, controller, and agent body. Experimental evidence here leads discussions on a dynamical systems perspective on behavioural robustness that goes beyond attractors of controller phase space.     

Order in spontaneous behavior

Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered Drosophila fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. Drosophila can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents.     

Shaping the dynamics of a bidirectional neural interface

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.     

Sensory system evolution at the origin of craniates

The multiple events at the transition from non-craniate invertebrate ancestors to craniates included the gain and/or elaboration of migratory neural crest and neurogenic placodes. These tissues give rise to the peripherally located, bipolar neurons of all non-visual sensory systems. The brain was also elaborated at or about this same time. Were the peripheral and central events simultaneous or sequential? A serial transformation hypothesis postulates that paired eyes and an enlarged brain evolved before the elaboration of migratory neural crest placodal sensory systems. Circumstantial evidence for this scenario is derived from the independent occurrence of the combination of large, paired eyes plus a large, elaborated brain in at least three taxa (cephalochordates, arthropods and craniates) and partly from the exclusivity of the diencephalon for visual system-related distal sensory components versus the restricted distribution of migratory neural crest-placodal sensory systems to the remaining parts of the neuraxis. This scenario accounts for the similarity of all central sensory system pathways due to the primary establishment of descending visual pathways via the diencephalon and midbrain tectum to brainstem motor regions and the subsequent exploitation of the same central beachhead by the migratory neural crest-placodal systems as a template for their organization.     

What we see is how we are: new paradigms in visual research

As most sensory modalities, the visual system needs to deal with very fast changes in the environment. Instead of processing all sensory stimuli, the brain is able to construct a perceptual experience by combining selected sensory input with an ongoing internal activity. Thus, the study of visual perception needs to be approached by examining not only the physical properties of stimuli, but also the brain's ongoing dynamical states onto which these perturbations are imposed. At least three different models account for this internal dynamics. One model is based on cardinal cells where the activity of few cells by itself constitutes the neuronal correlate of perception, while a second model is based on a population coding that states that the neuronal correlate of perception requires distributed activity throughout many areas of the brain. A third proposition, known as the temporal correlation hypothesis states that the distributed neuronal populations that correlate with perception, are also defined by synchronization of the activity on a millisecond time scale. This would serve to encode contextual information by defining relations between the features of visual objects. If temporal properties of neural activity are important to establish the neural mechanisms of perception, then the study of appropriate dynamical stimuli should be instrumental to determine how these systems operate. The use of natural stimuli and natural behaviors such as free viewing, which features fast changes of internal brain states as seen by motor markers, is proposed as a new experimental paradigm to study visual perception.     

Experiments on the central pattern generator for swimming in amphibian embryos

The central nervous system of paralysed Xenopus laevis embryos can generate a motor output pattern suitable for swimming locomotion. By recording motor root activity in paralysed embryos with transected nervous systems we have shown that: (a) the spinal cord is capable of swimming pattern generation; (b) swimming pattern generator capability in the hindbrain and spinal cord is distributed; (c) caudal hindbrain is necessary for sustained swimming output after discrete stimulation. By recording similarly from embryos whose central nervous system was divided longitudinally into left and right sides, we have shown that: (a) each side can generate rhythmic motor output with cycle periods like those in swimming; (b) during this activity cycle period increases within an episode, and there is the usual rostrocaudal delay found in swimming; (c) this activity is influenced by sensory stimuli in the same way as swimming activity; (d) normal phase coupling of the left and right sides can be established by the ventral commissure in the spinal cord. We conclude that interactions between the antagonistic (left and right) motor systems are not necessary for swimming rhythm generation and present a model for swimming pattern generation where autonomous rhythm generators on each side of the nervous system drive the motoneurons. Alternation is achieved by reciprocal inhibition, and activity is initiated and maintained by tonic excitation from the hindbrain.     

Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks

Feature extraction is a crucial part of advanced image recognition systems. In this research, an autonomous detection device was designed and developed for insect pest detection to improve the ability of intelligent systems in order to annihilate harmful insect pests in agricultural crop fields. Device included a dark chamber, a CCD digital camera, a LDR lightening module and a personal computer. The proposed programme for precise insect pest detection was based on an image processing algorithm and artificial neural networks (ANNs). After image acquisition, the insect pests’ images were extracted from original images with Canny filtration. Afterwards, four morphological and three textural features from the obtained images were measured and normalised. Performance of ANN model was tested successfully for Beet armyworm (Spodoptera exigua) recognition in images using back-propagation supervised learning method and inspection data. Results showed that proposed system was able to identify S. exigua in the images from other species. Such this machine vision system can be used in autonomous field robots to achieve a modern farmer’s assistant.     

A model of the neuro-musculo-skeletal system for human locomotion

The generation of human locomotion was examined by linking computational neuroscience with biomechanics from the perspective of nonlinear dynamical theory. We constructed a model of human locomotion, which includes a musculo-skeletal system with 8 segments and 20 muscles, a neural rhythm generator composed of 7 pairs of neural oscillators, and mechanisms for processing and transporting sensory and motor signals. Using a computer simulation, we found that locomotion emerged as a stable limit cycle that was generated by the global entrainment between the musculo-skeletal system, the neural system, and the environment. Moreover, the walking movements of the model could be compared quantitatively with those of experimental studies in humans.Part of this paper was presented to IVth International Symposium on Computer Simulation in Biomechanics, Paris, France, July 1, 1993     

Coordinates transformation and learning control for visually-guided voluntary movement with iteration: A Newton-like method in a function space

In order to control visually-guided voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of the coordinates of the desired trajectory to the body coordinates and (3) generation of motor command. In this paper, the second and the third problems are treated at computational, representational and hardware levels of Marr. We first study the problems at the computational level, and then propose an iterative learning scheme as a possible algorithm. This is a trial and error type learning such as repetitive training of golf swing. The amount of motor command needed to coordinate activities of many muscles is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Actually, the motor command in the (n+1)-th iteration is a sum of the motor command in then-th iteration plus two modification terms which are, respectively, proportional to acceleration and speed errors between the desired trajectory and the realized trajectory in then-th iteration. We mathematically formulate this iterative learning control as a Newton-like method in functional spaces and prove its convergence under appropriate mathematical conditions with use of dynamical system theory and functional analysis. Computer simulations of this iterative learning control of a robotic manipulator in the body or visual coordinates are shown. Finally, we propose that areas 2, 5, and 7 of the sensory association cortex are possible sites of this learning control. Further we propose neural network model which acquires transformation matrices from acceleration or velocity to motor command, which are used in these schemes.     

Multiple roles for Hox genes in segment-specific shaping of CNS lineages

In this “Extra View” article we highlight some of the recently accumulating evidence showing that Hox genes are involved at different steps during the development of neural cell lineages to control segmental patterning of the CNS. In addition to their well-known early role in establishing segmental identities, Hox genes act on neural stem cells and their progeny at various stages during embryonic and postembryonic development to control proliferation, cell fate and/or apoptosis in a segment-specific manner. This leads to differential shaping of serially homologous lineages and thus to structural diversification of segmental CNS units (neuromeres) in adaptation to their specific functional tasks in processing sensory information and generation of motor patterns.     

A model of motor and sensory axon activation in the median nerve using surface electrical stimulation

Surface electrical stimulation has the potential to be a powerful and non-invasive treatment for a variety of medical conditions but currently it is difficult to obtain consistent evoked responses. A viable clinical system must be able to adapt to variations in individuals to produce repeatable results. To more fully study the effect of these variations without performing exhaustive testing on human subjects, a system of computer models was created to predict motor and sensory axon activation in the median nerve due to surface electrical stimulation at the elbow. An anatomically-based finite element model of the arm was built to accurately predict voltages resulting from surface electrical stimulation. In addition, two axon models were developed based on previously published models to incorporate physiological differences between sensory and motor axons. This resulted in axon models that could reproduce experimental results for conduction velocity, strength-duration curves and activation threshold. Differences in experimentally obtained action potential shape between the motor and sensory axons were reflected in the models. The models predicted a lower threshold for sensory axons than motor axons of the same diameter, allowing a range of sensory axons to be activated before any motor axons. This system of models will be a useful tool for development of surface electrical stimulation as a method to target specific neural functions.     

Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings

The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory perception, decision making, and motor control. It is thought that such computations are implemented through the dynamical evolution of distributed activity in recurrent circuits. Thus, identifying dynamical structure in neural population activity is a key challenge towards a better understanding of neural computation. At the same time, interpreting this structure in light of the computation of interest is essential for linking the time-varying activity patterns of the neural population to ongoing computational processes. Here, we review methods that aim to quantify structure in neural population recordings through a dynamical system defined in a low-dimensional latent variable space. We discuss advantages and limitations of different modelling approaches and address future challenges for the field.     

Diverse mechanisms for assembly of branchiomeric nerves

The formation of branchiomeric nerves (cranial nerves V, VII, IX and X) from their sensory, motor and glial components is poorly understood. The current model for cranial nerve formation is based on the Vth nerve, in which sensory afferents are formed first and must enter the hindbrain in order for the motor efferents to exit. Using transgenic zebrafish lines to discriminate between motor neurons, sensory neurons and peripheral glia, we show that this model does not apply to the remaining three branchiomeric nerves. For these nerves, the motor efferents form prior to the sensory afferents, and their pathfinding show no dependence on sensory axons, as ablation of cranial sensory neurons by ngn1 knockdown had no effect. In contrast, the sensory limbs of the IXth and Xth nerves (but not the Vth or VIIth) were misrouted in gli1 mutants, which lack hindbrain bmn, suggesting that the motor efferents are crucial for appropriate sensory axon projection in some branchiomeric nerves. For all four nerves, peripheral glia were the intermediate component added and had a critical role in nerve integrity but not in axon guidance, as foxd3 null mutants lacking peripheral glia exhibited defasciculation of gVII, gIX, and gX axons. The bmn efferents were unaffected in these mutants. These data demonstrate that multiple mechanisms underlie formation of the four branchiomeric nerves. For the Vth, sensory axons initiate nerve formation, for the VIIth the sensory and motor limbs are independent, and for the IXth/Xth the motor axons initiate formation. In all cases the glia are patterned by the initiating set of axons and are needed to maintain axon fasciculation. These results reveal that coordinated interactions between the three neural cell types in branchiomeric nerves differ according to their axial position.     

An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition

Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences.     

Sensory-motor interactions modulate a primate vocal behavior: antiphonal calling in common marmosets

A fundamental issue in neuroscience pertains to how different cortical systems interact to generate behavior. One of the most direct ways to address this issue is to investigate how sensory information is encoded and used to produce a motor response. Antiphonal calling is a natural vocal behavior that involves individuals producing their species-specific long distance vocalization in response to hearing the same call and engages both the auditory and motor systems, as well as the cognitive neural systems involved in decision making and categorization. Here we present results from a series of behavioral experiments investigating the auditory–vocal interactions during antiphonal calling in the common marmoset (Callithrix jacchus). We manipulated sensory input by placing subjects in different social contexts and found that the auditory input had a significant effect on call timing and propensity to call. Playback experiments tested the significance of the timing of vocal production in antiphonal calling and showed that a short latency between antiphonal calls was necessary to maintain reciprocal vocal interactions. Overall, this study shows that sensory-motor interactions can be experimentally induced and manipulated in a natural primate vocal behavior. Antiphonal calling represents a promising model system to examine these issues in non-human primates at both the behavioral and neural levels.     

Agential autonomy and biological individuality

What is a biological individual? How are biological individuals individuated? How can we tell how many individuals there are in a given assemblage of biological entities? The individuation and differentiation of biological individuals are central to the scientific understanding of living beings. I propose a novel criterion of biological individuality according to which biological individuals are autonomous agents. First, I articulate an ecological–dynamical account of natural agency according to which, agency is the gross dynamical capacity of a goal-directed system to bias its repertoire to respond to its conditions as affordances. Then, I argue that agents or agential dynamical systems can be agentially dependent on, or agentially autonomous from, other agents and that this agential dependence/autonomy can be symmetrical or asymmetrical, strong or weak. Biological individuals, I propose, are all and only those agential dynamical systems that are strongly agentially autonomous. So, to determine how many individuals there are in a given multiagent aggregate, such as multicellular organism, a colony, symbiosis, or a swarm, we first have to identify how many agential dynamical systems there are, and then what their relations of agential dependence/autonomy are. I argue that this criterion is adequate to the extent that it vindicates the paradigmatic cases, and explains why the paradigmatic cases are paradigmatic, and why the problematic cases are problematic. Finally, I argue for the importance of distinguishing between agential and causal dependence and show the relevance of agential autonomy for understanding the explanatory structure of evolutionary developmental biology.