Establishment of vagal sensorimotor circuits during fetal development in rats

The differentiation of vagal motor neurons and their emerging central relationship with vagal sensory afferents was examined in fetal rats. To identify peripherally projecting sensory and motor neurons, 1,1′-dioctadecyl 3,3,3′,3′-tetramethylindocarbocyanine perchloarate (DiI) was inserted into the proximal gut or cervical vagus nerve in fixed preparations. At embryonic day (E) 12, labeled vagal sensory neurons are present in the nodose ganglia and a few sensory axons project into the dorsolateral medulla. Central sensory processes become increasingly prevalent between E13 and E14 but remain restricted to the solitary tract. Vagal motor neurons are first labeled at E13, clustered within a region corresponding to the nucleus ambiguus (NA). Additional motor neurons appear to be migrating toward the NA from the germinal zone of the fourth ventricle. Motor neurons in the dorsal motor nucleus of the vagus (DMV) first project to the gut at E14 and have processes that remain in physical contact with the ventricular zone through E16. Sensory axons emerge from the solitary tract at E15 and project medially through the region of the nucleus of the solitary tract (NST) to end in the ventricular zone. A possible substrate for direct vagovagal, sensorimotor interaction appears at E16, when vagal sensory fibers arborize within the DMV and DMV dendrites extend into the NST. By E18, the vagal nuclei appear remarkably mature. These data suggest specific and discrete targeting of vagal sensory afferents and motor neuron dendrites in fetal rats and define an orderly sequence of developmental events that precedes the establishment of vagal sensorimotor circuits. © 1993 John Wiley & Sons, Inc.     

Learning with slight forgetting optimizes sensorimotor transformation in redundant motor systems

Recent theoretical studies have proposed that the redundant motor system in humans achieves well-organized stereotypical movements by minimizing motor effort cost and motor error. However, it is unclear how this optimization process is implemented in the brain, presumably because conventional schemes have assumed a priori that the brain somehow constructs the optimal motor command, and largely ignored the underlying trial-by-trial learning process. In contrast, recent studies focusing on the trial-by-trial modification of motor commands based on error information suggested that forgetting (i.e., memory decay), which is usually considered as an inconvenient factor in motor learning, plays an important role in minimizing the motor effort cost. Here, we examine whether trial-by-trial error-feedback learning with slight forgetting could minimize the motor effort and error in a highly redundant neural network for sensorimotor transformation and whether it could predict the stereotypical activation patterns observed in primary motor cortex (M1) neurons. First, using a simple linear neural network model, we theoretically demonstrated that: 1) this algorithm consistently leads the neural network to converge at a unique optimal state; 2) the biomechanical properties of the musculoskeletal system necessarily determine the distribution of the preferred directions (PD; the direction in which the neuron is maximally active) of M1 neurons; and 3) the bias of the PDs is steadily formed during the minimization of the motor effort. Furthermore, using a non-linear network model with realistic musculoskeletal data, we demonstrated numerically that this algorithm could consistently reproduce the PD distribution observed in various motor tasks, including two-dimensional isometric torque production, two-dimensional reaching, and even three-dimensional reaching tasks. These results may suggest that slight forgetting in the sensorimotor transformation network is responsible for solving the redundancy problem in motor control.     

Time and space distribution of coincident impulses in closed neural circuits in the sensorimotor cortex of the rabbit (the rhythmical defensive dominanta)

In the course of analysis of the conjugate unit activity of simultaneously recorded neurons in the sensorimotor cortex of rabbits, 22 closed neural circuits consisting of 3 or 4 neurons were considered. In the model of the defensive dominanta, 1-3 weeks after imposing rhythmic (2 s) activity to a rabbit, the distribution of coincident impulses was analyzed in real time. It was found out that the events when the coincident impulses of neural pairs were generated with two-second intervals could be shifted in time and space over a closed circuit of neurons in one direction. Two-second intervals between the coincident impulses of the neighboring pairs could be conjugate, i.e. the end of one interval in one pair coincided with the beginning of a two-second interval in the next pair. Conjugate intervals of the neighboring neural pairs could promote a pass-through of the information on the stimulus properties over the closed neuronal circuit, thus completing a full cycle. The longest passes-through lasted from 10 and 12 s. Also, more intricate variants of the information transfer were revealed. Thus, not only passes-through of the two- second intervals between the neuronal pairs were observed, but also, coincident impulses repeatedly occurred with this interval in some of the pairs of the circuits. The longest transitions lasted 16 and 22 s.     

Studying sensorimotor integration in insects

Sensorimotor integration is a field rich in theory backed by a large body of psychophysical evidence. Relating the underlying neural circuitry to these theories has, however, been more challenging. With a wide array of complex behaviors coordinated by their small brains, insects provide powerful model systems to study key features of sensorimotor integration at a mechanistic level. Insect neural circuits perform both hard-wired and learned sensorimotor transformations. They modulate their neural processing based on both internal variables, such as the animal's behavioral state, and external ones, such as the time of day. Here we present some studies using insect model systems that have produced insights, at the level of individual neurons, about sensorimotor integration and the various ways in which it can be modified by context.     

Occam's Razor in sensorimotor learning

A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam''s Razor—an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam''s Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models—a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants’ choice behaviour was quantitatively consistent with Bayesian Occam''s Razor. We also show that participants’ drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam''s Razor is a general behavioural principle already present during sensorimotor processing.     

Symmetric sensorimotor somatotopy

Background

Functional imaging has recently been used to investigate detailed somatosensory organization in human cortex. Such studies frequently assume that human cortical areas are only identifiable insofar as they resemble those measured invasively in monkeys. This is true despite the electrophysiological basis of the latter recordings, which are typically extracellular recordings of action potentials from a restricted sample of cells.

Methodology/Principal Findings

Using high-resolution functional magnetic resonance imaging in human subjects, we found a widely distributed cortical response in both primary somatosensory and motor cortex upon pneumatic stimulation of the hairless surface of the thumb, index and ring fingers. Though not organized in a discrete somatotopic fashion, the population activity in response to thumb and index finger stimulation indicated a disproportionate response to fingertip stimulation, and one that was modulated by stimulation direction. Furthermore, the activation was structured with a line of symmetry through the central sulcus reflecting inputs both to primary somatosensory cortex and, precentrally, to primary motor cortex.

Conclusions/Significance

In considering functional activation that is not somatotopically or anatomically restricted as in monkey electrophysiology studies, our methodology reveals finger-related activation that is not organized in a simple somatotopic manner but is nevertheless as structured as it is widespread. Our findings suggest a striking functional mirroring in cortical areas conventionally ascribed either an input or an output somatotopic function.     

Role of uncertainty in sensorimotor control

Neural signals are corrupted by noise and this places limits on information processing. We review the processes involved in goal-directed movements and how neural noise and uncertainty determine aspects of our behaviour. First, noise in sensory signals limits perception. We show that, when localizing our hand, the central nervous system (CNS) integrates visual and proprioceptive information, each with different noise properties, in a way that minimizes the uncertainty in the overall estimate. Second, noise in motor commands leads to inaccurate movements. We review an optimal-control framework, known as 'task optimization in the presence of signal-dependent noise', which assumes that movements are planned so as to minimize the deleterious consequences of noise and thereby minimize inaccuracy. Third, during movement, sensory and motor signals have to be integrated to allow estimation of the body's state. Models are presented that show how these signals are optimally combined. Finally, we review how the CNS deals with noise at the neural and network levels. In all of these processes, the CNS carries out the tasks in such a way that the detrimental effects of noise are minimized. This shows that it is important to consider effects at the neural level in order to understand performance at the behavioural level.     

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.     

Mathematics reflecting sensorimotor organization

 This review combines short presentations of several mathematical approaches that conceptualize issues in sensorimotor neuroscience from different perspectives and levels of analysis. The intricate organization of neural structures and sensorimotor performance calls for characterization using a variety of mathematical approaches. This review points out the prospects for mathematical neuroscience: in addition to computational approaches, there is a wide variety of mathematical approaches that provide insight into the organization of neural systems. By starting from the perspective that provides the greatest clarity, a mathematical approach avoids specificity that is inaccurate in characterizing the inherent biological organization. Approaches presented include the mathematics of ordered structures, motion-phase space, subject-coincident coordinates, equivalence classes, topological biodynamics, rhythm space metric, and conditional dynamics. Issues considered in this paper include unification of levels of analysis, response equivalence, convergence, relationship of physics to motor control, support of rhythms, state transitions, and focussing on low-dimensional subspaces of a high-dimensional sensorimotor space. Received: 28 August 2001 / Accepted in revised form: 11 July 2002 Correspondence to: e-mail: mccollum@ohsu.edu Acknowledgements. I would like to express many thanks for the critical comments on this review offered by William L. Bloch, Jan E. Holly, Gerhard Magnus, and Patrick D. Roberts.     

Navigating a sensorimotor loop

Touch is an active process, but how do the body's somatic sensors influence its movement? In this issue of Neuron, Nguyen and Kleinfeld show that afferent activity from the whiskers on a rat's face trigger rapid and prolonged excitation of the motor neurons that drive movements of the same whiskers. Positive feedback through this sensorimotor loop may serve to optimize the interaction between sensors and stimuli.     

Representations of uncertainty in sensorimotor control

Uncertainty is ubiquitous in our sensorimotor interactions, arising from factors such as sensory and motor noise and ambiguity about the environment. Setting it apart from previous theories, a quintessential property of the Bayesian framework for making inference about the state of world so as to select actions, is the requirement to represent the uncertainty associated with inferences in the form of probability distributions. In the context of sensorimotor control and learning, the Bayesian framework suggests that to respond optimally to environmental stimuli the central nervous system needs to construct estimates of the sensorimotor transformations, in the form of internal models, as well as represent the structure of the uncertainty in the inputs, outputs and in the transformations themselves. Here we review Bayesian inference and learning models that have been successful in demonstrating the sensitivity of the sensorimotor system to different forms of uncertainty as well as recent studies aimed at characterizing the representation of the uncertainty at different computational levels.     

Principles of sensorimotor learning

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.     

Synthetic in vitro circuits

Inspired by advances in the ability to construct programmable circuits in living organisms, in vitro circuits are emerging as a viable platform for designing, understanding, and exploiting dynamic biochemical circuitry. In vitro systems allow researchers to directly access and manipulate biomolecular parts without the unwieldy complexity and intertwined dependencies that often exist in vivo. Experimental and computational foundations in DNA, DNA/RNA, and DNA/RNA/protein based circuitry have given rise to systems with more than 100 programmed molecular constituents. Functionally, they have diverse capabilities including: complex mathematical calculations, associative memory tasks, and sensing of small molecules. Progress in this field is showing that cell-free synthetic biology is a versatile testing ground for understanding native biological circuits and engineering novel functionality.     

Altered processing of sensorimotor feedback in schizophrenia

Schizophrenia is characterized by an altered sense of the reality, associated with hallucinations and delusions. Some theories suggest that schizophrenia is related to a deficiency of the system that generates information about the sensory consequences of the actions realized by the subject. This system monitors the reafferent information resulting from an action and allows its anticipation. In the present study, we examined visual-event-related potentials (ERPs) generated by a sensorimotor task in 15 patients with schizophrenia and 15 normal controls. The visual feedback from hand movements performed by the subjects was experimentally distorted. Behavioral results showed that patients were impaired in recognizing their own movements. The ERP signal in patients also differed from those of control subjects. In patients, the ERP waveform was affected during the early part of the response (200 ms). This early effect in schizophrenic patients reveals a modified processing of the visual consequence of their actions.