Michael G. Paulin

The role of the cerebellum in motor control and perception.

The cerebellum has an important role in control and coordination of movements, but in some species, notably weakly electric fish of the family Mormyridae, anatomical, electrophysiological and behavioural evidence indicates that parts of cerebellar cortex are concerned with tracking movements of objects around the animal, rather than with controlling movements of the animal itself. The existence of such anomalies suggests that the cerebellum may not be exclusively, or even primarily, a structure for motor control. Evidence reviewed in this paper shows that the cerebellum is associated with sensory systems used for tracking movements of targets in the environment, as well as movements made by the animal itself, in all vertebrates, not just in a few isolated cases. The evidence indicates that the standard theory that the function of the cerebellum is control and coordination of movements only partially characterises cerebellar function. The cerebellum may be better characterised as a tracking system, with an important role in control and coordination of movements which arises because of an animals need to track moving objects, to track its own movements, and to analyse the sensory consequences of movements in order to control movements. This theory not only predicts the known motor consequences of cerebellar dysfunction, it also predicts a specific kind of perceptual deficit caused by cerebellar dysfunction, namely an inability to accurately follow and predict trajectories of objects moving in the environment. A variety of behavioural and perceptual tasks in addition to motor control and movement tracking may require dynamical state estimation, and therefore may involve the cerebellum.

Evolution of the cerebellum as a neuronal machine for Bayesian state estimation

The cerebellum evolved in association with the electric sense and vestibular sense of the earliest vertebrates. Accurate information provided by these sensory systems would have been essential for precise control of orienting behavior in predation. A simple model shows that individual spikes in electrosensory primary afferent neurons can be interpreted as measurements of prey location. Using this result, I construct a computational neural model in which the spatial distribution of spikes in a secondary electrosensory map forms a Monte Carlo approximation to the Bayesian posterior distribution of prey locations given the sense data. The neural circuit that emerges naturally to perform this task resembles the cerebellar-like hindbrain electrosensory filtering circuitry of sharks and other electrosensory vertebrates. The optimal filtering mechanism can be extended to handle dynamical targets observed from a dynamical platform; that is, to construct an optimal dynamical state estimator using spiking neurons. This may provide a generic model of cerebellar computation. Vertebrate motion-sensing neurons have specific fractional-order dynamical characteristics that allow Bayesian state estimators to be implemented elegantly and efficiently, using simple operations with asynchronous pulses, i.e. spikes. The computational neural models described in this paper represent a novel kind of particle filter, using spikes as particles. The models are specific and make testable predictions about computational mechanisms in cerebellar circuitry, while providing a plausible explanation of cerebellar contributions to aspects of motor control, perception and cognition.

Peripheral modulation of worker bee responses to queen mandibular pheromone

It is generally accepted that young worker bees (Apis mellifera L.) are highly attracted to queen mandibular pheromone (QMP). Our results challenge this widely held view. We have found that unless young workers are exposed to QMP early in adult life, they, like foragers, avoid contact with this pheromone. Our data indicate that responses to QMP are regulated peripherally, at the level of the antennal sensory neurons, and that a window of opportunity exists in which QMP can alter a young bees response to this critically important pheromone. Exposing young bees to QMP from the time of adult emergence reduces expression in the antennae of the D1-like dopamine receptor gene, Amdop1. Levels of Amdop3 transcript, on the other hand, and of the octopamine receptor gene Amoa1, are significantly higher in the antennae of bees strongly attracted to QMP than in bees showing no attraction to this pheromone. A decline in QMP attraction with age is accompanied by a fall in expression in worker antennae of the D2-like dopamine receptor, AmDOP3, a receptor that is selectively activated by QMP. Taken together, our findings suggest that QMPs actions peripherally not only suppress avoidance behavior, but also enhance attraction to QMP, thereby facilitating attendance of the queen.

Digital filters for firing rate estimation

When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram.

Neural simulations of adaptive reafference suppression in the elasmobranch electrosensory system

The electrosensory system of elasmobranchs is extremely sensitive to weak electric fields, with behavioral thresholds having been reported at voltage gradients as low as 5 nV/cm. To achieve this amazing sensitivity, the electrosensory system must extract weak extrinsic signals from a relatively large reafferent background signal associated with the animals own movements. Ventilatory movements, in particular, strongly modulate the firing rates of primary electrosensory afferent nerve fibers, but this modulation is greatly suppressed in the medullary electrosensory processing nucleus, the dorsal octavolateral nucleus. Experimental evidence suggests that the neural basis of reafference suppression involves a common-mode rejection mechanism supplemented by an adaptive filter that fine tunes the cancellation. We present a neural model and computer simulation results that support the hypothesis that the adaptive component may involve an anti-Hebbian form of synaptic plasticity at molecular layer synapses onto ascending efferent neurons, the principal output neurons of the nucleus. Parallel fibers in the molecular layer carry a wealth of proprioceptive, efference copy, and sensory signals related to the animals own movements. The proposed adaptive mechanism acts by canceling out components of the electrosensory input signal that are consistently correlated with these internal reference signals.

An upper-body can improve the stability and efficiency of passive dynamic walking ☆

The compass-gait walker proposed by McGeer can walk down a shallow slope with a self-stabilizing gait that requires no actuation or control. However, as the slope goes to zero so does the walking speed, and dynamic gait stability is only possible over a very narrow range of slopes. Gomes and Ruina have results demonstrating that by adding a torso to the compass-gait walker, it can walk passively on level-ground with a non-infinitesimal constant average speed. However, the gait involves exaggerated joint movements, and for energetic reasons horizontal passive dynamic walking cannot be stable. We show in this research that in addition to collision-free walking, adding a torso improves stability and walking speed when walking downhill. Furthermore, adding arms to the torso results in a collision-free periodic gait with natural-looking torso and limb movements. Overall, in contrast to the suggestions that active control may be needed to balance an upper-body on legs, it turns out that the upper and lower bodies can be integrated to improve the stability, efficiency and speed of a passive dynamic walker.

Dynamics and the single spike

Responses of vestibular primary afferent neurons to head rotation exhibit fractional-order dynamics. As a consequence, the head tends to be in a localized region of its state-space at spike times of a particular neuron during arbitrary head movements, and single spikes can be interpreted as state measurements. We are developing a model of neural computations underlying trajectory prediction and control tasks, based on this experimental observation. This is a step toward a formal neural calculus in which single spikes are modeled realistically as the operands of neural computation.

Predation and the Origin of Neurones

The core design of spiking neurones is remarkably similar throughout the animal kingdom. Their basic function as fast-signalling thresholding cells might have been established very early in their evolutionary history. Identifying the selection pressures that drove animals to evolve spiking neurones could help us interpret their design and function today. We review fossil, ecological and molecular evidence to investigate when and why animals evolved spiking neurones. Fossils suggest that animals evolved nervous systems soon after the advent of animal-on-animal predation, 550 million years ago (MYa). Between 550 and 525 MYa, we see the first fossil appearances of many animal innovations, including eyes. Animal behavioural complexity increased during this period as well, as evidenced by their traces, suggesting that nervous systems were an innovation of that time. Fossils further suggest that, before 550 MYa, animals were either filter feeders or microbial mat grazers. Extant sponges and Trichoplax perform these tasks using energetically cheaper alternatives than spiking neurones. Genetic evidence testifies that nervous systems evolved before the protostome-deuterostome split. It is less clear whether nervous systems evolved before the cnidarian-bilaterian split, so cnidarians and bilaterians might have evolved their nervous systems independently. The fossil record indicates that the advent of predation could fit into the window of time between those two splits, though molecular clock studies dispute this claim. Collectively, these lines of evidence indicate that animals evolved spiking neurones soon after they started eating each other. The first sensory neurones could have been threshold detectors that spiked in response to other animals in their proximity, alerting them to perform precisely timed actions, such as striking or fleeing.

Intra-session and inter-day reliability of forearm surface EMG during varying hand grip forces☆

Surface electromyography (EMG) is widely used to evaluate forearm muscle function and predict hand grip forces; however, there is a lack of literature on its intra-session and inter-day reliability. The aim of this study was to determine reliability of surface EMG of finger and wrist flexor muscles across varying grip forces. Surface EMG was measured from six forearm flexor muscles of 23 healthy adults. Eleven of these subjects undertook inter-day test-retest. Six repetitions of five randomized isometric grip forces between 0% and 80% of maximum force (MVC) were recorded and normalized to MVC. Intra- and inter-day reliability were calculated through the intraclass correlation coefficient (ICC) and standard error of measurement (SEM). Normalized EMG produced excellent intra-session ICC of 0.90 when repeated measurements were averaged. Intra-session SEM was low at low grip forces, however, corresponding normalized SEM was high (23-45%) due to the small magnitude of EMG signals. This may limit the ability to evaluate finer forearm muscle function and hand grip forces in daily tasks. Combining EMG of functionally related muscles improved intra-session SEM, improving within-subject reliability without taking multiple measurements. Removing and replacing electrodes inter-day produced poor ICC (ICC < 0.50) but did not substantially affect SEM.

Optimal firing rate estimation

We define a measure for evaluating the quality of a predictive model of the behavior of a spiking neuron. This measure, information gain per spike (Is), indicates how much more information is provided by the model than if the prediction were made by specifying the neurons average firing rate over the same time period. We apply a maximum Is criterion to optimize the performance of Gaussian smoothing filters for estimating neural firing rates. With data from bullfrog vestibular semicircular canal neurons and data from simulated integrate-and-fire neurons, the optimal bandwidth for firing rate estimation is typically similar to the average firing rate. Precise timing and average rate models are limiting cases that perform poorly. We estimate that bullfrog semicircular canal sensory neurons transmit in the order of 1 bit of stimulus-related information per spike.