Coding characteristics of spiking local interneurons during imposed limb movements in the locust

Pre-processing and transfer entropy measures in motor neurons controlling limb movements

Directed information transfer measures are increasingly being employed in modeling neural system behavior due to their model-free approach, applicability to nonlinear and stochastic signals, and the potential to integrate repetitions of an experiment. Intracellular physiological recordings of graded synaptic potentials provide a number of additional challenges compared to spike signals due to non-stationary behaviour generated through extrinsic processes. We therefore propose a method to overcome this difficulty by using a preprocessing step based on Singular Spectrum Analysis (SSA) to remove nonlinear trends and discontinuities. We apply the method to intracellular recordings of synaptic responses of identified motor neurons evoked by stimulation of a proprioceptor that monitors limb position in leg of the desert locust. We then apply normalized delayed transfer entropy measures to neural responses evoked by displacements of the proprioceptor, the femoral chordotonal organ, that contains sensory neurones that monitor movements about the femoral-tibial joint. We then determine the consistency of responses within an individual recording of an identified motor neuron in a single animal, between repetitions of the same experiment in an identified motor neurons in the same animal and in repetitions of the same experiment from the same identified motor neuron in different animals. We found that delayed transfer entropy measures were consistent for a given identified neuron within and between animals and that they predict neural connectivity for the fast extensor tibiae motor neuron.

Sensory Cortical Activity Is Related to the Selection of a Rhythmic Motor Action Pattern

Rats produce robust, highly distinctive orofacial rhythms in response to taste stimuli-responses that aid in the consumption of palatable tastes and the ejection of aversive tastes, and that are sourced in a multifunctional brainstem central pattern generator. Several pieces of indirect evidence suggest that primary gustatory cortex (GC) may be a part of a distributed forebrain circuit involved in the selection of particular consumption-related rhythms, although not in the production of individual mouth movements per se. Here, we performed a series of tests of this hypothesis. We first examined the temporal relationship between GC activity and orofacial behaviors by performing paired single-neuron and electromyographic recordings in awake rats. Using a trial-by-trial analysis, we found that a subset of GC neurons shows a burst of activity beginning before the transition between nondistinct and taste-specific (i.e., consumption-related) orofacial rhythms. We further showed that shifting the latency of consumption-related behavior by selective cueing has an analogous impact on the timing of GC activity. Finally, we showed the complementary result, demonstrating that optogenetic perturbation of GC activity has a modest but significant impact on the probability that a specific rhythm will be produced in response to a strongly aversive taste. GC appears to be a part of a distributed circuit that governs the selection of taste-induced orofacial rhythms.

SIGNIFICANCE STATEMENT

In many well studied (typically invertebrate) sensorimotor systems, top-down modulation helps motor-control regions "select" movement patterns. Here, we provide evidence that gustatory cortex (GC) may be part of the forebrain circuit that performs this function in relation to oral behaviors ("gapes") whereby a substance in the mouth is rejected as unpalatable. We show that GC palatability coding is well timed to play this role, and that the latency of these codes changes as the latency of gaping shifts with learning. We go on to show that by silencing these neurons, we can change the likelihood of gaping. These data help to break down the sensory/motor divide by showing a role for sensory cortex in the selection of motor behavior.

Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network

Understanding the patterns of interconnections between neurons in complex networks is an enormous challenge using traditional physiological approaches. Here we combine the use of an information theoretic approach with intracellular recording to establish patterns of connections between layers of interneurons in a neural network responsible for mediating reflex movements of the hind limb of an insect. By analysing delayed mutual information of the synaptic and spiking responses of sensory neurons, spiking and nonspiking interneurons in response to movement of a joint receptor that monitors the position of the tibia relative to the femur, we are able to predict the patterns of interconnections between the layers of sensory neurons and interneurons in the network, with results matching closely those known from the literature. In addition, we use cross-correlation methods to establish the sign of those interconnections and show that they also show a high degree of similarity with those established for these networks over the last 30 years. The method proposed in this paper has great potential to elucidate functional connectivity at the neuronal level in many different neuronal networks.

The dynamics of analogue signalling in local networks controlling limb movement

Communication by analogue signals is relatively common in arthropod local networks. In the locust, non-spiking local interneurons play a key role in controlling sets of motor neurons in the generation of local reflex movements of the limbs. Here, our aim was two-fold. Our first aim was to determine the coding properties of a subpopulation of these interneurons by using system identification approaches. To this end, the femoro-tibial chordotonal organ, which monitors the movements of the tibia about the femur, was stimulated with Gaussian white noise and with more natural stimuli corresponding to the movements of the tibia during walking. The results showed that the sample of interneurons analysed displayed a wide, and overlapping, range of response characteristics. The second aim was to develop and test improved data analysis methods for describing neuronal function that are more robust and allow statistical analysis, a need emphasized by the high levels of background neuronal activity usually observed. We found that nonlinear models provided an improved fit in describing the response properties of interneurons that were then classified with statistical clustering methods. We identified four distinct categories of interneuron response that can be further divided into nine groups, with most interneurons being excited during extension movements of the leg, reflecting the outputs of upstream spiking local interneurons.

A system identification analysis of neural adaptation dynamics and nonlinear responses in the local reflex control of locust hind limbs

Nonlinear type system identification models coupled with white noise stimulation provide an experimentally convenient and quick way to investigate the often complex and nonlinear interactions between the mechanical and neural elements of reflex limb control systems. Previous steady state analysis has allowed the neurons in such systems to be categorised by their sensitivity to position, velocity or acceleration (dynamics) and has improved our understanding of network function. These neurons, however, are known to adapt their output amplitude or spike firing rate during repetitive stimulation and this transient response may be more important than the steady state response for reflex control. In the current study previously used system identification methods are developed and applied to investigate both steady state and transient dynamic and nonlinear changes in the neural circuit responsible for controlling reflex movements of the locust hind limbs. Through the use of a parsimonious model structure and Monte Carlo simulations we conclude that key system dynamics remain relatively unchanged during repetitive stimulation while output amplitude adaptation is occurring. Whilst some evidence of a significant change was found in parts of the systems nonlinear response, the effect was small and probably of little physiological relevance. Analysis using biologically more realistic stimulation reinforces this conclusion.

Understanding neuronal systems in movement control using Wiener/Volterra kernels: a dominant feature analysis

Although Volterra kernels have been extensively applied in modelling and analysis of biological systems, the relationship between the kernel characteristics and physiologically important features under study is still not revealed clearly. In this study, the link between Volterra kernels and dynamic response of neural systems which control animal movements was investigated and demonstrated using a dominant feature analysis. The new results show an effective but simplified method to use Volterra or Wiener kernels to understand and classify the neural systems which are responsible for the fundamental movements such as flexion and extension of animal limbs, and importantly demonstrate how the neuron pathways in locusts control joint activities of low and high frequency and perform fundamental joint movements such as position, velocity and acceleration. These results provide a useful insight into the nonlinear characteristics of neural systems in movement control and show a useful approach to the analysis of physiological systems using Volterra/Wiener kernels.