Single Synapse Information Coding in Intraburst Spike Patterns of Central Pattern Generator Motor Neurons

Burst firing is ubiquitous in nervous systems and has been intensively studied in central pattern generators (CPGs). Previous works have described subtle intraburst spike patterns (IBSPs) that, despite being traditionally neglected for their lack of relation to CPG motor function, were shown to be cell-type specific and sensitive to CPG connectivity. Here we address this matter by investigating how a bursting motor neuron expresses information about other neurons in the network. We performed experiments on the crustacean stomatogastric pyloric CPG, both in control conditions and interacting in real-time with computer model neurons. The sensitivity of postsynaptic to presynaptic IBSPs was inferred by computing their average mutual information along each neuron burst. We found that details of input patterns are nonlinearly and inhomogeneously coded through a single synapse into the fine IBSPs structure of the postsynaptic neuron following burst. In this way, motor neurons are able to use different time scales to convey two types of information simultaneously: muscle contraction (related to bursting rhythm) and the behavior of other CPG neurons (at a much shorter timescale by using IBSPs as information carriers). Moreover, the analysis revealed that the coding mechanism described takes part in a previously unsuspected information pathway from a CPG motor neuron to a nerve that projects to sensory brain areas, thus providing evidence of the general physiological role of information coding through IBSPs in the regulation of neuronal firing patterns in remote circuits by the CNS.

Physiological and pathophysiological interactions between the respiratory central pattern generator and the sympathetic nervous system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Respiratory rhythm generation: A modeling study of the respiratory central pattern generator and the phrenic motor neuron

The respiratory central pattern generator is a collection of medullary neurons that generates the rhythm of respiration. The respiratory central pattern generator feeds phrenic motor neurons, which, in turn, drive the main muscle of respiration, the diaphragm. The purpose of this thesis is to understand the neural control of respiration through mathematical models of the respiratory central pattern generator and phrenic motor neurons. ^ We first designed and validated a Hodgkin-Huxley type model that mimics the behavior of phrenic motor neurons under a wide range of electrical and pharmacological perturbations. This model was constrained physiological data from the literature. Next, we designed and validated a model of the respiratory central pattern generator by connecting four Hodgkin-Huxley type models of medullary respiratory neurons in a mutually inhibitory network. This network was in turn driven by a simple model of an endogenously bursting neuron, which acted as the pacemaker for the respiratory central pattern generator. Finally, the respiratory central pattern generator and phrenic motor neuron models were connected and their interactions studied. ^ Our study of the models has provided a number of insights into the behavior of the respiratory central pattern generator and phrenic motor neurons. These include the suggestion of a role for the T-type and N-type calcium channels during single spikes and repetitive firing in phrenic motor neurons, as well as a better understanding of network properties underlying respiratory rhythm generation. We also utilized an existing model of lung mechanics to study the interactions between the respiratory central pattern generator and ventilation. ^

Fractional central pattern generators for bipedal locomotion

Locomotion has been a major research issue in the last few years. Many models for the locomotion rhythms of quadrupeds, hexapods, bipeds and other animals have been proposed. This study has also been extended to the control of rhythmic movements of adaptive legged robots. In this paper, we consider a fractional version of a central pattern generator (CPG) model for locomotion in bipeds. A fractional derivative D α f(x), with α non-integer, is a generalization of the concept of an integer derivative, where α=1. The integer CPG model has been proposed by Golubitsky, Stewart, Buono and Collins, and studied later by Pinto and Golubitsky. It is a network of four coupled identical oscillators which has dihedral symmetry. We study parameter regions where periodic solutions, identified with legs’ rhythms in bipeds, occur, for 0<α≤1. We find that the amplitude and the period of the periodic solutions, identified with biped rhythms, increase as α varies from near 0 to values close to unity.

基于循环抑制CPG模型控制的蛇形机器人三维运动

具有三维运动能力和独特的节律运动方式,使生物蛇能在复杂的地形环境中生存.大多数动物节律运动是由中央模式发生器(Centralpatterngenerator,CPG)控制的.以此为理论依据,首次以循环抑制建模机理构建蛇形机器人组合关节运动控制的CPG模型.证明该模型是节律输出型CPG中微分方程维数最少的.采用单向激励方式连接该类CPG构建蛇形机器人三维运动神经网络控制体系,给出该CPG网络产生振荡输出的必要条件.应用蛇形机器人动力学模型仿真得到控制三维运动的CPG神经网络参数,利用该CPG网络的输出使“勘查者”成功实现三维运动.该结果为建立未探明的生物蛇神经网络模型提供了一种全新的方法.

基于循环抑制CPG模型的蛇形机器人控制器

依据生物利用中央模式发生器(Central pattern generator,CPG)的自激行为产生有节律的协调运动适应多种环境,基于循环抑制CPG建模理论设计了蛇形机器人CPG控制器模型,分析了单个神经元、循环抑制CPG以及该控制器模型的稳定性,并把该控制器应用到一个结合蛇形机器人“勘查者-Ⅰ”动力学特性的仿真模型,得到了实现蜿蜒运动的CPG控制器参数,进而研究了调节S波个数、身体构形曲率、蜿蜒运动速度以及运动轨迹曲率的CPG控制器参数设定策略。此外,“勘查者-Ⅰ”应用该CPG控制器的输出成功实现了蜿蜒运动。该研究结果为设计人工CPG控制器提供了一个可行的方法。

L'importance du système noradrénergique aux niveaux thoracique et lombaire de la moelle épinière pour la locomotion du chat

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

基于循环抑制CPG的蛇形机器人运动控制方法

蛇具有细长无肢的身体、独特的半球形关节，使其可在神经系统控制下完成与环境相适应的多种节律运动。模仿蛇的运动机理和行为方式而设计的蛇形机器人克服了轮腿式机器人的缺点，增加了机器人的运动方式，扩大了机器人的应用范围。但应用传统的控制策略实现蛇形机器人运动控制遇到了很难克服的问题。随着社会经济与科技的发展，研究人员把从蛇运动神经系统研究中得到的启示应用到蛇形机器人上，希望不仅可以解决其运动控制问题，更能在构型、步态及控制机制上皆可展示蛇的特征。 生物学家已经证明动物的节律运动是其低级神经中枢的自激行为，是由中枢模式发生器(Central Pattern Generator，CPG)控制的。中枢模式发生器是一种能够在缺乏有规律的感知和中枢控制输入的情况下，产生有节奏模式输出的神经网络。 本文以国家自然科学基金课题《基于CPG的蛇形机器人控制方法研究》和国家“863”高技术计划资助项目《具有环境适应能力的蛇形机器人的研究》为依托，突破以相互抑制机理研究CPG的传统观点，首次创新性地提出应用循环抑制(Cyclic Inhibition, CI)机理来研究蛇形机器人的CPG建模与实现问题。本研究涵概了神经元模型的特性分析、蛇形机器人关节循环抑制CPG建模理论、蛇形机器人循环抑制CPG神经网络稳定性分析以及典型步态的生成方法、循环抑制CPG神经网络控制蛇形机器人蜿蜒运动参数设定策略、应用动力学仿真和实验对该CPG控制方法有效性的验证。 首先，本文介绍了两个用于CPG建模研究的蛇形机器人“勘查者”和“勘查者-I”。给出各自机械系统、控制系统的构成和动力学仿真平台。 其次，详细分析了神经元以及传统的相互抑制(Mutual Inhibition, MI)CPG的特性。从工程角度首次创新性地应用循环抑制建模理论构建了蛇形机器人CPG模型，并对其稳定性进行了深入的分析。首次证明持续型神经元构成的单向循环抑制(Unilateral Cyclic Inhibition, UCI) CPG是能产生振荡输出CPG中微分方程数量最少的，而且其产生振荡输出的机理完全不同于传统的相互抑制CPG。其不需要具备调整功能，只需要神经元之间强的单向循环抑制连接。 第三，首次应用单向激励连接循环抑制CPG构成蛇形机器人神经网络系统。分析了其稳定性，给出其产生振荡输出的条件。通过仿真和实验验证了循环抑制CPG神经网络实现典型步态(蜿蜒运动、伸缩运动和侧向运动)的有效性。首次应用双向循环抑制(Bidirectional Cyclic Inhibition, BCI)CPG神经网络在不同高级控制神经元命令激活下的输出实现蛇形机器人典型运动步态之间的转换。为蛇节律运动生成机制建模提供了新方法。 最后，从实时性、控制方便性等工程应用的角度，对单向循环抑制CPG神经网络实现蛇形机器人蜿蜒运动控制进行了深入的分析。给出了S-波形、幅值、运动速度和运动轨迹曲率的参数设定策略。该系统应用首CPG自激励权重调解成功解决了传统CPG控制系统中CPG的个数比蛇形机器人关节数多一个的问题，并用其实现了一种独特的转弯控制策略。 综上，为蛇形机器人运动控制提供了全新的方法。

The central nervous control of walking in the locust, Schistocerca Americana

Rhythmic motor behaviors in all animals appear to be under the control of "central pattern generator" circuits, neural circuits which can produce output patterns appropriate for behavior even when isolated from their normal peripheral inputs. Insects have been a useful model system in which to study the control of legged terrestrial locomotion. Much is known about walking in insects at the behavioral level, but to date there has been no clear demonstration that a central pattern generator for walking exists. The focus of this thesis is to explore the central neural basis for locomotion in the locust, Schistocerca americana.

Rhythmic motor patterns could be evoked in leg motor neurons of isolated thoracic ganglia of locusts by the muscarinic agonist pilocarpine. These motor patterns would be appropriate for the movement of single legs during walking. Rhythmic patterns could be evoked in all three thoracic ganglia, but the segmental rhythms differed in their sensitivities to pilocarpine, their frequencies, and the phase relationships of motor neuron antagonists. These different patterns could be generated by a simple adaptable model circuit, which was both simulated and implemented in VLSI hardware. The intersegmental coordination of leg motor rhythms was then examined in preparations of isolated chains of thoracic ganglia. Correlations between motor patterns in different thoracic ganglia indicated that central coupling between segmental pattern generators is likely to contribute to the coordination of the legs during walking.

The work described here clearly demonstrates that segmental pattern generators for walking exist in insects. The pattern generators produce motor outputs which are likely to contribute to the coordination of the joints of a limb, as well as the coordination of different limbs. These studies lay the groundwork for further studies to determine the relative contributions of central and sensory neural mechanisms to terrestrial walking.

Neural Control of Interlimb Coordination and Gait Timing in Bipeds and Quadrupeds

1) A large body of behavioral data conceming animal and human gaits and gait transitions is simulated as emergent properties of a central pattern generator (CPG) model. The CPG model incorporates neurons obeying Hodgkin-Huxley type dynamics that interact via an on-center off-surround anatomy whose excitatory signals operate on a faster time scale than their inhibitory signals. A descending cornmand or arousal signal called a GO signal activates the gaits and controL their transitions. The GO signal and the CPG model are compared with neural data from globus pallidus and spinal cord, among other brain structures. 2) Data from human bimanual finger coordination tasks are simulated in which anti-phase oscillations at low frequencies spontaneously switch to in-phase oscillations at high frequencies, in-phase oscillations can be performed both at low and high frequencies, phase fluctuations occur at the anti-phase in-phase transition, and a "seagull effect" of larger errors occurs at intermediate phases. When driven by environmental patterns with intermediate phase relationships, the model's output exhibits a tendency to slip toward purely in-phase and anti-phase relationships as observed in humans subjects. 3) Quadruped vertebrate gaits, including the amble, the walk, all three pairwise gaits (trot, pace, and gallop) and the pronk are simulated. Rapid gait transitions are simulated in the order--walk, trot, pace, and gallop--that occurs in the cat, along with the observed increase in oscillation frequency. 4) Precise control of quadruped gait switching is achieved in the model by using GO-dependent modulation of the model's inhibitory interactions. This generates a different functional connectivity in a single CPG at different arousal levels. Such task-specific modulation of functional connectivity in neural pattern generators has been experimentally reported in invertebrates. Phase-dependent modulation of reflex gain has been observed in cats. A role for state-dependent modulation is herein predicted to occur in vertebrates for precise control of phase transitions from one gait to another. 5) The primary human gaits (the walk and the run) and elephant gaits (the amble and the walk) are sirnulated. Although these two gaits are qualitatively different, they both have the same limb order and may exhibit oscillation frequencies that overlap. The CPG model simulates the walk and the run by generating oscillations which exhibit the same phase relationships. but qualitatively different waveform shapes, at different GO signal levels. The fraction of each cycle that activity is above threshold quantitatively distinguishes the two gaits, much as the duty cycles of the feet are longer in the walk than in the run. 6) A key model properly concerns the ability of a single model CPG, that obeys a fixed set of opponent processing equations to generate both in-phase and anti-phase oscillations at different arousal levels. Phase transitions from either in-phase to anti-phase oscillations, or from anti-phase to in-phase oscillations, can occur in different parameter ranges, as the GO signal increases.

A Neural Pattern Generator that Exhibits Arousal-Dependant Human Gait Transitions

A neural pattern generator based upon a non-linear cooperative-competitive feedback neural network is presented. It can generate the two standard human gaits: the walk and the run. A scalar arousal or GO signal causes a bifurcation from one gait to the next. Although these two gaits are qualitatively different, they both have the same limb order and may exhibit oscillation frequencies that overlap. The model simulates the walk and the run via qualitatively different waveform shapes. The fraction of cycle that activity is above threshold distinguishes the two gaits, much as the duty cycles of the feet are longer in the walk than in the run.

Quadruped Gait Transitions from a Neural Pattern Generator with Arousal Modulated Interactions

A four-channel neural pattern generator is described in which both the frequency and the relative phase of oscillations are controlled by a scalar arousal or GO signal. The generator is used to simulate quadruped gaits; in particular, rapid transitions are simulated in the order - walk, trot, pace, and gallop - that occurs in the cat. Precise switching control is achieved by using an arousal dependent modulation of the model's inhibitory interactions. This modulation generates a different functional connectivity in a single network at different arousal levels.

A Neural Pattern Generator that Exhibits Frequency-Dependent In-Phase and Anti-Phase Oscillations

This article describes a. neural pattern generator based on a cooperative-competitive feedback neural network. The two-channel version of the generator supports both in-phase and anti-phase oscillations. A scalar arousal level controls both the oscillation phase and frequency. As arousal increases, oscillation frequency increases and bifurcations from in-phase to anti-phase, or anti-phase to in-phase oscillations can occur. Coupled versions of the model exhibit oscillatory patterns which correspond to the gaits used in locomotion and other oscillatory movements by various animals.

Complex order biped rhythms

Animal locomotion is a complex process, involving the central pattern generators (neural networks, located in the spinal cord, that produce rhythmic patterns), the brainstem command systems, the steering and posture control systems and the top layer structures that decide which motor primitive is activated at a given time. Pinto and Golubitsky studied an integer CPG model for legs rhythms in bipeds. It is a four-coupled identical oscillators' network with dihedral symmetry. This paper considers a new complex order central pattern generator (CPG) model for locomotion in bipeds. A complex derivative Dα±jβ, with α, β ∈ ℜ+, j = √-1, is a generalization of the concept of an integer derivative, where α = 1, β = 0. Parameter regions where periodic solutions, identified with legs' rhythms in bipeds, occur, are analyzed. Also observed is the variation of the amplitude and period of periodic solutions with the complex order derivative.