Inhibition of crossed caudal interneurons by lateral interneurons in lamprey spinal cord during fictive locomotion

Templates of the membrane potential profiles from lateral (LI) interneurons and motoneurons during glutamate- and N-methyl-D-aspartate (NMDA)-induced fictive locomotion showed pronounced plateau phases. In contrast, crossed caudal (CC) interneurons had a less obvious and steeper plateau region that was followed by a clear notch coinciding with the end of the lateral interneuron plateau phase. These results indicate a significant inhibitory input from LI to CC interneurons.     

Intersegmental coordination in the lamprey: simulations using a network model without segmental boundaries

Swimming in vertebrates such as eel and lamprey involves the coordination of alternating left and right activity in each segment. Forward swimming is achieved by a lag between the onset of activity in consecutive segments rostrocaudally along the spinal cord. The intersegmental phase lag is approximately 1% of the cycle duration per segment and is independent of the swimming frequency. Since the lamprey has approximately 100 spinal segments, at any given time one wave of activity is propagated along the body. Most previous simulations of intersegmental coordination in the lamprey have treated the cord as a chain of coupled oscillators or well-defined segments. Here a network model without segmental boundaries is described which can produce coordinated activity with a phase lag. This ‘continuous’ pattern-generating network is composed of a column of 420 excitatory interneurons (E1 to E420) and 300 inhibitory interneurons (C1 to C300) on each half of the simulated spinal cord. The interneurons are distributed evenly along the simulated spinal cord, and their connectivity is chosen to reflect the behavior of the intact animal and what is known about the length and strength of the synaptic connections. For example, E100 connects to all interneurons between E51 and E149, but at varying synaptic strengths, while E101 connects to all interneurons between E52 and E150. This unsegmented E-C network generates a motor pattern that is sampled by output elements similar to motoneurons (M cells), which are arranged along the cell column so that they receive input from seven E and five C interneurons. The M cells thus represent the summed excitatory and inhibitory input at different points along the simulated spinal cord and can be regarded as representing the ventral root output to the myotomes along the spinal cord. E and C interneurons have five simulated compartments and Hodgkin-Huxley based dynamics. The simulated network produces rhythmic output over a wide range of frequencies (1–11 Hz) with a phase lag constant over most of the length, with the exception of the ‘cut’ ends due to reduced synaptic input. As the inhibitory C interneurons in the simulation have more extensive caudal than rostral projections, the output of the simulation has positive phase lags, as occurs in forward swimming. However, unlike the biological network, phase lags in the simulation increase significantly with burst frequency, from 0.5% to 2.3% over the range of frequencies of the simulation. Local rostral or caudal increases in excitatory drive in the simulated network are sufficient to produce motor patterns with increased or decreased phase lags, respectively. Received: 15 December 1995 / Accepted in revised form: 17 September 1996     

Computer simulation of inhibition-dependent binding in a neural network

Reverberating dynamics of neural network is modeled on PC in order to illustrate possible role of inhibition as binding controller in the network. The network is composed of binding neurons. In the binding neuron model [BioSystems 48 (1998) 263], the degree of temporal coherence between synaptic inputs is decisive for triggering, and slow inhibition is expressed in terms of the degree, which is necessary for triggering. Two learning mechanisms are implemented in the network, namely, adjusting synaptic strength and/or propagation delays. By means of forced playing of external pattern, the network is taught to support dynamics with disconnected and bound patterns of activity. By choosing either high, or low inhibition, one can switch between the disconnected and bound patterns, respectively. This is interpreted as inhibition-controlled binding in the network.     

The nature of the coupling between segmental oscillators of the lamprey spinal generator for locomotion: A mathematical model

We present a theoretical model which is used to explain the intersegmental coordination of the neural networks responsible for generating locomotion in the isolated spinal cord of lamprey.A simplified mathematical model of a limit cycle oscillator is presented which consists of only a single dependent variable, the phase (t). By coupling N such oscillators together we are able to generate stable phase locked motions which correspond to traveling waves in the spinal cord, thus simulating fictive swimming. We are also able to generate irregular drifting motions which are compared to the experimental data obtained from cords with selective surgical lesions.     

Computer simulation of diffusely-connected neuronal populations

This paper describes computer simulations of diffusely-connected neuronal populations. Main findings are that diffuse monosynaptic linkages between populations are selectively sensitive to synchronized clusters of action potentials in the pre-synaptic population; that diffusely-connected excitatory recurrent collaterals tend to produce rhythmic series of synchronized clusters; and that diffusely-connected inhibition (both recurrent and afferent) tend to reduce the number of cells participating in a given synchronized cluster and thereby the overall transfer rate. However, recurrent inhibition tends to increase the rate at which synchronized clusters are produced by recurrent excitation. These results suggest the speculation that diffusely connected neuronal populations are particularly prone to deal with synchronized clusters of action potentials.This work has been supported by Grant GB 33687 of the National Science Foundation, Grant 1-R01-NS-10781-01 COM of the National Institutes of Health, and by a fellowship from Zonta, International     

Reconstruction of dopaminergic neural network and locomotion function in planarian regenerates

Planarian, an invertebrate flatworm, has a high capacity for regeneration when compared with other worms and animals. We show here for the first time that the reconstructed dopamine (DA) neural network regulates locomotion and behavior in planarian regenerates. The gene encoding tyrosine hydroxylase in the planarian Dugesia japonica (DjTH) was identified. DjTH protein was coexpressed with aromatic amino acid decarboxylase-like A (DjAADCA) in the planarian central nervous system (CNS). In addition, DjTH-knockdown planarians lost the ability to synthesize DA, but showed no change in 5-hydroxytryptamine synthesis. When the planarian body was amputated, DjTH-positive neurons were regenerated in the brain newly rebuilt from the tail piece at Day 3, and the DjTH-positive axonal and dendritic neural network in the CNS (dopaminergic tiara) was reconstructed at Days 5-7. At that time, autonomic locomotion and methamphetamine-induced hyperkinesia were also suppressed in DjTH-knockdown planarians. Planarian locomotion and behavior seem to be regulated in both cilia- and muscle-dependent manners. In DjTH-knockdown planarians, muscle-mediated locomotion and behavior were significantly attenuated. These results suggest that DA neurons play a key role in the muscle-mediated movement in planarians.     

The development of the lamprey pattern generator for locomotion

The life cycle of the lamprey includes a larval stage that can last for several years. The motor behavior of the larval lamprey, the ammocoete, has been only minimally studied and little is known of the neural correlates of that behavior. Comparison of known larval behavior to that of adults leaves unclear whether there are large or small changes in the spinal nervous system during transformation. The motor output of isolated larval and transforming spinal cords when stimulated to "swim" with D-glutamate has some differences from that of comparable adult preparations, but shares many important features with adults. Primarily, the fictive swimming is less well regulated and less stable than adults of the same species. We propose that a major difference in the structure and organization of the central pattern generator for locomotion between adults and ammocoetes is a relative lack or immaturity of some cell types that participate in the coordination of the segments and the generation of the rhythm of the periodic bursting.     

A computational model of visually guided locomotion in lamprey

This study addresses mechanisms for the generation and selection of visual behaviors in anamniotes. To demonstrate the function of these mechanisms, we have constructed an experimental platform where a simulated animal swims around in a virtual environment containing visually detectable objects. The simulated animal moves as a result of simulated mechanical forces between the water and its body. The undulations of the body are generated by contraction of simulated muscles attached to realistic body components. Muscles are driven by simulated motoneurons within networks of central pattern generators. Reticulospinal neurons, which drive the spinal pattern generators, are in turn driven directly and indirectly by visuomotor centers in the brainstem. The neural networks representing visuomotor centers receive sensory input from a simplified retina. The model also includes major components of the basal ganglia, as these are hypothesized to be key components in behavior selection. We have hypothesized that sensorimotor transformation in tectum and pretectum transforms the place-coded retinal information into rate-coded turning commands in the reticulospinal neurons via a recruitment network mimicking the layered structure of tectal areas. Via engagement of the basal ganglia, the system proves to be capable of selecting among several possible responses, even if exposed to conflicting stimuli. The anatomically based structure of the control system makes it possible to disconnect different neural components, yielding concrete predictions of how animals with corresponding lesions would behave. The model confirms that the neural networks identified in the lamprey are capable of responding appropriately to simple, multiple, and conflicting stimuli.     

Computer simulation of within family selection in finite populations

Summary The relative efficiency, in terms of selection limits, between mass selection and within family selection was compared by computer simulation methods. A 20-locus additive model was used to simulate a quantitative trait under selection. It was assumed that 50–75 percent of the genetic variance in the base population was controlled by four major genes initially at low frequencies.In populations of size N=100 no loss of major genes was found when either method of selection was used. When N=50 within family selection was generally superior to mass selection but when N=10 the situation was reversed. For N=30 within family selection was more efficient only under high selection intensity or high heritability situations.     

Insights from a sea lamprey into the evolution of neural crest gene regulatory network

The neural crest is a vertebrate innovation that forms at the embryonic neural plate border, transforms from epithelial to mesenchymal, migrates extensively throughout the embryo along well-defined pathways, and differentiates into a plethora of derivatives that include elements of peripheral nervous system, craniofacial skeleton, melanocytes, etc. The complex process of neural crest formation is guided by multiple regulatory modules that define neural crest gene regulatory network (NC GRN), which allows the neural crest to progressively acquire all of its defining characteristics. The molecular study of neural crest formation in lamprey, a basal extant vertebrate, consisting in identification and functional tests of molecular elements at each regulatory level of this network, has helped address the question of the timing of emergence of NC GRN and define its basal state. The results have revealed striking conservation in deployment of upstream factors and regulatory modules, suggesting that proximal portions of the network arose early in vertebrate evolution and have been tightly conserved for more than 500 million years. In contrast, certain differences were observed in deployment of some neural crest specifier and downstream effector genes expected to confer species-specific migratory and differentiation properties.     

A hemicord locomotor network of excitatory interneurons: a simulation study

Locomotor burst generation is simulated using a full-scale network model of the unilateral excitatory interneuronal population. Earlier small-scale models predicted that a population of excitatory neurons would be sufficient to produce burst activity, and this has recently been experimentally confirmed. Here we simulate the hemicord activity induced under various experimental conditions, including pharmacological activation by NMDA and AMPA as well as electrical stimulation. The model network comprises a realistic number of cells and synaptic connectivity patterns. Using similar distributions of cellular and synaptic parameters, as have been estimated experimentally, a large variation in dynamic characteristics like firing rates, burst, and cycle durations were seen in single cells. On the network level an overall rhythm was generated because the synaptic interactions cause partial synchronization within the population. This network rhythm not only emerged despite the distributed cellular parameters but relied on this variability, in particular, in reproducing variations of the activity during the cycle and showing recruitment in interneuronal populations. A slow rhythm (0.4–2 Hz) can be induced by tonic activation of NMDA-sensitive channels, which are voltage dependent and generate depolarizing plateaus. The rhythm emerges through a synchronization of bursts of the individual neurons. A fast rhythm (4–12 Hz), induced by AMPA, relies on spike synchronization within the population, and each burst is composed of single spikes produced by different neurons. The dynamic range of the fast rhythm is limited by the ability of the network to synchronize oscillations and depends on the strength of synaptic connections and the duration of the slow after hyperpolarization. The model network also produces prolonged bouts of rhythmic activity in response to brief electrical activations, as seen experimentally. The mutual excitation can sustain long-lasting activity for a realistic set of synaptic parameters. The bout duration depends on the strength of excitatory synaptic connections, the level of persistent depolarization, and the influx of Ca²⁺ ions and activation of Ca²⁺-dependent K⁺ current.     

Computer control of pH and DO in a laboratory fermenter using a neural network technique

In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good.     

Activity of lumbar interneurons during fictitious locomotion in thalamic cats

Unit activity of the lumbar interneurons was recorded in thalamic cats during fictitious locomotion. Neurons whose activity was modulated in the rhythm of fictitious locomotion were found in the lateral parts of the intermediate zone of gray matter and ventral horn. Of these neurons, 41.2% were activated mainly in the phase of "flexion," 48.5% in the phase of extension, and 10.3% in both phases. Neurons with tonically increasing or decreasing activity during rhythmic discharges and neurons whose activity was unchanged during fictitious locomotion also were observed. During later discharges all these neurons were similarly activated, although a depth of modulation of unit activity was lower than during fictitious locomotion. Afferent inputs to the recorded interneurons also were studied. The neuronal organization of the spinal locomotor generator is discussed on the basis of these results.A. A. Bogomolets Institute of Physiology, Academy of Sciences of the Ukrainian SSR, Kiev. Translated from Neirofiziologiya, Vol. 11, No. 4, pp. 329–338, July–August, 1979.     

Intersegmental coordinating system of the lamprey central pattern generator for locomotion

Summary In the lamprey,Ichthyomyzon unicuspis, the wave of activity required for normal swimming movements can be generated by a central pattern generator (CPG) residing in the spinal cord. A constant phase coupling between spinal segments can be organized by intersegmental coordinating neurons intrinsic to the cord. The rostral and caudal segmental oscillators of the CPG have different preferred frequencies when separated from each other. Therefore the system must maintain the segmental oscillators of the locomotor CPG at a single common frequency and with the proper relative timing. Using selective lesions and a split-bath, it is demonstrated that the coordinating system is comprised of at least 3 subsystems, short-axon systems in the lateral and medial tracts and a long axon system in the lateral tracts. Each alone can sustain relatively stable coordinated activity.Abbreviations CPG central pattern generator - NMDA N-methyl-D-aspartate - VR ventral root     

Assessment of human locomotion by using an insole measurement system and artificial neural networks

A new method for measuring and characterizing free-living human locomotion is presented. A portable device was developed to objectively record and measure foot-ground contact information in every step for up to 24h. An artificial neural network (ANN) was developed to identify the type and intensity of locomotion. Forty subjects participated in the study. The subjects performed level walking, running, ascending and descending stairs at slow, normal and fast speeds determined by each subject, respectively. The device correctly identified walking, running, ascending and descending stairs (accuracy 98.78%, 98.33%, 97.33%, and 97.29% respectively) among different types of activities. It was also able to determine the speed of walking and running. The correlation between actual speed and estimated speed is 0.98, p< 0.0001. The average error of walking and running speed estimation is -0.050+/-0.747 km/h (mean +/- standard deviation). The study has shown the measurement of duration, frequency, type, and intensity of locomotion highly accurate using the new device and an ANN. It provides an alternative tool to the use of a gait lab to quantitatively study locomotion with high accuracy via a small, light and portable device, and to do so under free-living conditions for the clinical applications.     

Computer simulation of the neural discharge carried by the abducens nerve during eye fixation in the cat

The neural signal carried by the abducens nerve during eye fixations was simulated. The neural discharge was defined by the number of spikes carried by the abducens nerve within each ms. Calculations were based on real neurophysiological data. The computed neural signal showed frequency histograms and variance/mean ratios typical of Poisson distribution. A peak was obtained in the power spectral density function of the simulated neural signal. This peak appeared in the frequency range corresponding to the firing rate of single motoneurons for each eye position. It remained as a broad spectral peak after filtering by a second-order differential equation simulating the ocular mechanics. The obtained spectra are similar to the described power spectral density function of eye position recordings. Present results add evidence of a possible neural basis for ocular tremor.     

Numerical analysis of a unique mode of locomotion: vertical climbing by Pacific lamprey

Pacific lampreys are capable of climbing vertical wetted surfaces through a two-phase (bending and stretching) locomotion mode using the oral disc for adherence. We investigate the physical mechanism and performance of this process by using a continuous beam model. Two mechanisms, one akin to the jumping process and the other related to the fast stretching of the body, have been identified. This locomotion mode may inspire biomimetic designs of anguilliform swimming devices capable of overcoming steep obstacles. By using a genetic algorithm simulation we identify the combination of kinematic parameters corresponding to optimal efficiency (defined as the gravitational potential energy gained in each climbing step divided by the energy spent to activate the motion). These parameters are similar to laboratory observations of lamprey motion, suggesting that this type of locomotion has been optimized for maximum efficiency through evolution.