Neural circuit flexibility in a small sensorimotor system

Neuronal circuits underlying rhythmic behaviors (central pattern generators: CPGs) can generate rhythmic motor output without sensory input. However, sensory input is pivotal for generating behaviorally relevant CPG output. Here we discuss recent work in the decapod crustacean stomatogastric nervous system (STNS) identifying cellular and synaptic mechanisms whereby sensory inputs select particular motor outputs from CPG circuits. This includes several examples in which sensory neurons regulate the impact of descending projection neurons on CPG circuits. This level of analysis is possible in the STNS due to the relatively unique access to identified circuit, projection, and sensory neurons. These studies are also revealing additional degrees of freedom in sensorimotor integration that underlie the extensive flexibility intrinsic to rhythmic motor systems.     

Premotor neurons in the feeding system of Aplysia californica

Central pattern generator (CPG) circuits control cyclic motor output underlying rhythmic behaviors. Although there have been extensive behavioral and cellular studies of food-induced feeding arousal as well as satiation in Aplysia, very little is known about the neuronal circuits controlling rhythmic consummatory feeding behavior. However, recent studies have identified premotor neurons that initiate and maintain buccal motor programs underlying ingestion and egestion in Aplysia. Other newly identified neurons receive synaptic input from feeding CPGs and in turn synapse with and control the output of buccal motor neurons. Some of these neurons and their effects within the buccal system are modulated by endogenous neuropeptides. With this information we can begin to understand how neuronal networks control buccal motor output and how their activity is modulated to produce flexibility in observed feeding behavior.     

Conditioned reflex: detector and command neuron

Conditioned reflex is characterized by plasticity resulting in a bilateral selective input-output linking. In simple nervous systems, input stimuli are represented by selective detectors connected with command neurons through plastic synapses strengthened during associative learning and weakened during extinction. The process of associative learning is due to temporal coincidence of excitation in both detector and command neurons. Short-term memory within a plastic synapses is mediated by phosphorilation of postsynaptic receptor molecules not requiring protein synthesis. Long-term synaptic memory parallels expression of immediate early genes that mediates structural gene expression and protein synthesis. A simple detector-command neuron association becomes more complex in the course of evolution. Input mechanism is supplemented with predetector interneurons preceding detectors. Detector selectively tuned to specific input stimulus is converging on a command neuron constitute selectivity mechanism for conditioned reflexes to complex stimuli. The complication also concerns the output mechanisms. Command neurons become more specialized, and an additional link of premotor interneurons is incorporated between command neurons and motor neurons. Via synapses, the command neurons can produce excitation in a particular set of premotor neurons controlling a specific set of motor neurons responsible for behavioral act configuration. Specialization of command neurons in combination with premotor neuron structures increases the variability of outputs. Conditioned reflexes with more complex inputs and more flexible outputs determine the diversity of acquired behaviors.     

Regulation of afferent transmission in the feeding circuitry of Aplysia

Although feeding in Aplysia is mediated by a central pattern generator (CPG), the activity of this CPG is modified by afferent input. To determine how afferent activity produces the widespread changes in motor programs that are necessary if behavior is to be modified, we have studied two classes of feeding sensory neurons. We have shown that afferent-induced changes in activity are widespread because sensory neurons make a number of synaptic connections. For example, sensory neurons make monosynaptic excitatory connections with feeding motor neurons. Sensori-motor transmission is, however, regulated so that changes in the periphery do not disrupt ongoing activity. This results from the fact that sensory neurons are also electrically coupled to feeding interneurons. During motor programs sensory neurons are, therefore, rhythmically depolarized via central input. These changes in membrane potential profoundly affect sensori-motor transmission. For example, changes in membrane potential alter spike propagation in sensory neurons so that spikes are only actively transmitted to particular output regions when it is behaviorally appropriate. To summarize, afferent activity alters motor output because sensory neurons make direct contact with motor neurons. Sensori-motor transmission is, however, centrally regulated so that changes in the periphery alter motor programs in a phase-dependent manner.     

Neuropeptide modulation of microcircuits

Neuropeptides provide functional flexibility to microcircuits, their inputs and effectors by modulating presynaptic and postsynaptic properties and intrinsic currents. Recent studies have relied less on applied neuropeptide and more on their neural release. In rhythmically active microcircuits (central pattern generators, CPGs), recent studies show that neuropeptide modulation can enable particular activity patterns by organizing specific circuit motifs. Neuropeptides can also modify microcircuit output indirectly, by modulating circuit inputs. Recently elucidated consequences of neuropeptide modulation include changes in motor patterns and behavior, stabilization of rhythmic motor patterns and changes in CPG sensitivity to sensory input. One aspect of neuropeptide modulation that remains enigmatic is the presence of multiple peptide family members in the same nervous system and even the same neurons.     

Systems-level modeling of neuronal circuits for leech swimming

This paper describes a mathematical model of the neuronal central pattern generator (CPG) that controls the rhythmic body motion of the swimming leech. The systems approach is employed to capture the neuronal dynamics essential for generating coordinated oscillations of cell membrane potentials by a simple CPG architecture with a minimal number of parameters. Based on input/output data from physiological experiments, dynamical components (neurons and synaptic interactions) are first modeled individually and then integrated into a chain of nonlinear oscillators to form a CPG. We show through numerical simulations that the values of a few parameters can be estimated within physiologically reasonable ranges to achieve good fit of the data with respect to the phase, amplitude, and period. This parameter estimation leads to predictions regarding the synaptic coupling strength and intrinsic period gradient along the nerve cord, the latter of which agrees qualitatively with experimental observations.     

Long-term potentiation and depression induced by a stochastic conditioning of a model synapse.

Protracted presynaptic activity can induce long-term potentiation (LTP) or long-term depression (LTD) of the synaptic strength. However, virtually all the experiments testing how LTP and LTD depend on the conditioning input are carried out with trains of stimuli at constant frequencies, whereas neurons in vivo most likely experience a stochastic variation of interstimulus intervals. We used a computational model of synaptic transmission to test if and to what extent the stochastic fluctuations of an input signal could alter the probability to change the state of a synapse. We found that, even if the mean stimulation frequency was maintained constant, the probability to induce LTD and LTP could be a function of the temporal variation of the input activity. This mechanism, which depends only on the statistical properties of the input and not on the onset of additional biochemical mechanisms, is not usually considered in the experiments, but it could have an important role to determine the amount of LTP/LTD induction in vivo. In response to a change in the distribution of the interstimulus intervals, as measured by the coefficient of variation, a synapse could be easily adapted to inputs that might require immediate attention, with a shift of the input thresholds required to elicit LTD or LTP, which are restored to their initial conditions as soon as the input pattern returns to the original temporal distribution.     

Temporal sub units in dendritic trees

This simulation study examines the possibility that dendritic sub units can be defined according to temporal aspects in the timing of populations of synaptic inputs. A two cell model with passive dendritic trees is used, which is subject to both common and independent synaptic inputs, the presence of common synaptic input results in a tendency for correlated firing in the two cell model. The strength of this correlation is used to measure the efficacy of the common synaptic inputs in modulating the output discharge of each neurone. Our results suggest that a small fraction of the total synaptic input can effectively modulate the timing of output spikes, this phenomenon is not dependent on the physical location of the inputs on the dendritic tree. This phenomenon depends on the presence of temporal correlation between the pre-synaptic spike trains that provide the common input. We propose to refer to these as temporal sub units.     

Cellular properties and modulation of the stomatogastric ganglion neurons of a stomatopod,Squilla oratoria

Cellular properties and modulation of the identified neurons of the posterior cardiac plate-pyloric system in the stomatogastric ganglion of a stomatopod, Squilla oratoria, were studied electrophysiologically. Each class of neurons involved in the cyclic bursting activity was able to trigger an endogenous, slow depolarizing potential (termed a driver potential) which sustained bursting. Endogenous oscillatory properties were demonstrated by the phase reset behavior in response to brief stimuli during ongoing rhythm. The driver potential was produced by membrane voltage-dependent activation and terminated by an active repolarization. Striking enhancement of bursting properties of all the cell types was induced by synaptic activation via extrinsic nerves, seen as increases in amplitude or duration of driver potentials, spiking rate during a burst, and bursting rate. The motor pattern produced under the influence of extrinsic modulatory inputs continued for a long time, relative to that in the absence of activation of modulatory inputs. Voltage-dependent conductance mechanisms underlying postinhibitory rebound and driver potential responses were modified by inputs. It is concluded that endogenous cellular properties, as well as synaptic circuitry and extrinsic inputs, contribute to generation of the rhythmic motor pattern, and that a motor system and its component neurons have been highly conserved during evolution between stomatopods and decapods.Abbreviations AB anterior burster neuron - CoG commissural ganglion - CPG central pattern generator - lvn lateral ventricular nerve - OG oesophageal ganglion - pcp posterior cardiac plate - PCP pcp constrictor neuron - PD pyloric dilator neuron - PY pyloric constrictor neuron - son superior oesophageal nerve - STG stomatogastric ganglion - stn stomatogastric nerve     

Recurrent Network Models for Perfect Temporal Integration of Fluctuating Correlated Inputs

Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.     

Neuromodulation of central pattern generators in invertebrates and vertebrates

Central pattern generators are subject to extensive modulation that generates flexibility in the rhythmic outputs of these neural networks. The effects of neuromodulators interact with one another, and modulatory neurons are themselves often subject to modulation, enabling both higher order control and indirect interactions among central pattern generators. In addition, modulators often directly mediate the interactions between functionally related central pattern generators. In systems such as the vertebrate respiratory central pattern generator, multiple pacemaker types interact to produce rhythmic output. Modulators can then alter the relative contributions of the different pacemakers, leading to substantial changes in motor output and hence to different behaviors. Surprisingly, substantial changes in some aspects of the circuitry of a central pattern generator, such as a several-fold increase in synaptic strength, can sometimes have little effect on the output of the CPG, whereas other changes have profound effects.     

Neural control of ventilation in the shore crab, Carcinus maenas. II. Frequency-modulating interneurons

1. We have identified a class of nonspiking interneurons which can control the frequency of ventilation in a graded manner. These frequency modulating interneurons (FMis) also receive synaptic inputs in-phase with the ventilatory motor output providing a functional positive feedback loop in the ventilatory system. The class of FMis is composed of three morphologically and physiologically distinct interneurons, FMi1, FMi2 and FMi3. 2. Depolarization of FMi1 increases the rate of ventilation, while hyperpolarization decreases the rate (Fig. 1). This control is restricted to a single ventilatory central pattern generator (CPG), (Fig. 2), although FMi1 sends processes into the neuropils of both hemiganglionic CPGs (Fig. 3). 3. Hyperpolarization of FMi2 increases the rate of both ventilatory CPGs while depolarization of this cell slows and eventually arrests the rhythm (Figs. 5 and 6). FMi2 receives a synaptic input correlated with the motor output of each of the ventilatory CPGs (Fig. 4). During periods of reversed ventilation, this cell is abruptly hyperpolarized and continues to be driven in-phase with the ventilatory motor output (Fig. 7). 4. Hyperpolarization of FMi3 increases the rate of ventilation and depolarization decreases the rate of ventilation produced by both CPGs (Fig. 10). This control of the ventilatory rate by FMi3 is graded (Fig. 11). There is no apparent change in the membrane potential of FMi3 during reversed ventilation and it is morphologically distinct from FMi2. 5. FMi2 and FMi3 may be involved in the switch in ventilatory motor pattern from forward to reversed ventilation. Hyperpolarization of FMi2 and depolarization of FMi3 can elicit bouts of reversed ventilation from both CPGs (Fig. 13). 6. These results suggest that the FM interneurons act in parallel to control the frequency of ventilation and may act as integrating elements between spiking 'command' fibers in the circumesophageal connectives and the nonspiking interneurons of the ventilatory CPG.     

Entrainment, Instability, Quasi-periodicity, and Chaos in a Compound Neural Oscillator

We studied the dynamical behavior of a class of compound central pattern generator (CPG) models consisting of a simple neural network oscillator driven by both constant and periodic inputs of varying amplitudes, frequencies, and phases. We focused on a specific oscillator composed of two mutually inhibiting types of neuron (inspiratory and expiratory neurons) that may be considered as a minimal model of the mammalian respiratory rhythm generator. The simulation results demonstrated how a simple CPG model— with a minimum number of neurons and mild nonlinearities— may reproduce a host of complex dynamical behaviors under various periodic inputs. In particular, the network oscillated spontaneously only when both neurons received adequate and proportionate constant excitations. In the presence of a periodic source, the spontaneous rhythm was overriden by an entrained oscillation of varying forms depending on the nature of the source. Stable entrained oscillations were inducible by two types of inputs: (1) anti-phase periodic inputs with alternating agonist-antagonist drives to both neurons and (2) a single periodic drive to only one of the neurons. In-phase inputs, which exert periodic drives of similar magnitude and phase relationships to both neurons, resulted in varying disruptions of the entrained oscillations including magnitude attenuation, harmonic and phase distortions, and quasi-periodic interference. In the absence of significant phasic feedback, chaotic motion developed only when the CPG was driven by multiple periodic inputs. Apneic episodes with repetitive alternation of active (intrinsic oscillation) and inactive (cessation of oscillation) states developed when the network was driven by a moderate periodic input of low frequency. %and amplitudes of intermediate strength, Similar results were demonstrated in other, more complex oscillator models (that is, half-center oscillator and three-phase respiratory network model). These theoretical results may have important implications in elucidating the mechanisms of rhythmogenesis in the mature and developing respiratory CPG as well as other compound CPGs in mammalian and invertebrate nervous systems.     

Optimization of Input Patterns and Neuronal Properties to Evoke Motor Neuron Synchronization

The study used a computational approach to identify combinations of synaptic input timing and strength superimposed on a variety of active dendritic conductances that could evoke similar levels of motor unit synchronization in model motor neurons. Two motor neurons with low recruitment thresholds but different passive properties were modeled using GENESIS software. The timing and strength of synaptic inputs and the density of dendritic ion channels were optimized with a genetic algorithm to produce a set of target discharge times. The target times were taken from experimental recordings made in a human subject and had the synchronization characteristics that are commonly observed in hand muscles. The main finding was that the two parameters with the highest association to output synchrony were the ratio of inward-to-outward ionic conductances (r = 0.344; P = 0.003) and the degree of correlation in inhibitory inputs (r = 0.306; P = 0.009). Variation in the amount of correlation in the excitatory input was not positively correlated with variation in output synchrony. Further, the variability in discharge rate of the model neurons was positively correlated with the density of N-type calcium channels in the dendritic compartments (r = 0.727; P < 0.001 and r = 0.533; P < 0.001 for the two cells). This result suggests that the experimentally observed correlation between discharge variability and synchronization is caused by an increase in fast inward ionic conductances in the dendrites. Given the moderate level of correlation between output synchrony and each of the model parameters, especially at moderate levels of synchrony (E < 0.09 and CIS < 1.0), the results suggest caution in ascribing mechanisms to observations of motor unit synchronization.     

Presynaptic control of neurones in pattern-generating networks

Recent studies have revealed presynaptic influences on neurones that participate in rhythmic motor patterns. Although there is still little direct information about the effects of these inputs at presynaptic terminals, their functional consequences are being unraveled. These presynaptic influences gate sensory input to pattern-generating networks and locally alter the synaptic strength and/or the activity pattern of network neurones.     

Emerging methodologies for the study of hypothalamic gonadotropin-releasing-hormone (GnRH) neurons

Gonadotropin-releasing-hormone (GnRH) neurons form part of a central neural oscillator that controls sexual reproduction through intermittent release of the GnRH peptide. Activity of GnRH neurons, and by extension release of GnRH, has been proposed to reflect intrinsic properties and synaptic input of GnRH neurons. To study GnRH neurons, we used traditional electrophysiology and computational methods. These emerging methodologies enhance the elucidation of processing in GnRH neurons. We used dynamic current-clamping to understand how living GnRH somata process input from glutamate and GABA, two key neurotransmitters in the neuroendocrine hypothalamus. In order to study the impact of synaptic integration in dendrites and neuronal morphology, we have developed full-morphology models of GnRH neurons. Using dynamic clamping, we have demonstrated that small-amplitude glutamatergic currents can drive repetitive firing in GnRH neurons. Furthermore, application of simulated GABAergic synapses with a depolarized reversal potential have revealed two functional subpopulations of GnRH neurons: one population in which GABA chronically depolarizes membrane potential (without inducing action potentials) and a second population in which GABAergic excitation results in slow spiking. Finally, when AMPA-type and GABA-type simulated inputs are applied together, action potentials occur when the AMPA-type conductance occurs during the descending phase of GABAergic excitation and at the nadir of GABAergic inhibition. Compartmental computer models have shown that excitatory synapses at >300 microns from somtata are unable to drive spiking with purely passive dendrites. In models with active dendrites, distal synapses are more efficient at driving spiking than somatic inputs. We then used our models to extend the results from dynamic current clamping at GnRH somata to distribute synaptic inputs along the dendrite. We show that propagation delays for dendritic synapses alter synaptic integration in GnRH neurons by widening the temporal window of interaction for the generation of action potentials. Finally, we have shown that changes in dendrite morphology can modulate the output of GnRH neurons by altering the efficacy of action potential generation in response to after-depolarization potentials (ADPs). Taken together, the methodologies of dynamic current clamping and multi-compartmental modeling can make major contributions to the study of synaptic integration and structure-function relationships in hypothalamic GnRH neurons. Use of these methodological approaches will continue to provide keen insights leading to conceptual advances in our understanding of reproductive hormone secretion in normal and pathological physiology and open the door to understanding whether the mechanisms of pulsatile GnRH release are conserved across species.     

Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments

Accurately describing synaptic interactions between neurons and how interactions change over time are key challenges for systems neuroscience. Although intracellular electrophysiology is a powerful tool for studying synaptic integration and plasticity, it is limited by the small number of neurons that can be recorded simultaneously in vitro and by the technical difficulty of intracellular recording in vivo. One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons. These techniques have the potential to reveal large-scale connectivity structure based on the spike timing alone. However, the interpretation of functional connectivity is often approximate, since only a small fraction of presynaptic inputs are typically observed. Here we use in vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled. In experiments with partially-defined input, we inject a single simulated input with known amplitude on a background of fluctuating noise. In a fully-defined input paradigm, we then control the synaptic weights and timing of many simulated presynaptic neurons. By analyzing the firing of neurons in response to these artificial inputs, we ask 1) How does functional connectivity inferred from spikes relate to simulated synaptic input? and 2) What are the limitations of connectivity inference? We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions. Detectability depends on input amplitude and output firing rate, and excitatory inputs are detected more readily than inhibitory. Moreover, as we model increasing numbers of presynaptic inputs, we are able to estimate connection strengths more accurately and detect the presence of connections more quickly. These results illustrate the possibilities and outline the limits of inferring synaptic input from spikes.     

Partly shared spinal cord networks for locomotion and scratching

Animals produce a variety of behaviors using a limited number of muscles and motor neurons. Rhythmic behaviors are often generated in basic form by networks of neurons within the central nervous system, or central pattern generators (CPGs). It is known from several invertebrates that different rhythmic behaviors involving the same muscles and motor neurons can be generated by a single CPG, multiple separate CPGs, or partly overlapping CPGs. Much less is known about how vertebrates generate multiple, rhythmic behaviors involving the same muscles. The spinal cord of limbed vertebrates contains CPGs for locomotion and multiple forms of scratching. We investigated the extent of sharing of CPGs for hind limb locomotion and for scratching. We used the spinal cord of adult red-eared turtles. Animals were immobilized to remove movement-related sensory feedback and were spinally transected to remove input from the brain. We took two approaches. First, we monitored individual spinal cord interneurons (i.e., neurons that are in between sensory neurons and motor neurons) during generation of each kind of rhythmic output of motor neurons (i.e., each motor pattern). Many spinal cord interneurons were rhythmically activated during the motor patterns for forward swimming and all three forms of scratching. Some of these scratch/swim interneurons had physiological and morphological properties consistent with their playing a role in the generation of motor patterns for all of these rhythmic behaviors. Other spinal cord interneurons, however, were rhythmically activated during scratching motor patterns but inhibited during swimming motor patterns. Thus, locomotion and scratching may be generated by partly shared spinal cord CPGs. Second, we delivered swim-evoking and scratch-evoking stimuli simultaneously and monitored the resulting motor patterns. Simultaneous stimulation could cause interactions of scratch inputs with subthreshold swim inputs to produce normal swimming, acceleration of the swimming rhythm, scratch-swim hybrid cycles, or complete cessation of the rhythm. The type of effect obtained depended on the level of swim-evoking stimulation. These effects suggest that swim-evoking and scratch-evoking inputs can interact strongly in the spinal cord to modify the rhythm and pattern of motor output. Collectively, the single-neuron recordings and the results of simultaneous stimulation suggest that important elements of the generation of rhythms and patterns are shared between locomotion and scratching in limbed vertebrates.     

Short-term memory of motor network performance via activity-dependent potentiation of Na+/K+ pump function

Brain networks memorize previous performance to adjust their output in light of past experience. These activity-dependent modifications generally result from changes in synaptic strengths or ionic conductances, and ion pumps have only rarely been demonstrated to play a dynamic role. Locomotor behavior is produced by central pattern generator (CPG) networks and modified by sensory and descending signals to allow for changes in movement frequency, intensity, and duration, but whether or how the CPG networks recall recent activity is largely unknown. In Xenopus frog tadpoles, swim bout duration correlates linearly with interswim interval, suggesting that the locomotor network retains a short-term memory of previous output. We discovered an ultraslow, minute-long afterhyperpolarization (usAHP) in network neurons following locomotor episodes. The usAHP is mediated by an activity- and sodium spike-dependent enhancement of electrogenic Na(+)/K(+) pump function. By integrating spike frequency over time and linking the membrane potential of spinal neurons to network performance, the usAHP plays a dynamic role in short-term motor memory. Because Na(+)/K(+) pumps are ubiquitously expressed in neurons of all animals and because sodium spikes inevitably accompany network activity, the usAHP may represent a phylogenetically conserved but largely overlooked mechanism for short-term memory of neural network function.