Biomedical subjects

Brian Mulloney

Publications and source records attributed to Brian Mulloney.

8 recordsLinked to original sources

PubMed2006

Not by spikes alone: responses of coordinating neurons and the swimmeret system to local differences in excitation.

Swimmeret coordinating neurons in the crayfish CNS collectively encode a detailed cycle-by-cycle report on features of the motor output to each swimmeret. This information coordinates the motor output that drives swimmeret movements. To see how coordinating neurons responded to forced changes in intersegmental phase, we used a split-bath, repeated-measures experimental design to expose different regions of isolated abdominal nerve cords to different levels of excitation. We present a quantitative description of the firing of power-stroke (PS) motor units and two kinds of coordinating interneurons, ASC(E) and DSC, recorded simultaneously from each swimmeret ganglion under uniform and nonuniform excitation. When anterior and posterior ganglia were excited differently, several parameters of the swimmeret motor pattern were affected. Strengths of PS bursts in each ganglion were determined by local excitation. The phase of PS bursts in neighboring ganglia changed at the excitation boundary. Coordinating neurons from the two ganglia closest to the excitation boundary were most affected by nonuniform excitation. ASC(E) neurons tracked the timing and duration of each PS burst in their home ganglion, but did not follow changes in PS burst strength. DSC neurons changed the duration, phase, and number of spikes per burst. We propose two models to explain these results. First, the period expressed under nonuniform conditions is the sum of local intersegmental latencies and these latencies are determined by local excitation. Second, the phase change at the excitation boundary is determined by local modulation of the targets of the intersegmental coordinating neurons, not by modulation of the coordinating neurons themselves.

Action Potentials↗

PubMed2005

Bursts of information: coordinating interneurons encode multiple parameters of a periodic motor pattern.

The limbs on different segments of the crayfish abdomen that drive forward swimming are directly controlled by modular pattern-generating circuits. These circuits are linked together by axons of identified coordinating interneurons. We described the distributions of these neurons in each abdominal ganglion and monitored their firing during expression of the swimming motor pattern. We analyzed the timing, the numbers of spikes, and the duration of each burst of spikes in these coordinating neurons. To see what information these neurons encoded, we correlated these parameters with the timing, durations, and strengths of bursts of spikes in motor axons from the same modules. During the power-stroke phase of each output cycle, the anterior-projecting neurons fired bursts of spikes that encoded information about the start-time, duration, and strength of each burst of spikes in power-stroke motor neurons from the same module. When the period and intensity of the motor output fluctuated, the bursts of spikes in these neurons tracked these fluctuations accurately. Each additional spike in these neurons signified an increase in the strength of the power-stroke burst. The posterior-projecting neurons that fired during the return-stroke phase encoded similar information about the return-stroke motor neurons. Although homologous neurons from different ganglia were qualitatively similar, neurons from posterior ganglia fired significantly more spikes per burst than those from more anterior ganglia, a segmental gradient that correlates with the normal posterior-to-anterior phase progression of limb movements. We propose that this gradient and a similar gradient in the durations of bursts in power-stroke motor neurons might reflect a hitherto-undetected difference in the excitation or intrinsic excitability of swimmeret modules in different segments.

Action Potentials↗

PubMed2005

A method to measure the strength of multi-unit bursts of action potentials.

Both the numbers of neurons that are active during multi-unit bursts of spikes and the frequencies with which individual neurons fire in these bursts can vary in response to changes in excitation. Here is a digital-filtering method that measures the strength of a burst of spikes by calculating the area of a polygon derived from the squared voltages that record the burst, and dividing this area by the burst's duration. The method was developed in the SigmaPlot environment, and makes use of the Fast-Fourier Transform functions provided in the SigmaPlot transform language. To test the method's performance, I constructed multi-unit bursts of spikes with known structure and calculated the strengths of these known bursts. To demonstrate the method's usefulness, I applied it to a train of 23 bursts of spikes in motor axons recorded during a spontaneous bout of patterned motor output. The measured strengths of these bursts varied 30-fold, and were well-correlated with the differences in the original recording. The results demonstrate that the method effectively measures burst strength independent of burst duration.

Action Potentials↗

PubMed2004

A multichannel electronic monitor of acoustic behaviors, and software to parse individual channels.

We designed an electronic device to monitor calling behavior in male crickets. The device and its associated software can record to disk the activity of as many as 16 individuals simultaneously. The data recorded contain detailed information about the temporal structure of each individual's calls. The temporal resolution achieved with an ordinary PC is good enough to detect and record the occurrence of every pulse of sound from each cricket. The resulting data files are efficient and compact, and so the system is appropriate for experiments lasting many days.

Acoustics↗

PubMed2003

Local commissural interneurons integrate information from intersegmental coordinating interneurons.

The information that coordinates movements of swimmerets on different segments of the crayfish abdomen is conducted by interneurons that originate in each abdominal ganglion. These interneurons project axons to neighboring ganglia and beyond. To discover the anatomy of these axons in their target ganglia, we used Neurobiotin and dextran-Texas Red microelectrodes to fill them near their targets. Coordinating axons coursed through these target ganglia close to the midline and extended only a few short branches that did not approach the lateral neuropils. Two of the three types of coordinating axons made direct synaptic connections with a class of local commissural interneurons that relayed the information to targets in the swimmeret pattern-generating circuits. These commissural interneurons, named here ComInt 1 neurons, followed a particular route to cross the midline and reach their targets. ComInt 1 neurons were nonspiking; they received EPSPs from the coordinating axons near the midline and transmitted this information to their targets in the lateral neuropils using graded transmission. The output of each ComInt 1 was restricted to a single local circuit and had opposite effects on the power-stroke and return-stroke motor neurons driven by that circuit. ComInt 1 neurons were direct postsynaptic targets of both descending and ascending coordinating axons that originated in other anterior and posterior ganglia. Because of phase differences in the impulses in these different coordinating axons, their signals arrived simultaneously at each ComInt 1. We discuss these findings in the context of alternative models of the intersegmental coordinating circuit.

Animals↗

PubMed2003

During fictive locomotion, graded synaptic currents drive bursts of impulses in swimmeret motor neurons.

During forward swimming, motor neurons that innervate each crayfish swimmeret fire periodic coordinated bursts of impulses. These bursts occur simultaneously in neurons that are functional synergists but alternate with bursts in their antagonists. These impulses ride on periodic oscillations of membrane potential that occur simultaneously in neurons of each type. A model of the local circuit that generates this motor pattern has been proposed. In this model, each motor neuron is driven alternately by excitatory and inhibitory synaptic currents from nonspiking local interneurons. I tested this model by perturbing individual interneurons and recording synaptic currents and changes in input resistance from each class of motor neuron. I also simulated the synaptic currents that would be observed in a cell subject to different patterns of presynaptic input. When the CNS was actively expressing the swimming motor pattern, changes in the membrane potential of individual local interneurons controlled firing of whole sets of motor neurons. Membrane currents in these motor neurons oscillated in phase with the motor output from their own local circuit. The phases of these oscillations differed in different functional classes of motor neurons. In neurons that could be clamped at the reversal potential of their outward currents, the model predicted that large periodic inward currents would be recorded. I observed no signs of periodic inward currents, even when the outward currents clearly had reversed. These results permit a simplification of the cellular model. They are discussed in the context of neural control of locomotion in crustacea and insects.

Animals↗

PubMed2003

Coordination of cellular pattern-generating circuits that control limb movements: the sources of stable differences in intersegmental phases.

Neuronal mechanisms in nervous systems that keep intersegmental phase lags the same at different frequencies are not well understood. We investigated biophysical mechanisms that permit local pattern-generating circuits in neighboring segments to maintain stable phase differences. We use a modified version of an existing model of the crayfish swimmeret system that is based on three known coordinating neurons and hypothesized intersegmental synaptic connections. Weakly coupled oscillator theory was used to derive coupling functions that predict phase differences between neurons in neighboring segments. We show how features controlling the size of the lag under simplified network configurations combine to create realistic lags in the full network. Using insights from the coupled oscillator theory analysis, we identify an alternative intersegmental connection pattern producing realistic stable phase differences. We show that the persistence of a stable phase lag to changes in frequency can arise from complementary effects on the network with ascending-only or descending-only intersegmental connections. To corroborate the numerical results, we experimentally constructed phase-response curves (PRCs) for two different coordinating interneurons in the swimmeret system by perturbing the firing of individual interneurons at different points in the cycle of swimmeret movement. These curves provide information about the contribution of individual intersegmental connections to the stable phase lag. We also numerically constructed PRCs for individual connections in the model. Similarities between the experimental and numerical PRCs confirm the plausibility of the network configuration that has been proposed and suggest that the same stabilizing balance present in the model underlies the normal phase-constant behavior of the swimmeret system.

Action Potentials↗

PubMed2003

Architectonics of crayfish ganglia.

The central nervous system of crayfish consists of a chain of segmental ganglia that are linked by cables of intersegmental axons. Each ganglion contains a highly-ordered core of longitudinal tracts, vertical tracts, commissures, and synaptic neuropils. We review from a technical perspective the history of the description of these ganglia, and recognize four episodes of progress. Each major innovation in anatomical methods has led to new insight into the structure and function of this nervous system, and new awareness of the structural patterns that are common to the CNS of all arthropods. Ganglia in different segments of the body differ in size, and appear to differ in anatomy. From a comparison of the structures of the cores of abdominal, thoracic, and subesophageal ganglia, we argue that this apparent difference is illusory. Rather, each of these ganglia is organized on the same plan, a plan also found in insect segmental ganglia. The apparent differences follow from longitudinal compression during development and from allometric growth of particular neuropils associated with innervation of the walking legs. Different authors have described the internal organization of ganglia in different segments, so we provide a cross-reference to the nomenclatures they have introduced. We compare the locations of cell bodies of motor neurons and accessory neurons that innervate different peripheral structures, and demonstrate double-labeling of certain GABAergic peripheral inhibitory neurons. Finally, we describe the construction of digital movies of serial sections of these ganglia, and discuss their utility.

Animals↗