Monitoring Spiking Activity of Many Individual Neurons in Invertebrate Ganglia

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

The development of transgenic invertebrate preparations in which the activity of specifiable sets of neurons can be recorded and manipulated with light represents a revolutionary advance for studies of the neural basis of behavior. However, a downside of this development is its tendency to focus investigators on a very small number of "designer" organisms (e.g., C. elegans and Drosophila), potentially negatively impacting the pursuit of comparative studies across many species, which is needed for identifying general principles of network function. The present article illustrates how optical recording with voltage-sensitive dyes in the brains of non-transgenic gastropod species can be used to rapidly (i.e., within the time course of single experiments) reveal features of the functional organization of their neural networks with single-cell resolution. We outline in detail the dissection, staining, and recording methods used by our laboratory to obtain action potential traces from dozens to ~150 neurons during behaviorally relevant motor programs in the CNS of multiple gastropod species, including one new to neuroscience - the nudibranch Berghia stephanieae. Imaging is performed with absorbance voltage-sensitive dyes and a 464-element photodiode array that samples at 1,600 frames/second, fast enough to capture all action potentials generated by the recorded neurons. Multiple several-minute recordings can be obtained per preparation with little to no signal bleaching or phototoxicity. The raw optical data collected through the methods described can subsequently be analyzed through a variety of illustrated methods. Our optical recording approach can be readily used to probe network activity in a variety of non-transgenic species, making it well suited for comparative studies of how brains generate behavior.

A novel method for quantifying periodicity and time delay in dynamic neural networks using unstable subaction potential threshold depolarizations

Techniques to identify and correlate the propagation of electrical signals (like action potentials) along neural networks are well described, using multisite recordings. In these cases, the waveform of action potentials is usually relatively stable and discriminating relevant electrical signals straightforward. However, problems can arise when attempting to identify and correlate the propagation of signals when their waveforms are unstable (e.g., fluctuations in amplitude or time course). This makes correlation of the degree of synchronization and time lag between propagating electrical events across two or more recording sites problematic. Here, we present novel techniques for the determination of the periodicity of electrical signals at individual sites. When recording from two independent sites, we present novel analytical techniques for joint determination of periodicity and time delay. The techniques presented exploit properties of the cross-correlation function, rather than utilizing the time lag at which the cross-correlation function is maximized. The approach allows determination of directionality of the spread of excitation along a neural network based on measurements of the time delay between recording sites. This new method is particularly applicable to analysis of signals in other biological systems that have unstable characteristics in waveform that show dynamic variability.NEW & NOTEWORTHY The determination of frequency(s) at which two sources are synchronized, and relative time delay between them, is a fundamental problem for a wide a range of signal-processing applications. In this methodology paper, we present novel procedures for periodicity estimation for single time series and joint periodicity and time delay estimation for two time series. The methods use properties of the cross-correlation function rather than the cross-correlation function explicitly.

Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics

Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed in vivo may therefore be a result of evolutionary selection for optimal information encoding and expect the methodology developed to be generalizable for alternative model networks as well as experimental paradigms.

Serial-section atlas of the Tritonia pedal ganglion

The pedal ganglion of the nudibranch gastropod Tritonia diomedea has been the focus of neurophysiological studies for more than 50 yr. These investigations have examined the neural basis of behaviors as diverse as swimming, crawling, reflex withdrawals, orientation to water flow, orientation to the earth's magnetic field, and learning. Despite this sustained research focus, most studies have confined themselves to the layer of neurons that are visible on the ganglion surface, leaving many neurons, which reside in deeper layers, largely unknown and thus unstudied. To facilitate work on such neurons, the present study used serial-section light microscopy to generate a detailed pictorial atlas of the pedal ganglion. One pedal ganglion was sectioned horizontally at 2-µm intervals and another vertically at 5-µm intervals. The resulting images were examined separately or combined into stacks to generate movie tours through the ganglion. These were also used to generate 3D reconstructions of individual neurons and rotating movies of digitally desheathed whole ganglia to reveal all surface neurons. A complete neuron count of the horizontally sectioned ganglion yielded 1,885 neurons. Real and virtual sections from the image stacks were used to reveal the morphology of individual neurons, as well as the major axon bundles traveling within the ganglion to and between its several nerves and connectives. Extensive supplemental data are provided, as well as a link to the Dryad Data Repository site, where the complete sets of high-resolution serial-section images can be downloaded. NEW & NOTEWORTHY Because of the large size and relatively low numbers of their neurons, gastropod mollusks are widely used for investigations of the neural basis of behavior. Most studies, however, focus on the neurons visible on the ganglion surface, leaving the majority, located out of sight below the surface, unexamined. The present light microscopy study generates the first detailed visual atlas of all neurons of the highly studied Tritonia pedal ganglion.