Allen I. Selverston

The stomatogastric nervous system: structure and function of a small neural network.

Introduction 1. Gross anatomy of the stomatogastric system and experimental procedure 1.1. Anatomy 1.2. Experimental procedure 2. Behavior 2.1. Gastric mill 2.2. Pyloric cycle 2.3. Higher order control over stomach behavior 3. Pyloric system 4. Gastric system 5. Pyloric-gastric interactions 6. Modulation of stomatogastric activity 6.1. Sources of inputs 6.1.1. The commissural ganglia 6.1.2. The esophageal ganglion 6.2. Central connections of pyloric neurons 6.2.1. Pathways 6.2.2. Synaptic events 6.2.3. Esophagus rhythm inputs to pyloric neurons 6.2.4. Through fiber bursts 6.2.5. Distribution of the pyloric system 6.2.6. Summary 6.3. Inputs to the gastric system 6.3.1. Continuous gastric cycling in vitro 6.3.2. Input pathways 6.3.3. Rhythmic inputs 6.3.4. E neurons 6.3.5. LI neurons 6.3.6. P neurons 6.3.7. Esophagus rhythm modulation 6.3.8. IVN through fibers 6.3.9. Effects of sensory nerve stimulation 6.3.10. Summary and general conclusions 7. EX cells and their inputs 8. Cellular properties of stomatogastric neurons 8.1. Soma potentials 8.2. Bursting and non-bursting cells 8.3. Postinhibitory rebound 8.4. Reversal potentials 8.5. Gating of cell output 9. Mechanisms of rhythm generation 9.1. Types of mechanisms 9.2. Pyloric system 9.3. Gastric mill system 10. Computer network modeling and neuron reconstruction studies 10.

Synchronous Behavior of Two Coupled Biological Neurons

We report experimental studies of synchronization phenomena in a pair of biological neurons that interact through naturally occurring, electrical coupling. When these neurons generate irregular bursts of spikes, the natural coupling synchronizes slow oscillations of membrane potential, but not the fast spikes. By adding artificial electrical coupling we studied transitions between synchrony and asynchrony in both slow oscillations and fast spikes. We discuss the dynamics of bursting and synchronization in living neurons with distributed functional morphology. [S0031-9007(98)08008-9] The dynamics of many neural ensembles such as central pattern generators (CPGs) or thalamo-cortical circuits pose questions related to cooperative behavior of neurons. Individual neurons may show irregular behavior [1], while ensembles of different neurons can synchronize in order to process biological information [2] or to produce regular, rhythmical activity [3]. How do the irregular neurons synchronize? How do they inhibit noise and intrinsic fluctuations? What parameters of the ensemble are responsible for such synchronization and regularization? Answers to these and similar questions may be found through experiments that enable one to follow qualitatively the cooperative dynamics of neurons as intrinsic and synaptic parameters are varied. Despite their interest, these problems have not received extensive study. Results of such an experiment for a minimal ensemble of two coupled, living neurons are reported in this communication. The experiment was carried out on two electrically coupled neurons (the pyloric dilators, PD) from the pyloric CPG of the lobster stomatogastric ganglion [3]. Individually, these neurons can generate spiking-bursting activity that is irregular and seemingly chaotic. This activity pattern can be altered by injecting dc current (I1 and I2) into the neurons; see Fig. 1. In parallel to their natural coupling, we added artificial coupling by a dynamic current clamp device [7]. Varying these control parameters (offset current and artificial coupling), we found the following regimes of cooperative behavior. Natural coupling produces state-dependent synchronization; see Fig. 2. (i) When depolarized by positive dc current, both neurons fire a continuous pattern of synchronized spikes (Fig. 2d). (ii) With little or no applied current, the neurons fire spikes in irregular bursts: now the slow oscillations are well synchronized while spikes are not (Fig. 2a). Changing the magnitude and sign of electrical coupling restructures the cooperative dynamics. (iii) Increasing the strength of coupling produces complete synchronization of both irregular slow oscillations and fast spikes (see below). (iv) Compensating the natural coupling leads to the onset

Organization of the stomatogastric ganglion of the spiny lobster

SummaryThe Stomatogastric ganglion ofPanulirus interruptus contains about 30 neurons, and controls the movements of the lobsters stomach. When experimentally isolated, the ganglion continues to generate complex rhythmic patterns of activity in its motor neurons which are similar to those seen in intact animals.In this paper, we describe the synaptic organization of a group of six neurons which drive the stomachs lateral teeth (Figs. 2, 6). This group includes four motor neurons and two interneurons, all but one of which were recorded and stimulated with intracellular microelectrodes.One pair of synergistic motor neurons, LGN and MGN, are electrotonically coupled and reciprocally inhibitory (Figs. 9, 12). A second pair of synergistic motor neurons, the LPGNs, are antagonists of LGN and MGN. The LPGNs are electrotonically coupled (Fig. 14), and are both inhibited by LGN and MGN (Figs. 8, 11). The LPGNs inhibit MGN (Fig. 15) but not LGN. One of the two interneurons in the ganglion, Int 1, reciprocally inhibits both LGN and MGN (Figs. 10, 13). The other interneuron, Int 2, excites Int 1 and inhibits the LPGNs (Fig. 16). The synaptic connections observed in the ganglion are reflected in the spontaneous activity recorded from the isolated ganglion and from intact animals.From the known synaptic organization and observations on the physiological properties of each of the neurons, we have formulated some hypotheses about the pattern-generating mechanism. We found no evidence that any of the neurons are endogenous bursters.

Synchronized action of synaptically coupled chaotic model neurons

Experimental observations of the intracellular recorded electrical activity in individual neurons show that the temporal behavior is often chaotic. We discuss both our own observations on a cell from the stom-atogastric central pattern generator of lobster and earlier observations in other cells. In this paper we work with models of chaotic neurons, building on models by Hindmarsh and Rose for bursting, spiking activity in neurons. The key feature of these simplified models of neurons is the presence of coupled slow and fast subsystems. We analyze the model neurons using the same tools employed in the analysis of our experimental data. We couple two model neurons both electrotonically and electrochemically in inhibitory and excitatory fashions. In each of these cases, we demonstrate that the model neurons can synchronize in phase and out of phase depending on the strength of the coupling. For normal synaptic coupling, we have a time delay between the action of one neuron and the response of the other. We also analyze how the synchronization depends on this delay. A rich spectrum of synchronized behaviors is possible for electrically coupled neurons and for inhibitory coupling between neurons. In synchronous neurons one typically sees chaotic motion of the coupled neurons. Excitatory coupling produces essentially periodic voltage trajectories, which are also synchronized. We display and discuss these synchronized behaviors using two distance measures of the synchronization.

Invertebrate central pattern generator circuits

There are now a reasonable number of invertebrate central pattern generator (CPG) circuits described in sufficient detail that a mechanistic explanation of how they work is possible. These small circuits represent the best-understood neural circuits with which to investigate how cell-to-cell synaptic connections and individual channel conductances combine to generate rhythmic and patterned output. In this review, some of the main lessons that have appeared from this analysis are discussed and concrete examples of circuits ranging from single phase to multiple phase patterns are described. While it is clear that the cellular components of any CPG are basically the same, the topology of the circuits have evolved independently to meet the particular motor requirements of each individual organism and only a few general principles of circuit operation have emerged. The principal usefulness of small systems in relation to the brain is to demonstrate in detail how cellular infrastructure can be used to generate rhythmicity and form specialized patterns in a way that may suggest how similar processes might occur in more complex systems. But some of the problems and challenges associated with applying data from invertebrate preparations to the brain are also discussed. Finally, I discuss why it is useful to have well-defined circuits with which to examine various computational models that can be validated experimentally and possibly applied to brain circuits when the details of such circuits become available.

Extended dynamic clamp: controlling up to four neurons using a single desktop computer and interface

The dynamic clamp protocol allows an experimenter to simulate the presence of membrane conductances in, and synaptic connections between, biological neurons. Existing protocols and commercial ADC/DAC boards provide ready control in and between < or =2 neurons. Control at >2 sites is desirable when studying neural circuits with serial or ring connectivity. Here, we describe how to extend dynamic clamp control to four neurons and their associated synaptic interactions, using a single IBM-compatible PC, an ADC/DAC interface with two analog outputs, and an additional demultiplexing circuit. A specific C++ program, DYNCLAMP4, implements these procedures in a Windows environment, allowing one to change parameters while the dynamic clamp is running. Computational efficiency is increased by varying the duration of the input-output cycle. The program simulates < or =8 Hodgkin-Huxley-type conductances and < or =18 (chemical and/or electrical) synapses in < or =4 neurons and runs at a minimum update rate of 5 kHz on a 450 MHz CPU. (Increased speed is possible in a two-neuron version that does not need auxiliary circuitry). Using identified neurons of the crustacean stomatogastric ganglion, we illustrate on-line parameter modification and the construction of three-member synaptic rings.

Interacting biological and electronic neurons generate realistic oscillatory rhythms.

Small assemblies of neurons such as central pattern generators (CPG) are known to express regular oscillatory firing patterns comprising bursts of action potentials. In contrast, individual CPG neurons isolated from the remainder of the network can generate irregular firing patterns. In our study of cooperative behavior in CPGs we developed an analog electronic neuron (EN) that reproduces firing patterns observed in lobster pyloric CPG neurons. Using a tuneable artificial synapse we connected the EN bidirectionally to neurons of this CPG. We found that the periodic bursting oscillation of this mixed assembly depends on the strength and sign of the electrical coupling. Working with identified, isolated pyloric CPG neurons whose network rhythms were impaired, the EN/biological network restored the characteristic CPG rhythm both when the EN oscillations are regular and when they are irregular.

Learning algorithms for oscillatory networks with gap junctions and membrane currents

One of the most important problems for studying neural network models is the adjustment of parametas. Here we show how to formulate the problem as the minimization of the dilferslee betwen two limit cycles. The backprop%ation method for leaming algorithms is described .ss the application of grsdient descent to an eror fundion that computes this difference. A mathematical formulation is given that is applicable to any type of network model, and is applied to several models. For example, when learning in a network in which dl cells have a common, adjustable, bias current, the value of the bias ie adjustcd at a rate proportional to the difference between the sum of the target outputs and the sum of the actual outputs. When learning in a network of n cells where a target output is given for every cell. the learning algorithm splits into R indepndent leaming algorithms, one per cell. For networks containing gap junctions, a gap junction is modelled D conductance times the potential difference between the two adjacent cells. The requirement that a conductme g munt be positive is enionzed by replacing g by a functbn pos(g*) whose value is always positive, for example cxp(O.lg*), and deriving an algorithm that adjusts the parameta g* in place of g. When target output is specified for every cell in a network with gap junctions. the learning algorithm splits into fewer independent componeds, one for each gap-mnneaed subset of the network. The lemming algorithm for II gspmnneeted set of cells cannot be paralklized further. As a find example, a leaming algwithm is derived for a mutually inhibitory twc- cell network in which each cell has a membrane current. This generalized approach to backpropagation allows one to derive a learning algorithm for alntoat any model neural network given in tem of differmtid equations. It will be an essential tool for adjusting parmetem in small but complex network models.

Antidromic Action Potentials Fail to Demonstrate Known Interactions between Neurons

An identified motor neuron in the stomatogastric ganglion of Panulirus interruptus inhibits four other motor neurons when it fires spontaneously or in response to depolarization of its soma. It does not inhibit these neurons when it is fired antidromically, although the attenuated antidromic spike is visible at its soma. These findings point out the difficulty of interpreting negative results from antidromic stimulation experiments and the importance of neuronal structure to the integrative activities of nervous systems.

Reliable circuits from irregular neurons: A dynamical approach to understanding central pattern generators

Central pattern generating neurons from the lobster stomatogastric ganglion were analyzed using new nonlinear methods. The LP neuron was found to have only four or five degrees of freedom in the isolated condition and displayed chaotic behavior. We show that this chaotic behavior could be regularized by periodic pulses of negative current injected into the neuron or by coupling it to another neuron via inhibitory connections. We used both a modified Hindmarsh-Rose model to simulate the neurons behavior phenomenologically and a more realistic conductance-based model so that the modeling could be linked to the experimental observations. Both models were able to capture the dynamics of the neuron behavior better than previous models. We used the Hindmarsh-Rose model as the basis for building electronic neurons which could then be integrated into the biological circuitry. Such neurons were able to rescue patterns which had been disabled by removing key biological neurons from the circuit.