StdpC: a modern dynamic clamp

With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC (“Spike timing-dependent plasticity Clamp”) allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable Hodgkin–Huxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and ‘voltage clamped’ a conductance based model cell from a passive resistor–capacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken

Multiple types of control by identified interneurons in a sensory-activated rhythmic motor pattern.

Modulatory interneurons that can drive central pattern generators (CPGs) are considered as good candidates for decision-making roles in rhythmic behaviors. Although the mechanisms by which such neurons activate their target CPGs are known in detail in many systems, their role in the sensory activation of CPG-driven behaviors is poorly understood. In the feeding system of the mollusc Lymnaea, one of the best-studied rhythmical networks, intracellular stimulation of either of two types of neuron, the cerebral ventral 1a (CV1a) and the slow oscillator (SO) cells, leads to robust CPG-driven fictive feeding patterns, suggesting that they might make an important contribution to natural food-activated behavior. In this paper we investigated this contribution using a lip-CNS preparation in which feeding was elicited with a natural chemostimulant rather than intracellular stimulation. We found that despite their CPG-driving capabilities, neither CV1a nor SO were involved in the initial activation of sucrose-evoked fictive feeding, whereas a CPG interneuron, N1M, was active first in almost all preparations. Instead, the two interneurons play important and distinct roles in determining the characteristics of the rhythmic motor output; CV1a by modulating motoneuron burst duration and SO by setting the frequency of the ongoing rhythm. This is an example of a distributed system in which (1) interneurons that drive similar motor patterns when activated artificially contribute differently to the shaping of the motor output when it is evoked by the relevant sensory input, and (2) a CPG rather than a modulatory interneuron type plays the most critical role in initiation of sensory-evoked rhythmic activity

Distinct Neuromuscular Patterns from a Single Motor Network

My thesis aimed to elucidate several aspects of motor circuit regulation and its impact on movement. It is well established that a single motor network can produce different output patterns in response to different inputs. However, in most model systems it remains challenging to identify the neurons comprising these networks and determine their role(s) in network operation, including whether each network neuron retains its role(s) when the network generates different output patterns. Also, most work on these circuits has occurred in the isolated nervous system, so little is known about how muscles respond to distinct neural outputs. I therefore aimed to address the cellular and synaptic mechanisms underlying these unresolved issues using the decapod crustacean stomatogastric nervous system. My work focused on a rhythmically active, network-driven motor circuit (central pattern generator [CPG] circuit) called the gastric mill (chewing) CPG in the crab stomatogastric ganglion. This circuit generates the gastric mill rhythm when activated by modulatory projection neurons (e.g. MCN1, CPN2) located in the commissural ganglia, and it is regulated by identified sensory feedback. I addressed and confirmed the hypothesis that, in the isolated nervous system, different extrinsic inputs can drive different gastric mill motor patterns. This enabled me to determine, for the first time in a network-driven motor circuit, that different motor patterns generated by the same motor circuit are paced by the same set of rhythm generator neurons. I further hypothesized and confirmed that these distinct motor patterns are retained at the level of at least some target muscles, and hence likely underlie different behavioral patterns. Lastly, I obtained data supporting the hypothesis that different extrinsic inputs distinctly modify the influence of a sensory feedback pathway on the relevant projection neurons (MCN1, CPN2), enabling the same sensory system to have different effects on different gastric mill rhythms. These results provide among the most detailed comparisons of how motor patterns generated by a single sensorimotor system are selected and regulated. The results thereby provide evidence for several novel cellular and synaptic mechanisms that expand our appreciation of the number of degrees of freedom available to even small sensorimotor systems

Probing the dynamics of identified neurons with a data-driven modeling approach

In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach

Distinct Circuit States Enable State-dependent Flexibility in a Rhythm Generating Network

My thesis aimed to elucidate general organizing principles underlying the modulation of neural circuits. These circuits are flexible constructs that, when modulated, can occupy many distinct states and produce different output patterns. Distinct circuit states can also produce the same output pattern in some cases. However, understanding the mechanisms and consequences of this latter phenomenon is impossible to achieve without the capability to observe and manipulate the cellular and synaptic properties of all circuit neurons. This work takes advantage of our detailed, cellular-level access to the central pattern generator (CPG) circuits found in the decapod crustacean stomatogastric nervous system, a specialized extension of the CNS dedicated to internal feeding-related behaviors. As CPGs are rhythmically active networks, much of this work focuses on the ability of such circuits to produce rhythmic output patterns (i.e. rhythm generation). Using this system, I found that distinct circuit states (configured by MCN1 projection neuron stimulation and CabPK peptide application) can enable comparable rhythm generation by recruiting distinct ionic conductances with overlapping functional roles (i.e. IMI and ITrans-LTS), each being regulated by synaptic inhibition to produce phasic excitatory drive to a pivotal circuit neuron (LG). In one case (MCN1 stimulation), the conductance is activated by a modulatory peptide transmitter whose release is regulated by presynaptic feedback inhibition. In the other case (CabPK application), the conductance has a slow inactivation property that is removed by hyperpolarization caused by synaptic inhibition. I also describe the consequences of having different circuit states that produce identical outputs by assaying their responses to the same, well-defined modulatory inputs - peptide (CCAP) hormone modulation and sensory feedback (GPR neuron). I found that hormonal modulation produced opposite effects on these two circuits states even though the cellular-level hormonal action is likely the same in both states. In contrast, I found these circuits were similarly sensitive to sensory feedback, despite this feedback acting via different synapses under each condition. My work thereby provides the first mechanistic understanding of input-pathway specific rhythm generators that produce convergent output patterns and the flexibility enabled by these circuit states when responding to additional modulatory inputs

RPCH modulation of a multi-oscillator network: Effects on the pyloric network of the spiny lobster

The neuropeptide red pigment concentrating hormone (RPCH), which we have previously shown to activate the cardiac sac motor pattern and lead to a conjoint gastric mill-cardiac sac pattern in the spiny lobster Panulirus, also activates and modulates the pyloric pattern. Like the activity of gastric mill neurons in RPCH, the pattern of activity in the pyloric neurons is considerably more complex than that seen in control saline. This reflects the influence of the cardiac sac motor pattern, and particularly the upstream inferior ventricular (IV) neurons, on many of the pyloric neurons. RPCH intensifies this interaction by increasing the strength of the synaptic connections between the IV neurons and their targets in the stomatogastric ganglion. At the same time, RPCH enhances postinhibitory rebound in the lateral pyloric (LP) neuron. Taken together, these factors largely explain the complex pyloric pattern recorded in RPCH in Panulirus

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

An in vivo Assay for Simultaneous Monitoring of Neuronal Activity and Behavioral Output in the Stomatogastric Nervous System of Decapod Crustaceans

Central pattern generators (CPGs) generate rhythmic output patterns and drive vital behaviors such as breathing, swallowing, locomotion and chewing 1-10. While most insights into the rhythm generating mechanisms of CPGs have been derived from isolated nervous system preparations, the relationship between neural activity and corresponding behavioral expression is often unclear. The stomatogastric system of decapod crustaceans is one of the best characterized neural system for motor pattern generation 9-12 and many mechanisms of motor pattern generation and selection have been discovered in this system. Since most studies are limited to the isolated nervous system, little is known about the actual behavioral output of this system. For example, it is unknown whether the observed flexibility in the motor patterns is present in vivo and whether distinct motor activities drive corresponding behavioral patterns. We present a method which allows electrophysiological recordings of CPG neurons and the simultaneous monitoring of the behavioral output of the stomatogastric nervous system. For this, we use extracellular hook electrodes either for recording or stimulation of neurons in the gastric mill CPG that drive the chewing movements of three teeth in the foregut of the animal. Electrodes are applied in tethered, but otherwise fully intact crabs (Cancer pagurus) and an endoscope is used to monitor tooth movements. Nerve and video recordings of the endoscopic camera are synchronized and motion tracking techniques are used to analyze gastric mill movements. This approach thus allows testing the behavioral relevance of the neural activity patterns produced by central pattern generators