We present a novel approach to implementing the dynamic-clamp protocol (Sharp et al., 1993), commonly used in neurophysiology and cardiac electrophysiology experiments. Our approach is based on real-time extensions to the Linux operating system. Conventional PC-based approaches have typically utilized single-cycle computational rates of 10 kHz or slower. In thispaper, we demonstrate reliable cycle-to-cycle rates as fast as 50 kHz. Our system, which we call model reference current injection (MRCI); pronounced merci is also capable of episodic logging of internal state variables and interactive manipulation of model parameters. The limiting factor in achieving high speeds was not processor speed or model complexity, but cycle jitter inherent in the CPU/motherboard performance. We demonstrate these high speeds and flexibility with two examples: 1) adding action-potential ionic currents to a mammalian neuron under whole-cell patch-clamp and 2) altering a cell's intrinsic dynamics via MRCI while simultaneously coupling it via artificial synapses to an internal computational model cell. These higher rates greatly extend the applicability of this technique to the study of fast electrophysiological currents such fast a currents and fast excitatory/inhibitory synapses.
We present a novel approach for neuron model specification using a Genetic Algorithm (GA) to develop simple firing neuron models consisting of a single compartment with one inward and one outward current. The GA not only chooses the model parameters, but also chooses the formulation of the ionic currents (i.e. single-variable, two-variable, instantaneous, or leak). The fitness function of the GA compares the frequency output of the GA generated models to an I-F curve of a nominal Morris-Lecar (ML) model. Initially, several different classes of models compete among the population. Eventually, the GA converges to a population containing only ML-type firing models with an instantaneous inward and single-variable outward current. Simulations where ML-type models are restricted from the population are also investigated. This GA approach allows the exploration of a universe of feasible model classes that is less constrained by model formulation assumptions than traditional parameter estimation approaches. While we use a simple model, this technique is scalable to much larger and more complex formulations.
There is a general consensus that neurons responsible for the generation of a respiratory rhythm are located in the rostral ventrolateral medulla. However, there is still controversy regarding the necessary components for respiratory rhythm generation. Both inspiratory neurons in the preBotzinger Complex (preBOtC) and pre-inspiratory neurons located more rostrally to this anatomical structure referred to as the parafacial respiratory group (pFRG) have been proposed to be essential for respiratory rhythmogenesis. To study the dynamical interactions between preBOtC and pFRG neurons, we use a canonical model that describes each neuron population as a phase oscillator. We assume that the oscillators are weakly coupled with pFRG neurons providing stimulation to preBOtC neurons and preBOtC neurons providing inhibitory drive to pFRG neurons. In our mathematical study, we explore plausible mechanisms that may account for the complex interactions between I and pre-I neuron. In particular, we show that reduced excitability of inspiratory in preBOtC may lead to the phenomena known as "quantal slowing".
A minimal neuron model, the Morris-Lecar model, is implemented on field programmable analog arrays (FPAAs). Our approach is to solve the differential equation describing the model in a similar way a computer solves the same problem: numerically integrate the differential equation by making arithmetic operations on voltage mode circuits of the FPAAs. The results demonstrate that biologically relevant dynamics can be observed from the electronic neuron despite limitations on the configurability of the FPAAs. Such models can be run accurately in real-time or many orders of magnitude faster than real-time. FPAAs are feasible candidates for implementation of neuron models using off-the-shelf software-reconfigurable analog circuit elements.
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