Wim Rutten

Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks

Extracellular action potentials were recorded from developing dissociated rat neocortical networks continuously for up to 49 days in vitro using planar multielectrode arrays. Spontaneous neuronal activity emerged toward the end of the first week in vitro and from then on exhibited periods of elevated firing rates, lasting for a few days up to weeks, which were largely uncorrelated among different recording sites. On a time scale of seconds to minutes, network activity typically displayed an ongoing repetition of distinctive firing patterns, including short episodes of synchronous firing at many sites ( network bursts). Network bursts were highly variable in their individual spatio-temporal firing patterns but showed a remarkably stable underlying probabilistic structure (obtained by summing consecutive bursts) on a time scale of hours. On still longer time scales, network bursts evolved gradually, with a significant broadening (to about 2 s) in the third week in vitro, followed by a drastic shortening after about one month in vitro. Bursts at this age were characterized by highly synchronized onsets reaching peak firing levels within less than ca. 60 ms. This pattern persisted for the rest of the culture period. Throughout the recording period, active sites showed highly persistent temporal relationships within network bursts. These longitudinal recordings of network firing have, thus, brought to light a reproducible pattern of complex changes in spontaneous firing dynamics of bursts during the development of isolated cortical neurons into synaptically interconnected networks.

Longterm stability and developmental changes in spontaneous network burst firing patterns in dissociated rat cerebral cortex cell cultures on multielectrode arrays

Spontaneous action potentials were recorded longitudinally for 4-7 weeks from dissociated rat occipital cortex cells cultured on planar multi-electrode plates, during their development from isolated neurons into synaptically connected neuronal networks. Activity typically consisted of generalized bursts lasting up to several seconds, separated by variable epochs of sporadic firing at some of the active sites. These network bursts displayed discharge patterns with age-dependent firing rate profiles, and durations significantly increasing in the 3rd week in vitro and decreasing after about 1 month in vitro, when they evolved into short events with prompt onsets. These findings indicate that after about a month in vitro these cultured neuronal networks have developed a degree of excitability that allows almost instantaneous triggering of generalized discharges. Individual neurons tend to fire in specific and persistent temporal relationships to one another within these network bursts, suggesting that network connectivity maintains a core topology during its development.

Sensitivity and selectivity of intraneural stimulation using a silicon electrode array

A multielectrode array in silicon technology, as well as experimental paradigms and model calculations for sensitivity and selectivity measures, have been developed. The array consists of twelve platinum electrode sites (10*50 mu m and 50 mu m interdistance) on a 45- mu m thick tip-shaped silicon substrate and a Si/sub 3/N/sub 4/ insulating glass cover layer. The tip is inserted in the peroneal nerve of a rat during acute experiments to stimulate alpha motor fibers or the extensor digitorum longus muscle. Sensitivity calculations and experiments show a cubic dependence of the number of stimulated motor units on current amplitude of the stimulatory pulse (recruitment curves), starting at single motor level. Selectivity was tested by a method based on the refractory properties of neurons. At the lowest stimulus levels (for one motor unit) selectivity is maximal when two electrodes are separated by 200-250 mu m, which was also estimated on theoretical grounds. The study provides clues for future designs of two- and three-dimensional devices.<<ETX>>

The median frequency of the surface EMG power spectrum in relation to motor unit firing and action potential properties

Three components determine the power spectrum of the surface EMG signal: the auto- and cross-power spectra of the firing processes and the power spectra of the motor unit action potential (MUAP). To clarify the relative contribution of these components to the median frequency (MF) of the power spectrum, a stochastic simulation model was used in which most input parameters [e.g., MUAP peak-peak time (PPT), mean interpulse interval time, and synchronization parameters] were described in terms of distribution functions. Simulation clearly predicts that MF is especially sensitive to variations in MUAP shape, the MUAP PPT, and synchronization. The influence of the firing process parameters was predicted to be marginal. To obtain values for the MUAP parameters, a needle-triggered averaging technique was used to gather surface MUAPs from the m. biceps brachii. With use of these MUAPs as input for the model, it was found that intrasubject variability of MF is caused by variations in both MUAP PPT and MUAP shape, whereas intersubject variability in MF is caused primarily by variations in PPT.

Investigating membrane breakdown of neuronal cells exposed to nonuniform electric fields by finite-element modeling and experiments

High electric field strengths may induce high cell membrane potentials. At a certain breakdown level the membrane potential becomes constant due to the transition from an insulating state into a high conductivity and high permeability state. Pores are thought to be created through which molecules may be transported into and out of the cell interior. Membrane rupture may follow due to the expansion of pores or the creation of many small pores across a certain part of the membrane surface. In nonuniform electric fields, it is difficult to predict the electroporated membrane area. Therefore, in this study the induced membrane potential and the membrane area where this potential exceeds the breakdown level is investigated by finite-element modeling. Results from experiments in which the collapse of neuronal cells was detected were combined with the computed field strengths in order to investigate membrane breakdown and membrane rupture. It was found that in nonuniform fields membrane rupture is position dependent, especially at higher breakdown levels. This indicates that the size of the membrane site that is affected by electroporation determines rupture.

Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques

One symbolic (rule-based inductive learning) and one connectionist (neural network) machine learning technique were used to reconstruct muscle activation patterns from kinematic data measured during normal human walking at several speeds. The activation patterns (or desired outputs) consisted of surface electromyographic (EMG) signals from the semitendinosus and vastus medialis muscles. The inputs consisted of flexion and extension angles measured at the hip and knee of the ipsilateral leg, their first and second derivatives, and bilateral foot contact information. The training set consisted of data from six trials, at two different speeds. The testing set consisted of data from two additional trials (one at each speed), which were not in the training set. It was possible to reconstruct the muscular activation at both speeds using both techniques. Timing of the reconstructed signals was accurate. The integrated value of the activation bursts was less accurate. The neural network gave a continuous output, whereas the rule-based inductive learning rule tree gave a quantised activation level. The advantage of rule-based inductive learning was that the rules used were both explicit and comprehensible, whilst the rules used by the neural network were implicit within its structure and not easily comprehended. The neural network was able to reconstruct the activation patterns of both muscles from one network, whereas two separate rule sets were needed for the rule-based technique. It is concluded that machine learning techniques, in comparison to explicit inverse muscular skeletal models, show good promise in modelling nearly cyclic movements such as locomotion at varying walking speeds. However, they do not provide insight into the biomechanics of the system, because they are not based on the biomechanical structure of the system.

Geometry-based finite-element modeling of the electrical contact between a cultured neuron and a microelectrode

The electrical contact between a substrate embedded microelectrode and a cultured neuron depends on the geometry of the neuron-electrode interface. Interpretation and improvement of these contacts requires proper modeling of all coupling mechanisms. In literature, it is common practice to model the neuron-electrode contact using lumped circuits in which large simplifications are made in the representation of the interface geometry. In this paper, the finite-element method is used to model the neuron-electrode interface, which permits numerical solutions for a variety of interface geometries. The simulation results offer detailed spatial and temporal information about the combined electrical behavior of extracellular volume, electrode-electrolyte interface and neuronal membrane.

Measurement of sealing resistance of cell-electrode interfaces in neuronal cultures using impedance spectroscopy.

Sealing resistance is highly significant with respect to the electrical neuronelectrode contact because it decreases the stimulation threshold of neurons cultured on a planar micro-electrode array. A method is proposed for measurement of the sealing resistance using impedance spectroscopy. The effect of the sealing resistance on the total impedance spectrum of a cell-electrode interface is modelled for complete coverage of the electrode by the cell. Sensitivity analysis demonstrates that the impedance spectrum is determined by four parameters: two electrode parameters, the sealing resistance and the shunt capacitance between the lead of the electrode and the culture medium. Experimental verification of the model is performed by simultaneous measurement of the impedance spectrum and electrode coverage. A good and unique fit between the simulated and measured impedance spectra was obtained by varying the two electrode parameters and the sealing resistance.

Analysis of Cultured Neuronal Networks Using Intraburst Firing Characteristics

It is an open question whether neuronal networks, cultured on multielectrode arrays, retain any capability to usefully process information (learning and memory). A necessary prerequisite for learning is that stimulation can induce lasting changes in the network. To observe these changes, one needs a method to describe the network in sufficient detail, while stable in normal circumstances. We analyzed the spontaneous bursting activity that is encountered in dissociated cultures of rat neocortical cells. Burst profiles (BPs) were made by estimating the instantaneous array-wide firing frequency. The shape of the BPs was found to be stable on a time scale of hours. Spatiotemporal detail is provided by analyzing the instantaneous firing frequency per electrode. The resulting phase profiles (PPs) were estimated by aligning BPs to their peak spiking rate over a period of 15 min. The PPs reveal a stable spatiotemporal pattern of activity during bursts over a period of several hours, making them useful for plasticity and learning studies. We also show that PPs can be used to estimate conditional firing probabilities. Doing so, yields an approach in which network bursting behavior and functional connectivity can be studied.

The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured Networks of Rat Cortical Neurons

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our ‘learning curves’ to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.