Franz Hamilton

Differential responses to ω-agatoxin IVA in murine frontal cortex and spinal cord derived neuronal networks.

ω-Agatoxin-IVA is a well known P/Q-type Ca(2+) channel blocker and has been shown to affect presynaptic Ca(2+) currents as well postsynaptic potentials. P/Q-type voltage gated Ca(2+) channels play a vital role in presynaptic neurotransmitter release and thus play a role in action potential generation. Monitoring spontaneous activity of neuronal networks on microelectrode arrays (MEAs) provides an important tool for examining this neurotoxin. Changes in extracellular action potentials are readily observed and are dependent on synaptic function. Given the efficacy of murine frontal cortex and spinal cord networks to detect neuroactive substances, we investigated the effects of ω-agatoxin on spontaneous action potential firing within these networks. We found that networks derived from spinal cord are more sensitive to the toxin than those from frontal cortex; a concentration of only 10nM produced statistically significant effects on activity from spinal cord networks whereas 50 nM was required to alter activity in frontal cortex networks. Furthermore, the effects of the toxin on frontal cortex are more complex as unit specific responses were observed. These manifested as either a decrease or increase in action potential firing rate which could be statistically separated as unique clusters. Administration of bicuculline, a GABAA inhibitor, isolated a single response to ω-agatoxin, which was characterized by a reduction in network activity. These data support the notion that the two clusters detected with ω-agatoxin exposure represent differential responses from excitatory and inhibitory neuronal populations.

Reconstructing neural dynamics using data assimilation with multiple models

Assimilation of data with models of physical processes is a critical component of modern scientific analysis. In recent years, nonlinear versions of Kalman filtering have been developed, in addition to methods that estimate model parameters in parallel with the system state. We propose a substantial extension of these tools to deal with the specific case of unmodeled variables, when training data from the variable is avaiable. The method uses a stack of several, nonidentical copies of a physical model to jointly reconstruct the variable in question. We demonstrate the ability of this technique to accurately recover an unmodeled experimental quantity, such as an ion concentration, from a single voltage trace after the training period is completed. The method is applied to reconstruct the potassium concentration in a neural culture from multielectrode array voltage measurements.

Time-Dependent Increase in Network Response to Stimulation

In vitro neuronal cultures have become a popular method with which to probe network-level neuronal dynamics and phenomena in controlled laboratory settings. One of the key dynamics of interest in these in vitro studies has been the extent to which cultured networks display properties indicative of learning. Here we demonstrate the effects of a high frequency electrical stimulation signal in training cultured networks of cortical neurons. Networks receiving this training signal displayed a time-dependent increase in the response to a low frequency probing stimulation, particularly in the time window of 20–50 ms after stimulation. This increase was found to be statistically significant as compared to control networks that did not receive training. The timing of this increase suggests potentiation of synaptic mechanisms. To further investigate this possibility, we leveraged the powerful Cox statistical connectivity method as previously investigated by our group. This method was used to identify and track changes in network connectivity strength.

Tracking intracellular dynamics through extracellular measurements

Extracellular recordings of neuronal cells are frequently a part of in vitro and in vivo experimental studies as a means of monitoring network-level dynamics. Their connections to intracellular dynamics are not well understood. Single-unit recordings are a more direct way to measure intracellular dynamics, but are typically difficult and expensive. On the other hand, simple differential equations models exist for single neurons. In this article, we apply a recent advance in data assimilation theory, designed to correct bias in general observation functions, toward the reconstruction of model-based intracellular dynamics from extracellular recordings.

Dynamic steering of in vitro cortical neurons using field stimulation.

Neurological disorders are often characterized by abnormal neuronal activity. In the case of epilepsy, this can manifest itself in the form of uncontrolled synchronous activity often in the form of bursting. Pattern steering is the ability to apply stimulation to a network that effectively changes its dynamical firing pattern. In an epileptic network, the stimulation would be used to move the seizing network from its abnormal state to a normal state. This idea is explored here in cultured networks of cortical neurons plated on microelectrode arrays. Stimulation was applied to the bath resulting in an electric field generated throughout the network. This field was verified as sub-threshold in strength using a finite element model simulation. Stimulated networks showed a significant suppression in the number of bursts and increase in the interburst interval as compared to control networks. This observed burst suppression suggests that the sub-threshold stimulating field moved networks from a state of high frequency bursting to a state of low frequency bursting.