High-speed solution switching using piezo-based micropositioning stages.

S Stilson; A McClellan; S Devasia

doi:10.1109/10.930905

Home/Archive/PubMed

PubMed · 11442292

High-speed solution switching using piezo-based micropositioning stages.

S Stilson·A McClellan·S Devasia

View original source ↗Export metadata ↓

Abstract

Motion-induced vibration is a critical limitation in high-speed micropositioning stages used to achieve solution switching. Controlled rapid solution switching is used to study the fast activation and deactivation kinetics of ligand-gated ion-channel populations isolated in excised membrane patches--such studies are needed to understand fundamental mechanisms that mediate synaptic excitation and inhibition in the central nervous system. However, as the solution-switching speed is increased, vibration induced in the piezo-based positioning stages can result in undesired, repeated, ligand application to the excised patch. The article describes a method to use knowledge of the piezo-stage's vibrational dynamics to compensate for and reduce these unwanted vibrations. The method was experimentally verified using an open-electrode technique, and fast solution switching (100 micros range) was achieved.

Explore related subjects

Models, Neurological Patch-Clamp Techniques Signal Processing, Computer-Assisted Vibration Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

S Stilson, A McClellan, S Devasia. 2001. High-speed solution switching using piezo-based micropositioning stages.. https://doi.org/10.1109/10.930905

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Balanced state of networks of winner-take-all units.

Irregularly timed action potentials, or spikes, are pervasively observed in the brain activity of awake mammals. However, the role of this temporal irregularity in neural computation is still not well understood. In canonical network models irregular spiking emerges via balanced, fluctuating input currents, leading to collective responses that track inputs linearly. How networks characterized by irregular spiking could support flexible nonlinear dynamics needed for general-purpose computation remains under ongoing debate. Here we characterize the dynamics of networks whose elementary unit is not a single neuron but a small group of neurons, with distinct tunings, that compete at each timestep via a winner-take-all (WTA) interaction. While WTA has long been proposed as an elementary functional motif in the brain and represents a powerful computational primitive, how large networks of such units behave has received less investigation. We show that these networks, like classic excitatory-inhibitory balanced networks, exhibit a chaotic fluctuation-driven regime characterized by sustained irregular activity resembling realistic cortical spiking, which we interpret as a multidimensional balance spread over several competing neural populations with different tunings. We develop a mean-field theory for the network, which shows how irregular spiking sustained by time-varying input fluctuations can support flexible nonlinear collective dynamics. Using the theory we predict and verify network regimes in which input fluctuations alone yield multistability, stable sequence generation, or complex heterogeneous firing rate dynamics-three core dynamical primitives thought to underlie memory-dependent neural computation-via consistent Poisson-like spiking produced through chaos. Thus, networks of WTA units support a chaotic fluctuation-driven regime characterized by irregular spiking that can power complex nonlinear collective dynamics. This represents a new model of brain activity capable of simultaneously reproducing realistic spike trains and diverse nonlinear firing rate patterns well posed for flexible computation, and which can be trained or fit to data.

Models, Neurological↗

PubMed2002

The influence of limit cycle topology on the phase resetting curve.

Understanding the phenomenology of phase resetting is an essential step toward developing a formalism for the analysis of circuits composed of bursting neurons that receive multiple, and sometimes overlapping, inputs. If we are to use phase-resetting methods to analyze these circuits, we can either generate phase-resetting curves (PRCs) for all possible inputs and combinations of inputs, or we can develop an understanding of how to construct PRCs for arbitrary perturbations of a given neuron. The latter strategy is the goal of this study. We present a geometrical derivation of phase resetting of neural limit cycle oscillators in response to short current pulses. A geometrical phase is defined as the distance traveled along the limit cycle in the appropriate phase space. The perturbations in current are treated as displacements in the direction corresponding to membrane voltage. We show that for type I oscillators, the direction of a perturbation in current is nearly tangent to the limit cycle; hence, the projection of the displacement in voltage onto the limit cycle is sufficient to give the geometrical phase resetting. In order to obtain the phase resetting in terms of elapsed time or temporal phase, a mapping between geometrical and temporal phase is obtained empirically and used to make the conversion. This mapping is shown to be an invariant of the dynamics. Perturbations in current applied to type II oscillators produce significant normal displacements from the limit cycle, so the difference in angular velocity at displaced points compared to the angular velocity on the limit cycle must be taken into account. Empirical attempts to correct for differences in angular velocity (amplitude versus phase effects in terms of a circular coordinate system) during relaxation back to the limit cycle achieved some success in the construction of phase-resetting curves for type II model oscillators. The ultimate goal of this work is the extension of these techniques to biological circuits comprising type II neural oscillators, which appear frequently in identified central pattern-generating circuits.

Models, Neurological↗

PubMed2002

Fields as limit functions of stochastic discrimination and their adaptability.

For a particular type of elementary function, stochastic discrimination is shown to have an analytic limit function. Classifications can be performed directly by this limit function instead of by a sampling procedure. The limit function has an interpretation in terms of fields that originate from the training examples of a classification problem. Fields depend on the global configuration of the training points. The classification of a point in input space is known when the contributions of all fields are summed. Two modifications of the limit function are proposed. First, for nonlinear problems like high-dimensional parity problems, fields can be quantized. This leads to classification functions with perfect generalization for high-dimensional parity problems. Second, fields can be provided with adaptable amplitudes. The classification corresponding to a limit function is taken as an initialization; subsequently, amplitudes are adapted until an error function for the test set reaches minimal value. It is illustrated that this increases the performance of stochastic discrimination. Due to the nature of the fields, generalization improves even if the amplitude of every training example is adaptable.

Models, Neurological↗