The design of a novel complementary metal oxide semiconductor detection system for biochemical luminescence.

We designed a complementary metal oxide semiconductor (CMOS) chip with accompanied accessories as a system for the detections and quantifications of biochemical luminescence. This is the first of such instruments that has been reported. The semiconductor chip was manufactured through a 0.25 microm CMOS standard process. A current mirror was designed in integrated circuit (IC) to amplify the signal current that was induced by chemiluminescence. Horseradish peroxidase (HRP)-luminol-H2O2 system was used as an example to constitute a useful platform for coupling to chemiluminescence reactions which produce H2O2. Glucose-glucose oxidase (GOD) reaction was coupled with HRP-luminol-H2O2 reaction to demonstrate the ability of the novel CMOS base instrument for quantifying the biological luminescence of a variety of valuable clinical assays. Our results illustrated that the combination of the specifically designed CMOS IC and commercially available electronic devices established a simple and useful bioanalytical tool.

Nonlinear dynamic modeling of neuron action potential threshold during synaptically driven broadband intracellular activity.

Activity-dependent variation of neuronal thresholds for action potential (AP) generation is one of the key determinants of spike-train temporal-pattern transformations from presynaptic to postsynaptic spike trains. In this study, we model the nonlinear dynamics of the threshold variation during synaptically driven broadband intracellular activity. First, membrane potentials of single CA1 pyramidal cells were recorded under physiologically plausible broadband stimulation conditions. Second, a method was developed to measure AP thresholds from the continuous recordings of membrane potentials. It involves measuring the turning points of APs by analyzing the third-order derivatives of the membrane potentials. Four stimulation paradigms with different temporal patterns were applied to validate this method by comparing the measured AP turning points and the actual AP thresholds estimated with varying stimulation intensities. Results show that the AP turning points provide consistent measurement of the AP thresholds, except for a constant offset. It indicates that 1) the variation of AP turning points represents the nonlinearities of threshold dynamics; and 2) an optimization of the constant offset is required to achieve accurate spike prediction. Third, a nonlinear dynamical third-order Volterra model was built to describe the relations between the threshold dynamics and the AP activities. Results show that the model can predict threshold accurately based on the preceding APs. Finally, the dynamic threshold model was integrated into a previously developed single neuron model and resulted in a 33% improvement in spike prediction.

An analysis of the expression locus of long-term potentiation in hippocampal CA1 neurons.

Long-term potentiation (LTP) has long been understood as an increase in the potency of a synaptic connection between two neurons. In this study, we combine a previously developed two-stage cascade model with electrophysiological recordings of rat hippocampal CA1 pyramidal cells both before and after LTP to analyze linear and nonlinear contributions of pre and post-synaptic partners to the strengthening of their synaptic connectivity. The result suggests that the major nonlinear expression locus of LTP exists in the post-synaptic side. Additionally, the report reveals that LTP should be understood not only in the traditional view as a change in the magnitude of communication between two cells, but also as a change in their temporal coding properties of information exchange.

Nonlinear dynamic modeling of synaptically driven single hippocampal neuron intracellular activity.

A high-order nonlinear dynamic model of the input-output properties of single hippocampal CA1 pyramidal neurons was developed based on synaptically driven intracellular activity. The purpose of this study is to construct a model that: 1) can capture the nonlinear dynamics of both subthreshold activities [postsynaptic potentials (PSPs)] and suprathreshold activities (action potentials) in a single formalism; 2) is sufficiently general to be applied to any spike-input and spike-output neurons (point process input and point process output neural systems); and 3) is computationally efficient. The model consisted of three major components: 1) feedforward kernels (up to third order) that transform presynaptic action potentials into PSPs; 2) a constant threshold, above which action potentials are generated; and 3) a feedback kernel (first order) that describes spike-triggered after-potentials. The model was applied to CA1 pyramidal cells, as they were electrically stimulated with broadband Poisson random impulse trains through the Schaffer collaterals. The random impulse trains used here have physiological properties similar to spiking patterns observed in CA3 hippocampal neurons. PSPs and action potentials were recorded from the soma of CA1 pyramidal neurons using whole-cell patch-clamp recording. We evaluated the model performance separately with respect to PSP waveforms and the occurrence of spikes. The average normalized mean square error of PSP prediction is 14.4%. The average spike prediction error rate is 18.8%. In summary, although prediction errors still could be reduced, the model successfully captures the majority of high-order nonlinear dynamics of the single-neuron intracellular activity. The model captures the general biophysical processes with a small set of open parameters that are directly constrained by the intracellular recording, and thus, can be easily applied to any spike-input and spike-output neuron.

A two-stage cascade nonlinear dynamical model of single neurons for the separation and quantification of pre- and post-synaptic mechanisms of synaptic transmission.

Neurons receive pre-synaptic spike trains and transform them into post-synaptic spike trains. This spike train to spike train temporal transformation underlies all cognitive functions performed by neurons, e.g., learning and memory. The transformation is a highly nonlinear dynamical process that involves both pre- and post-synaptic mechanisms. The ability to separate and quantify the nonlinear dynamics of pre- and post-synaptic mechanism is needed to gain insights into this transformation. In this study, we developed a Volterra kernel based two-stage cascade model of synaptic transmission using synaptically-driven intracellular activities, to which broadband stimulation conditions were imposed. The first stage of the model represents the pre-synaptic mechanisms and describes the nonlinear dynamical transformation from pre-synaptic spike trains to transmitter vesicle release strengths. The vesicle release strengths were obtained from the intracellularly recorded excitatory post-synaptic currents (EPSCs). The second stage of the model represents the post-synaptic mechanisms and describes the nonlinear dynamical transformation from release strengths to excitatory post-synaptic potentials (EPSPs). One application of this cascade model is to analyze the pre- and post-synaptic mechanism change induced by long-term potentiation (LTP). This future application is expected to shed new light on the expression locus of LTP.

Nonlinear dynamic analyses of single hippocampal neurons before and after long-term potentiation.

Long-term potentiation (LTP) has long been considered an important phenomenon involved in learning and memory. However, the current literature lacks systematical analyses of single neuron dynamics before and after LTP induction. In this report, we applied an up to 3rd-order Volterra kernel to analyze the dynamics of single hippocampal neurons before and after LTP induction. Broadband Poisson random impulse trains with a 2 Hz mean frequency, which included physiologically plausible patterns, were applied to stimulate CA1 pyramidal neurons through Schaffer collateral before and after LTP induction. Corresponding somatic sub-threshold excitatory postsynaptic potentials (EPSPs) were recorded from CA1 neurons using whole-cell patch-clamp recording. The result suggests that LTP increases linear responses and depresses nonlinear responses. The phenomenon can be explained with both presynaptic and postsynaptic hypotheses. Further comparisons of voltage-clamp and current-clamp recordings are needed to distinguish the changes of dynamics in presynaptic and/or postsynaptic mechanisms.

Nonlinear model of single hippocampal neurons with dynamical thresholds.

Neurons transform a series of presynaptic spikes into a series of postsynaptic spikes through a number of nonlinear mechanisms. A nonlinear model with a dynamical threshold was built using a Volterra Laguerre kernel method to characterize the spike train to spike train transformations of hippocampal CA1 pyramidal neurons. Inputs of the model were broadband Poisson random impulse trains with a 2 Hz mean frequency, and outputs of the model were the corresponding evoked post-synaptic potential (PSP) and spike train data recorded from CA1 cell bodies using a whole-cell recording technique. The model consists of four major components, i.e., feedforward kernels representing the transformation of presynaptic spikes to PSPs; a dynamical threshold kernel determining threshold value based on output inter-spike-intervals (ISIs); a spike detector; and a feedback kernel representing the spike-triggered after-potentials.

Nonparametric modeling of single neuron.

Nonlinear dynamic models were built with Volterra Lagurre kernel method to characterize the input-output properties of single hippocampal CA1 pyramidal neurons. Broadband Poisson random impulse trains with a 2 Hz mean frequency, which include the majorities of the spike patterns in behaving rats, were used to stimulate the Schaffer collaterals. Corresponding random-interval post-synaptic potential (PSP) and spike train data were recorded from the cell bodies using whole-cell recording technique and then analyzed with the nonlinear dynamic model. The model consists of two major components, i.e., a feedforward three order Volterra kernel model characterizing the transformation of presynaptic stimulations to pre-threshold PSPs, and a feedback one order Volterra kernel model capturing the spike-triggered after-potential. Results showed that the model could predict 1) the sub-threshold PSPs trace with a normalized mean square error around 10% and 2) the spikes with accuracy higher than 80%.