Takashi Tateno

Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons

Activity-dependent modification of synaptic efficacy is widely recognized as a cellular basis of learning, memory, and developmental plasticity. Little is known, however, of the consequences of such modification on network activity. Using electrode arrays, we examined how a single, localized tetanic stimulus affects the firing of up to 72 neurons recorded simultaneously in cultured networks of cortical neurons, in response to activation through 64 different test stimulus pathways. The same tetanus produced potentiated transmission in some stimulus pathways and depressed transmission in others. Unexpectedly, responses were homogeneous: for any one stimulus pathway, neuronal responses were either all enhanced or all depressed. Cross-correlation of responses with the responses elicited through the tetanized site revealed that both enhanced and depressed responses followed a common principle: activity that was closely correlated before tetanus with spikes elicited through the tetanized pathway was enhanced, whereas activity outside a 40-ms time window of correlation to tetanic pathway spikes was depressed. Response homogeneity could result from pathway-specific recurrently excitatory circuits, whose gain is increased or decreased by the tetanus, according to its cross-correlation with the tetanized pathway response. The results show how spatial responses following localized tetanic stimuli, although complex, can be accounted for by a simple rule for activity-dependent modification.

A system for MEA-based multisite stimulation

The capability for multisite stimulation is one of the biggest potential advantages of microelectrode arrays (MEAs). There remain, however, several technical problems which have hindered the development of a practical stimulation system. An important design goal is to allow programmable multisite stimulation, which produces minimal interference with simultaneous extracellular and patch or whole cell clamp recording. Here, we describe a multisite stimulation and recording system with novel interface circuit modules, in which preamplifiers and transistor transistor logic-driven solid-state switching devices are integrated. This integration permits PC-controlled remote switching of each substrate electrode. This allows not only flexible selection of stimulation sites, but also rapid switching of the selected sites between stimulation and recording, within 1.2 ms. This allowed almost continuous monitoring of extracellular signals at all the substrate-embedded electrodes, including those used for stimulation. In addition, the vibration-free solid-state switching made it possible to record whole-cell synaptic currents in one neuron, evoked from multiple sites in the network. We have used this system to visualize spatial propagation patterns of evoked responses in cultured networks of cortical neurons. This MEA-based stimulation system is a useful tool for studying neuronal signal processing in biological neuronal networks, as well as the process of synaptic integration within single neurons.

Activity-dependent enhancement in the reliability of correlated spike timings in cultured cortical neurons

Abstract. To study the use-dependent modification of activity in neural networks, we investigated the spike timing by simultaneously recording activity at multiple sites in a network of cultured cortical neurons. We used dynamical analysis to study the temporal structure of spike trains and the activity-dependent changes in the reliability and reproducibility of spike patterns evoked by a stimulus. We also used cross-correlation analysis to evaluate the interactions of neuron pairs. Our main conclusions are that even when no obvious change in spike numbers can be seen, use-dependent modification occurs, either enhancing or reducing in the reliability and reproducibility of spike trains evoked by a stimulus, and the fine temporal structure of stimulus-evoked spike trains and interactions between neurons are also modified by tetanic stimulation.

Random dynamics of the Morris-Lecar neural model.

Determining the response characteristics of neurons to fluctuating noise-like inputs similar to realistic stimuli is essential for understanding neuronal coding. This study addresses this issue by providing a random dynamical system analysis of the Morris-Lecar neural model driven by a white Gaussian noise current. Depending on parameter selections, the deterministic Morris-Lecar model can be considered as a canonical prototype for widely encountered classes of neuronal membranes, referred to as class I and class II membranes. In both the transitions from excitable to oscillating regimes are associated with different bifurcation scenarios. This work examines how random perturbations affect these two bifurcation scenarios. It is first numerically shown that the Morris-Lecar model driven by white Gaussian noise current tends to have a unique stationary distribution in the phase space. Numerical evaluations also reveal quantitative and qualitative changes in this distribution in the vicinity of the bifurcations of the deterministic system. However, these changes notwithstanding, our numerical simulations show that the Lyapunov exponents of the system remain negative in these parameter regions, indicating that no dynamical stochastic bifurcations take place. Moreover, our numerical simulations confirm that, regardless of the asymptotic dynamics of the deterministic system, the random Morris-Lecar model stabilizes at a unique stationary stochastic process. In terms of random dynamical system theory, our analysis shows that additive noise destroys the above-mentioned bifurcation sequences that characterize class I and class II regimes in the Morris-Lecar model. The interpretation of this result in terms of neuronal coding is that, despite the differences in the deterministic dynamics of class I and class II membranes, their responses to noise-like stimuli present a reliable feature.

Spatio-temporal cholinergic modulation in cultured networks of rat cortical neurons: spontaneous activity.

Activation of the cholinergic innervation of the cortex has been implicated in sensory processing, learning, and memory. At the cellular level, acetylcholine both increases excitability and depresses synaptic transmission, and its effects on network firing are hard to predict. We studied the effects of carbachol, a cholinergic agonist, on network firing in cultures of rat cortical neurons, using electrode arrays to monitor the activity of large numbers of neurons simultaneously. These cultures show stable spontaneous synchronized burst firing which propagates through dense synaptic connections. Carbachol (10-50 microM), acting through muscarinic receptors, was found to induce a switch to asynchronous single-spike firing and to result in a loss of regularity and fragmentation of the burst structure. To obtain a quantitative measure of cholinergic actions on cortical networks, we applied a cluster Poisson-process model to sets of paralleled spike-trains in the presence and absence of carbachol. This revealed that the time series can be well-characterized by such a simple model, consistent with the observed 1/f(b)-like spectra (0.04<b<2.08). After applying higher concentrations of carbachol the property of the spectra shifted toward a Poisson-process (white) spectrum. These results indicate that cholinergic neurotransmitters have a strong and reliable desynchronizing action on cortical neural activity.

The mechanism of ethanol action on midbrain dopaminergic neuron firing: a dynamic-clamp study of the role of Ih and GABAergic synaptic integration

Hyperpolarization-activated and cyclic nucleotide-gated (HCN) channels are expressed in dopaminergic (DA) neurons of the ventral tegmental area (VTA) as well as in DA and GABAergic neurons of the substantia nigra (SN). The excitation of DA neurons induced by ethanol has been proposed to result from its enhancing HCN channel current, I(h). Using perforated patch-clamp recordings in rat midbrain slices, we isolated I(h) in these neurons by voltage clamp. We showed that ethanol reversibly increased the amplitude and accelerated the activation kinetics of I(h) and caused a depolarizing shift in its voltage dependence. Using dynamic-clamp conductance injection, we injected artificial I(h) and fluctuating GABAergic synaptic conductance inputs into neurons following block of intrinsic I(h). This demonstrated directly a major role of I(h) in promoting rebound spiking following phasic inhibition, which was enhanced as the kinetics and amplitude of I(h) were changed in the manner induced by ethanol. Similar effects of ethanol were observed on I(h) and firing rate in non-DA, putatively GABAergic interneurons, indicating that in addition to its direct effects on firing, ethanol will produce large changes in the inhibition and disinhibition (via GABAergic interneurons) converging on DA neurons. Thus the overall effects of ethanol on firing of DA cells of the VTA and SN in vivo, and hence on phasic dopamine release in the striatum, appear to be determined substantially by its action on I(h) in both DA cells and GABAergic interneurons.

Synchronization of Firing in Cortical Fast-Spiking Interneurons at Gamma Frequencies: A Phase-Resetting Analysis

Fast-spiking (FS) cells in the neocortex are interconnected both by inhibitory chemical synapses and by electrical synapses, or gap-junctions. Synchronized firing of FS neurons is important in the generation of gamma oscillations, at frequencies between 30 and 80 Hz. To understand how these synaptic interactions control synchronization, artificial synaptic conductances were injected in FS cells, and the synaptic phase-resetting function (SPRF), describing how the compound synaptic input perturbs the phase of gamma-frequency spiking as a function of the phase at which it is applied, was measured. GABAergic and gap junctional conductances made distinct contributions to the SPRF, which had a surprisingly simple piecewise linear form, with a sharp midcycle break between phase delay and advance. Analysis of the SPRF showed how the intrinsic biophysical properties of FS neurons and their interconnections allow entrainment of firing over a wide gamma frequency band, whose upper and lower frequency limits are controlled by electrical synapses and GABAergic inhibition respectively.

STOCHASTIC PHASE LOCKINGS IN A RELAXATION OSCILLATOR FORCED BY A PERIODIC INPUT WITH ADDITIVE NOISE: A FIRST-PASSAGE-TIME APPROACH

Noise effects on phase lockings in a system consisting of a piecewise-linear van der Pol relaxation oscillator driven by a periodic input are studied. The problem of finding the period of the oscillator is reduced to the first-passage-time problem of the Ornstein-Uhlenbeck process with time-varying boundary. The probability density functions of the first-passage time are used to define the operator which governs a transition of an input phase density after one cycle of the oscillator. Phase lockings in a stochastic sense are investigated on the basis of the density evolution by the operator.

Integration of Broadband Conductance Input in Rat Somatosensory Cortical Inhibitory Interneurons: An Inhibition-Controlled Switch Between Intrinsic and Input-Driven Spiking in Fast-Spiking Cells

Quantitative understanding of the dynamics of particular cell types when responding to complex, natural inputs is an important prerequisite for understanding the operation of the cortical network. Different types of inhibitory neurons are connected by electrical synapses to nearby neurons of the same type, enabling the formation of synchronized assemblies of neurons with distinct dynamical behaviors. Under what conditions is spike timing in such cells determined by their intrinsic dynamics and when is it driven by the timing of external input? In this study, we have addressed this question using a systematic approach to characterizing the input-output relationships of three types of cortical interneurons (fast spiking [FS], low-threshold spiking [LTS], and nonpyramidal regular-spiking [NPRS] cells) in the rat somatosensory cortex, during fluctuating conductance input designed to mimic natural complex activity. We measured the shape of average conductance input trajectories preceding spikes and fitted a two-component linear model of neuronal responses, which included an autoregressive term from its own output, to gain insight into the input-output relationships of neurons. This clearly separated the contributions of stimulus and discharge history, in a cell-type dependent manner. Unlike LTS and NPRS cells, FS cells showed a remarkable switch in dynamics, from intrinsically driven spike timing to input-fluctuation-controlled spike timing, with the addition of even a small amount of inhibitory conductance. Such a switch could play a pivotal role in the function of FS cells in organizing coherent gamma oscillations in the local cortical network. Using both pharmacological perturbations and modeling, we show how this property is a consequence of the particular complement of voltage-dependent conductances in these cells.

Characterization of Stochastic Bifurcations in a Simple Biological Oscillator

This study of the effect of noise on bifurcations in a simple biological oscillator with a periodically modulated threshold uses the first-passage-time problem of the Ornstein–Uhlenbeck process with a periodic boundary to define the operator governing the transition of a threshold phase density. Stochastic phase-locking is analyzed numerically by evaluating the evolution of the probability density function of the threshold phase. A firing phase map in a noisy environment is extended to a stochastic kernel so that stochastic bifurcations can be investigated by spectral analysis of the kernel.