Biomedical subjects

Ad Aertsen

Publications and source records attributed to Ad Aertsen.

12 recordsLinked to original sources

PubMed2005

Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex.

Cortical field potentials have been used for decades in neurophysiological studies to probe spatio-temporal activity patterns of local populations of neurons. Recently, however, interest in these signals was spurred as they were proposed as potential control signals for neuronal motor prostheses, i.e., for devices fit to record and decode brain activity to restore motor functions in paralyzed patients. Little is known, however, about the functional significance of these cortical field potentials. Here we compared information about arm movement direction in two types of movement related cortical field potentials, obtained during a four direction center-out arm reaching paradigm: local field potentials (LFPs) recorded with intracortical micro-electrodes from monkey motor cortex, and epicortical field potentials (EFPs) recorded with macro-electrode arrays subdurally implanted on the surface of the human cerebral cortex. While monkey LFPs showed a typical sequence of positive and negative potential peaks, an initial negative peak was the most salient feature of human EFPs. Individual contralateral LFPs from the monkey motor cortex carried approximately twice as much decoded information (DI) about arm movement direction (median 0.27 bit) as did individual EFPs from the contralateral hand/arm area of primary motor cortex in humans (median 0.12 bit). This relation was similar to the relation between median peak signal-to-noise ratios for directional modulation of movement related potentials (MRPs) of both types of signals. We discuss possible reasons for the observed differences, amongst them epi- vs. intracortical recording and the different electrode dimensions used to measure EFPs and LFPs.

Action Potentials↗

PubMed2005

Encoding of movement direction in different frequency ranges of motor cortical local field potentials.

Recent studies showed that the low-frequency component of local field potentials (LFPs) in monkey motor cortex carries information about parameters of voluntary arm movements. Here, we studied how different signal components of the LFP in the time and frequency domains are modulated during center-out arm movements. Analysis of LFPs in the time domain showed that the amplitude of a slow complex waveform beginning shortly before the onset of arm movement is modulated with the direction of the movement. Examining LFPs in the frequency domain, we found that direction-dependent modulations occur in three frequency ranges, which typically increased their amplitudes before and during movement execution: < or =4, 6-13, and 63-200 Hz. Cosine-like tuning was prominent in all signal components analyzed. In contrast, activity in a frequency band approximately 30 Hz was not modulated with the direction of movement and typically decreased its amplitude during the task. This suggests that high-frequency oscillations have to be divided into at least two functionally different regimes: one approximately 30 Hz and one >60 Hz. Furthermore, using multiple LFPs, we could show that LFP amplitude spectra can be used to decode movement direction, with the best performance achieved by the combination of different frequency ranges. These results suggest that using the different frequency components in the LFP is useful in improving inference of movement parameters from local field potentials.

Action Potentials↗

PubMed2005

Controlling synaptic input patterns in vitro by dynamic photo stimulation.

Recent experimental and theoretical work indicates that both the intensity and the temporal structure of synaptic activity strongly modulate the integrative properties of single neurons in the intact brain. However, studying these effects experimentally is complicated by the fact that, in experimental systems, network activity is either absent, as in the acute slice preparation, or difficult to monitor and to control, as in in vivo recordings. Here, we present a new implementation of neurotransmitter uncaging in acute brain slices that uses functional projections to generate tightly controlled, spatio-temporally structured synaptic input patterns in individual neurons. For that, a set of presynaptic neurons is activated in a precisely timed sequence through focal photolytic release of caged glutamate with the help of a fast laser scanning system. Integration of synaptic inputs can be studied in postsynaptic neurons that are not directly stimulated with the laser, but receive input from the targeted neurons through intact axonal projections. Our new approach of dynamic photo stimulation employs functional synapses, accounts for their spatial distribution on the dendrites, and thus allows study of the integrative properties of single neurons with physiologically realistic input. Data obtained with our new technique suggest that, not only the neuronal spike generator, but also synaptic transmission and dendritic integration in neocortical pyramidal cells, can be highly reliable.

Action Potentials↗

PubMed2004

Synaptic integration in rat frontal cortex shaped by network activity.

Neocortical neurons in vivo are embedded in networks with intensive ongoing activity. How this network activity affects the neurons' integrative properties and what function this may imply at the network level remain largely unknown. Most of our knowledge regarding synaptic communication and integration is based on recordings in vitro, where network activity is strongly diminished or even absent. Here, we present results from two complementary series of experiments based on intracellular in vivo recordings in anesthetized rat frontal cortex. Specifically, we measured 1) the relationship between the excursions of a neuron's membrane potential and the spiking activity in the surrounding network and 2) how the summation of several inputs to a single neuron changes with the different levels of its membrane potential excursions and the associated states of network activity. The combination of these measurements enables us to assess how the level of network activity influences synaptic integration. We present direct evidence that integration of synaptic inputs in frontal cortex is linear, independent of the level of network activity. However, during periods of high network activity, the neurons' response to synaptic input is markedly reduced in both amplitude and duration. This results in a drastic shortening of its window for temporal integration, requiring more precise coordination of presynaptic spike discharges to reliably drive the neuron to spike under conditions of high network activity. We conclude that ongoing activity, as present in the active brain, emphasizes the need for neuronal cooperation at the network level, and cannot be ignored in the exploration of cortical function.

Action Potentials↗

PubMed2004

Neuronal integration of synaptic input in the fluctuation-driven regime.

During sensory stimulation, visual cortical neurons undergo massive synaptic bombardment. This increases their input conductance, and action potentials mainly result from membrane potential fluctuations. To understand the response properties of neurons operating in this regime, we studied a model neuron with synaptic inputs represented by transient membrane conductance changes. We show that with a simultaneous increase of excitation and inhibition, the firing rate first increases, reaches a maximum, and then decreases at higher input rates. Comodulation of excitation and inhibition, therefore, does not provide a straightforward way of controlling the neuronal firing rate, in contrast to coding mechanisms postulated previously. The synaptically induced conductance increase plays a key role in this effect: it decreases firing rate by shunting membrane potential fluctuations, and increases it by reducing the membrane time constant, allowing for faster membrane potential transients. These findings do not depend on details of the model and, hence, are relevant to cells of other cortical areas as well.

Action Potentials↗

PubMed2003

Inference of hand movements from local field potentials in monkey motor cortex.

The spiking of neuronal populations in motor cortex provides accurate information about movement parameters. Here we show that hand movement target and velocity can be inferred from multiple local field potentials (LFPs) in single trials approximately as efficiently as from multiple single-unit activity (SUA) recorded from the same electrodes. Our results indicate that LFPs can be used as an additional signal for decoding brain activity, particularly for new neuroprosthetic applications.

Action Potentials↗

PubMed2003

Activity dynamics and propagation of synchronous spiking in locally connected random networks.

Random network models have been a popular tool for investigating cortical network dynamics. On the scale of roughly a cubic millimeter of cortex, containing about 100,000 neurons, cortical anatomy suggests a more realistic architecture. In this locally connected random network, the connection probability decreases in a Gaussian fashion with the distance between neurons. Here we present three main results from a simulation study of the activity dynamics in such networks. First, for a broad range of parameters these dynamics exhibit a stationary state of asynchronous network activity with irregular single-neuron spiking. This state can be used as a realistic model of ongoing network activity. Parametric dependence of this state and the nature of the network dynamics in other regimes are described. Second, a synchronous excitatory stimulus to a fraction of the neurons results in a strong activity response that easily dominates the network dynamics. And third, due to that activity response an embedding of a divergent-convergent feed-forward subnetwork (as in synfire chains) does not naturally lead to a stable propagation of synchronous activity in the subnetwork; this is in contrast to our earlier findings in isolated subnetworks of that type. Possible mechanisms for stabilizing the interplay of volleys of synchronous spikes and network dynamics by specific learning rules or generalizations of the subnetworks are discussed.

Action Potentials↗

PubMed2003

Elimination of response latency variability in neuronal spike trains.

Neuronal activity in the mammalian cortex exhibits a considerable amount of trial-by-trial variability. This may be reflected by the magnitude of the activity as well as by the response latency with respect to an external event, such as the onset of a sensory stimulus, or a behavioral event. Here we present a novel nonparametric method for estimating trial-by-trial differences in response latency from neuronal spike trains. The method makes use of the dynamic rate profile for each single trial and maximizes their total pairwise correlation by appropriately shifting all trials in time. The result is a new alignment of trials that largely eliminates the variability in response latency and provides a new internal trigger that is independent of experiment time. To calibrate the method, we simulated spike trains based on stochastic point processes using a parametric model for phasic response profiles. We illustrate the method by an application to simultaneous recordings from a pair of neurons in the motor cortex of a behaving monkey. It is demonstrated how the method can be used to study the temporal relation of the neuronal response to the experiment, to investigate whether neurons share the same dynamics, and to improve spike correlation analysis. Differences between this and other previously published methods are discussed.

Action Potentials↗

PubMed2003

Higher-order statistics of input ensembles and the response of simple model neurons.

Pairwise correlations among spike trains recorded in vivo have been frequently reported. It has been argued that correlated activity could play an important role in the brain, because it efficiently modulates the response of a postsynaptic neuron. We show here that a neuron's output firing rate critically depends on the higher-order statistics of the input ensemble. We constructed two statistical models of populations of spiking neurons that fired with the same rates and had identical pairwise correlations, but differed with regard to the higher-order interactions within the population. The first ensemble was characterized by clusters of spikes synchronized over the whole population. In the second ensemble, the size of spike clusters was, on average, proportional to the pairwise correlation. For both input models, we assessed the role of the size of the population, the firing rate, and the pairwise correlation on the output rate of two simple model neurons: a continuous firing-rate model and a conductance-based leaky integrate-and-fire neuron. An approximation to the mean output rate of the firing-rate neuron could be derived analytically with the help of shot noise theory. Interestingly, the essential features of the mean response of the two neuron models were similar. For both neuron models, the three input parameters played radically different roles with respect to the postsynaptic firing rate, depending on the interaction structure of the input. For instance, in the case of an ensemble with small and distributed spike clusters, the output firing rate was efficiently controlled by the size of the input population. In addition to the interaction structure, the ratio of inhibition to excitation was found to strongly modulate the effect of correlation on the postsynaptic firing rate.

Action Potentials↗

PubMed2002

Unitary events in multiple single-neuron spiking activity: I. Detection and significance.

It has been proposed that cortical neurons organize dynamically into functional groups (cell assemblies) by the temporal structure of their joint spiking activity. Here, we describe a novel method to detect conspicuous patterns of coincident joint spike activity among simultaneously recorded single neurons. The statistical significance of these unitary events of coincident joint spike activity is evaluated by the joint-surprise. The method is tested and calibrated on the basis of simulated, stationary spike trains of independently firing neurons, into which coincident joint spike events were inserted under controlled conditions. The sensitivity and specificity of the method are investigated for their dependence on physiological parameters (firing rate, coincidence precision, coincidence pattern complexity) and temporal resolution of the analysis. In the companion article in this issue, we describe an extension of the method, designed to deal with nonstationary firing rates.

Action Potentials↗

PubMed2002

Unitary events in multiple single-neuron spiking activity: II. Nonstationary data.

In order to detect members of a functional group (cell assembly) in simultaneously recorded neuronal spiking activity, we adopted the widely used operational definition that membership in a common assembly is expressed in near-simultaneous spike activity. Unitary event analysis, a statistical method to detect the significant occurrence of coincident spiking activity in stationary data, was recently developed (see the companion article in this issue). The technique for the detection of unitary events is based on the assumption that the underlying processes are stationary in time. This requirement, however, is usually not fulfilled in neuronal data. Here we describe a method that properly normalizes for changes of rate: the unitary events by moving window analysis (UEMWA). Analysis for unitary events is performed separately in overlapping time segments by sliding a window of constant width along the data. In each window, stationarity is assumed. Performance and sensitivity are demonstrated by use of simulated spike trains of independently firing neurons, into which coincident events are inserted. If cortical neurons organize dynamically into functional groups, the occurrence of near-simultaneous spike activity should be time varying and related to behavior and stimuli. UEMWA also accounts for these potentially interesting nonstationarities and allows locating them in time. The potential of the new method is illustrated by results from multiple single-unit recordings from frontal and motor cortical areas in awake, behaving monkey.

Action Potentials↗

PubMed2002

Statistical significance of coincident spikes: count-based versus rate-based statistics.

Inspired by different conceptualizations of temporal neural coding schemes, there has been recent interest in the search for signs of precisely synchronized neural activity in the cortex. One method developed for this task is unitary-event analysis. This method tests multiple single neuron recordings for short epochs with significantly more coincident spikes than expected from independent neurons. We reformulated the statistical test underlying this method using a coincidence count distribution based on empirical spike counts rather than on estimated spike probabilities. In the case of two neurons, the requirement of stationary firing rates, originally imposed on both neurons, can be relaxed; only the rate of one neuron needs to be stationary, while the other may follow an arbitrary time course. By analytical calculations of the test power curves of the original and the revised method, we demonstrate that the test power can be increased by a factor of two or more in physiologically realistic regimes. In addition, we analyze the effective significance levels of both methods for neural firing rates ranging between 0.2 Hz and 30 Hz.

Action Potentials↗