Neurons in the primary visual cortex respond to oriented stimuli placed in the center of their receptive field, yet their response is modulated by stimuli outside the receptive field (the surround). Classically, this surround modulation is assumed to be strongest if the orientation of the surround stimulus aligns with the neuron's preferred orientation, irrespective of the actual center stimulus. This neuron-dependent surround modulation has been used to explain a wide range of psychophysical phenomena, such as biased tilt perception and saliency of stimuli with contrasting orientation.
View Article and Find Full Text PDFCortical information processing at the cellular level has predominantly been studied in local networks, which are dominated by strong vertical connectivity between layers. However, recent studies suggest that the bulk of axons targeting pyramidal neurons most likely originate from outside this local range, emphasizing the importance of horizontal connections. We mapped a subset of these connections to L5B pyramidal neurons in rat somatosensory cortex with photostimulation, identifying intact projections up to a lateral distance of 2 mm.
View Article and Find Full Text PDFNeuronal networks confront researchers with an overwhelming complexity of interactions between their elements. A common approach to understanding neuronal processing is to reduce complexity by defining subunits and infer their functional role by selectively modulating them. However, this seemingly straightforward approach may lead to confusing results if the network exhibits parallel pathways leading to recurrent connectivity.
View Article and Find Full Text PDFWhat is the role of higher-order spike correlations for neuronal information processing? Common data analysis methods to address this question are devised for the application to spike recordings from multiple single neurons. Here, we present a new method which evaluates the subthreshold membrane potential fluctuations of one neuron, and infers higher-order correlations among the neurons that constitute its presynaptic population. This has two important advantages: Very large populations of up to several thousands of neurons can be studied, and the spike sorting is obsolete.
View Article and Find Full Text PDFThe Poisson process is an often employed model for the activity of neuronal populations. It is known, though, that superpositions of realistic, non- Poisson spike trains are not in general Poisson processes, not even for large numbers of superimposed processes. Here we construct superimposed spike trains from intracellular in vivo recordings from rat neocortex neurons and compare their statistics to specific point process models.
View Article and Find Full Text PDFAlternating epochs of activity and silence are a characteristic feature of neocortical networks during certain sleep cycles and deep states of anesthesia. The mechanism and functional role of these slow oscillations (<1 Hz) have not yet been fully characterized. Experimental and theoretical studies show that slow-wave oscillations can be generated autonomously by neocortical tissue but become more regular through a thalamo-cortical feedback loop.
View Article and Find Full Text PDFCurrent concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume.
View Article and Find Full Text PDFFor the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data.
View Article and Find Full Text PDFThe mammalian neocortex has a remarkable ability to precisely reproduce behavioral sequences or to reliably retrieve stored information. In contrast, spiking activity in behaving animals shows a considerable trial-to-trial variability and temporal irregularity. The signal propagation and processing underlying these conflicting observations is based on fundamental neurophysiological processes like synaptic transmission, signal integration within single cells, and spike formation.
View Article and Find Full Text PDFTo understand the mechanisms of fast information processing in the brain, it is necessary to determine how rapidly populations of neurons can respond to incoming stimuli in a noisy environment. Recently, it has been shown experimentally that an ensemble of neocortical neurons can track a time-varying input current in the presence of additive correlated noise very fast, up to frequencies of several hundred hertz. Modulations in the firing rate of presynaptic neuron populations affect, however, not only the mean but also the variance of the synaptic input to postsynaptic cells.
View Article and Find Full Text PDFWe propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV(2)) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5-10 spikes on average.
View Article and Find Full Text PDFRecent 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.
View Article and Find Full Text PDFMicroglial cells are the pathologic sensors in the brain. ATP released from damaged cells is a candidate for signalling neural injury to microglia. Moreover, ATP is an extracellular messenger for propagating astrocyte activity in the form of Ca2+ waves.
View Article and Find Full Text PDFPathologic impacts in the brain lead to a widespread activation of microglial cells far beyond the site of injury. Here, we demonstrate that glial Ca2+ waves can trigger responses in microglial cells. We elicited Ca2+ waves in corpus callosum glial cells by electrical stimulation or local adenosine triphosphate (ATP) ejection in acute brain slices.
View Article and Find Full Text PDF