Local Neuronal Populations Research Articles

Desynchronization and rebound of beta oscillations during conscious and unconscious local neuronal processing in the macaque lateral prefrontal cortex

Accumulating evidence indicates that control mechanisms are not tightly bound to conscious perception since both conscious and unconscious information can trigger control processes, probably through the activation of higher-order association areas like the prefrontal cortex. Studying the modulation of control-related prefrontal signals in a microscopic, neuronal level during conscious and unconscious neuronal processing, and under control-free conditions could provide an elementary understanding of these interactions. Here we performed extracellular electrophysiological recordings in the macaque lateral prefrontal cortex (LPFC) during monocular physical alternation (PA) and binocular flash suppression (BFS) and studied the local scale relationship between beta (15–30 Hz) oscillations, a rhythmic signal believed to reflect the current sensory, motor, or cognitive state (status-quo), and conscious or unconscious neuronal processing. First, we show that beta oscillations are observed in the LPFC during resting state. Both PA and BFS had a strong impact on the power of this spontaneous rhythm with the modulation pattern of beta power being identical across these two conditions. Specifically, both perceptual dominance and suppression of local neuronal populations in BFS were accompanied by a transient beta desynchronization followed by beta activity rebound, a pattern also observed when perception occurred without any underlying visual competition in PA. These results indicate that under control-free conditions, at least one rhythmic signal known to reflect control processes in the LPFC (i.e., beta oscillations) is not obstructed by local neuronal, and accordingly perceptual, suppression, thus being independent from temporally co-existing conscious and unconscious local neuronal representations. Future studies could reveal the additive effects of motor or cognitive control demands on prefrontal beta oscillations during conscious and unconscious processing.

Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness

The neurophysiological mechanisms by which anesthetic drugs cause loss of consciousness are poorly understood. Anesthetic actions at the molecular, cellular, and systems levels have been studied in detail at steady states of deep general anesthesia. However, little is known about how anesthetics alter neural activity during the transition into unconsciousness. We recorded simultaneous multiscale neural activity from human cortex, including ensembles of single neurons, local field potentials, and intracranial electrocorticograms, during induction of general anesthesia. We analyzed local and global neuronal network changes that occurred simultaneously with loss of consciousness. We show that propofol-induced unconsciousness occurs within seconds of the abrupt onset of a slow (<1 Hz) oscillation in the local field potential. This oscillation marks a state in which cortical neurons maintain local patterns of network activity, but this activity is fragmented across both time and space. Local (<4 mm) neuronal populations maintain the millisecond-scale connectivity patterns observed in the awake state, and spike rates fluctuate and can reach baseline levels. However, neuronal spiking occurs only within a limited slow oscillation-phase window and is silent otherwise, fragmenting the time course of neural activity. Unexpectedly, we found that these slow oscillations occur asynchronously across cortex, disrupting functional connectivity between cortical areas. We conclude that the onset of slow oscillations is a neural correlate of propofol-induced loss of consciousness, marking a shift to cortical dynamics in which local neuronal networks remain intact but become functionally isolated in time and space.

Temporal Production Signals in Parietal Cortex

Author SummaryOne advantage of studying sensory systems is that external stimuli can be readily controlled and quantified. Our internal perception of time, however, is not as easily approachable. To address this challenge, we developed a task that leverages our knowledge of the neuronal circuits underlying eye movements. Monkeys were trained to move their eyes consistently at regular time intervals without any external cueing or immediate expectation of reward. The animals were remarkably precise and consistent in their timing. Recordings from individual lateral intraparietal area (LIP) neurons, an area associated with eye movement planning, showed a linear decrease in activity between eye movements. This contrasts with previous studies that have reported increases in LIP activity immediately prior to eye movements and suggests that expectation of reward may have influenced such results. We also found that variations in this decreasing activity were predictive of the animals’ timing. Finally, we demonstrate through a simple model that LIP activity was sufficiently precise to completely explain the timing of the animals' actions. The model demonstrates that a precise internal sense of time can arise by comparing activity across different neuronal populations.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

Octopaminergic modulation of contrast gain adaptation in fly visual motion‐sensitive neurons

Locomotor activity like walking or flying has recently been shown to alter visual processing in several species. In insects, the neuromodulator octopamine is thought to play an important role in mediating state changes during locomotion of the animal [K.D. Longden & H.G. Krapp (2009) J. Neurophysiol., 102, 3606-3618; (2010) Front. Syst. Neurosci., 4, 153; S.N. Jung et al. (2011)J. Neurosci., 31, 9231-9237]. Here, we used the octopamine agonist chlordimeform (CDM) to mimic effects of behavioural state changes on visual motion processing. We recorded from identified motion-sensitive visual interneurons in the lobula plate of the blowfly Calliphora vicina. In these neurons, which are thought to be involved in visual guidance of locomotion, motion adaptation leads to a prominent attenuation of contrast sensitivity. Following CDM application, the neurons maintained high contrast sensitivity in the adapted state. This modulation of contrast gain adaptation was independent of the activity of the recorded neurons, because it was also present after stimulation with visual motion that did not result in deviations from the neurons' resting activity. We conclude that CDM affects presynaptic inputs of the recorded neurons. Accordingly, the effect of CDM was weak when adapting and test stimuli were presented in different parts of the receptive field, stimulating separate populations of local presynaptic neurons. In the peripheral visual system adaptation depends on the temporal frequency of the stimulus pattern and is therefore related to pattern velocity. Contrast gain adaptation could therefore be the basis for a shift in the velocity tuning that was previously suggested to contribute to state-dependent processing of visual motion information in the lobula plate interneurons.

Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons

The ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC) and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF) neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency oscillations in local excitatory networks, but indicate that inhibition rather than excitation generates coherent rhythms at higher frequencies.

Dynamic Effective Connectivity of Inter-Areal Brain Circuits

Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question “Which areas cause the present activity of which others?”. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this “communication-through-coherence”, making thus possible a fast “on-demand” reconfiguration of global information routing modalities.

Layer specific neuronal activities in response to visual object images in the inferior temporal cortex revealed by current source density analysis

Layer specific neuronal activities in response to visual object images in the inferior temporal cortex revealed by current source density analysis

Chronic imaging of cortical sensory map dynamics using a genetically encoded calcium indicator

In vivo optical imaging can reveal the dynamics of large-scale cortical activity, but methods for chronic recording are limited. Here we present a technique for long-term investigation of cortical map dynamics using wide-field ratiometric fluorescence imaging of the genetically encoded calcium indicator (GECI) Yellow Cameleon 3.60. We find that wide-field GECI signals report sensory-evoked activity in anaesthetized mouse somatosensory cortex with high sensitivity and spatiotemporal precision, and furthermore, can be measured repeatedly in separate imaging sessions over multiple weeks. This method opens new possibilities for the longitudinal study of stability and plasticity of cortical sensory representations.

Local Diversity and Fine-Scale Organization of Receptive Fields in Mouse Visual Cortex

Many thousands of cortical neurons are activated by any single sensory stimulus, but the organization of these populations is poorly understood. For example, are neurons in mouse visual cortex--whose preferred orientations are arranged randomly--organized with respect to other response properties? Using high-speed in vivo two-photon calcium imaging, we characterized the receptive fields of up to 100 excitatory and inhibitory neurons in a 200 μm imaged plane. Inhibitory neurons had nonlinearly summating, complex-like receptive fields and were weakly tuned for orientation. Excitatory neurons had linear, simple receptive fields that can be studied with noise stimuli and system identification methods. We developed a wavelet stimulus that evoked rich population responses and yielded the detailed spatial receptive fields of most excitatory neurons in a plane. Receptive fields and visual responses were locally highly diverse, with nearby neurons having largely dissimilar receptive fields and response time courses. Receptive-field diversity was consistent with a nearly random sampling of orientation, spatial phase, and retinotopic position. Retinotopic positions varied locally on average by approximately half the receptive-field size. Nonetheless, the retinotopic progression across the cortex could be demonstrated at the scale of 100 μm, with a magnification of ≈ 10 μm/°. Receptive-field and response similarity were in register, decreasing by 50% over a distance of 200 μm. Together, the results indicate considerable randomness in local populations of mouse visual cortical neurons, with retinotopy as the principal source of organization at the scale of hundreds of micrometers.

Spike-field activity in parietal area LIP during coordinated reach and saccade movements

The posterior parietal cortex is situated between visual and motor areas and supports coordinated visually guided behavior. Area LIP in the intraparietal sulcus contains representations of visual space and has been extensively studied in the context of saccades. However, area LIP has not been studied during coordinated movements, so it is not known whether saccadic representations in area LIP are influenced by coordinated behavior. Here, we studied spiking and local field potential (LFP) activity in area LIP while subjects performed coordinated reaches and saccades or saccades alone to remembered target locations to test whether activity in area LIP is influenced by the presence of a coordinated reach. We find that coordination significantly changes the activity of individual neurons in area LIP, increasing or decreasing the firing rate when a reach is made with a saccade compared with when a saccade is made alone. Analyzing spike-field coherence demonstrates that area LIP neurons whose firing rate is suppressed during the coordinated task have activity temporally correlated with nearby LFP activity, which reflects the synaptic activity of populations of neurons. Area LIP neurons whose firing rate increases during the coordinated task do not show significant spike-field coherence. Furthermore, LFP power in area LIP is suppressed and does not increase when a coordinated reach is made with a saccade. These results demonstrate that area LIP neurons display different responses to coordinated reach and saccade movements, and that different spike rate responses are associated with different patterns of correlated activity. The population of neurons whose firing rate is suppressed is coherently active with local populations of LIP neurons. Overall, these results suggest that area LIP plays a role in coordinating visually guided actions through suppression of coherent patterns of saccade-related activity.

Odor segmentation and identification in an abstract large-scale model of the mammalian olfactory system

We integrate an olfactory epithelium and bulb (OB) model with an olfactory (piriform) cortex (OC) model, all comprising abstract graded units representing local populations of neurons, to demonstrate the overall system's applicability to odor recognition tasks. Large-scale simulations on a cluster computer allow us to evaluate an instance of the model with the size matching that of the olfactory system of a small mammal. The input data is synthesized with the intention to resemble the distribution of various features of early olfactory response patterns to naturalistic odor stimuli, e.g. [1,2]. Self-organization of the feedforward connectivity from the OB to the piriform cortex based on statistical properties of synthetic olfactory stimuli with the support of synaptic plasticity provide the capacity for generating sparse and distributed cortical representations [3]. At the cortical level, associative functions resulting from recurrent connectivity with Hebbian plasticity implement attractor memory storage and pattern processing, e.g. completion and rivalry [4]. Adaptation in cortical units, mimicking neuronal spike frequency adaptation, serves as an underlying mechanism for odor segmentation. We also investigate how backprojecting connections from the OC to the OB facilitate context dependent stimulus identification. The model is evaluated in real-world scenarios involving odor classification and segmentation in noisy environments. Firstly, the network is trained to perform concentration invariant odor recognition. Secondly, the model identifies single-ligand components of odor mixtures it is subjected to in a testing phase. Finally, the effect of context dependence is evaluated by examining the system's capability to recognize specific low intensity stimuli when it is forced into an arousal state. Preliminary results show the dependence of performance on the network size, the number of odors and the complexity of their early olfactory activation patterns. In a broader context, the proposed large-scale abstract models of holistic olfactory processing implements a multiscale approach to modeling olfaction. After the network has been trained, its connection matrices are utilized in simulating a more biophysically realistic model of the mammalian olfactory system (for details see poster by Kaplan et al.).

Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention

Visual attention affects both perception and neuronal responses. Whether the same neuronal mechanisms mediate spatial attention, which improves perception of attended locations, and nonspatial forms of attention has been a subject of considerable debate. Spatial and feature attention have similar effects on individual neurons. Because visual cortex is retinotopically organized, however, spatial attention can comodulate local neuronal populations, whereas feature attention generally requires more selective modulation. We compared the effects of feature and spatial attention on local and spatially separated populations by recording simultaneously from dozens of neurons in both hemispheres of V4. Feature and spatial attention affect the activity of local populations similarly, modulating both firing rates and correlations between pairs of nearby neurons. However, whereas spatial attention appears to act on local populations, feature attention is coordinated across hemispheres. Our results are consistent with a unified attentional mechanism that can modulate the responses of arbitrary subgroups of neurons.

Neural mechanism of activity spread in the cat motor cortex and its relation to the intrinsic connectivity

Motor cortical points are linked by intrinsic horizontal connections having a recurrent network topology. However, it is not known whether neural activity can propagate over the area covered by these intrinsic connections and whether there are spatial anisotropies of synaptic strength, as opposed to synaptic density. Moreover, the mechanisms by which activity spreads have yet to be determined. To address these issues, an 8 × 8 microelectrode array was inserted in the forelimb area of the cat motor cortex (MCx). The centre of the array had a laser etched hole ∼500 μm in diameter. A microiontophoretic pipette, with a tip diameter of 2-3 μm, containing bicuculline methiodide (BIC) was inserted in the hole and driven to a depth of 1200-1400 μm from the cortical surface. BIC was ejected for ∼2min from the tip of the micropipette with positive direct current ranging between 20 and 40 nA in different experiments. This produced spontaneous nearly periodic bursts (0.2-1.0 Hz) of multi-unit activity in a radius of about 400 μm from the tip of the micropipette. The bursts of neural activity spread at a velocity of 0.11-0.24 ms⁻¹ (mean=0.14 mm ms⁻¹, SD=0.05)with decreasing amplitude.The area activated was on average 7.22 mm² (SD=0.91 mm²), or ∼92% of the area covered by the recording array. The mode of propagation was determined to occur by progressive recruitment of cortical territory, driven by a central locus of activity of some 400 μm in radius. Thus, activity did not propagate as a wave. Transection of the connections between the thalamus and MCx did not significantly alter the propagation velocity or the size of the recruited area, demonstrating that the bursts spread along the routes of intrinsic cortical connectivity. These experiments demonstrate that neural activity initiated within a small motor cortical locus (≤ 400 μm in radius) can recruit a relatively large neighbourhood in which a variety of muscles acting at several forelimb joints are represented. These results support the hypothesis that the MCx controls the forelimb musculature in an integrated and anticipatory manner based on a recurrent network topology

Local sleep in awake rats

When the brain is awake, neurons in the cerebral cortex fire irregularly and the electroencephalogram (EEG) displays low amplitude, high frequency fluctuations. After falling asleep, neurons start oscillating between ON periods, when they fire as during wake, and OFF periods, when they stop firing altogether, and the EEG displays high amplitude slow waves. But what happens to neuronal firing after a long period of wake? We show here in freely behaving rats that, after prolonged wake, cortical neurons can go briefly “OFF line” as they do in sleep, accompanied by slower waves in the local EEG. Strikingly, neurons often go OFF line in one cortical area and not in another. During these periods of “local sleep”, whose incidence increases with wake duration, rats appear awake, active, and display a wake EEG. However, they are progressively impaired in a sugar pellet reaching task. Thus, though both the EEG and behavior indicate wakefulness, local populations of neurons in the cortex may be falling asleep, with negative consequences on performance.

Respiratory modulation of brown adipose tissue sympathetic nerve activity during activation of dorsomedial hypothalamic neurons

Activation of dorsomedial hypothalamic (DMH) neurons contributes to the increases in brown adipose tissue (BAT) sympathetic nerve activity (SNA), BAT thermogenesis and heart rate (HR) induced by skin cooling and during the febrile response mimicked by injection of PGE2 into the medial preoptic area. Neurons in the same DMH region mediate increases in HR, and potentially other autonomic variables, during stress responses. In this study, the relationship between BAT SNA and phrenic nerve activity (PHR) was characterized during activation of localized populations of DMH neurons with nanoinjections of NMDA in urethane/chloralose-anesthetized, vagotomized, artificially-ventilated rats. NMDA in DMH usually elicited increases both in BAT SNA and in PHR amplitude and rate, although the optimal sites for evoking maximal increases in BAT SNA and in PHR were rarely the same. The bursts in evoked BAT SNA displayed a strong respiratory modulation with a phase relationship to the PHR which was not different from that observed during skin cooling-evoked activation of BAT SNA. These data are consistent with the availability of a descending drive to BAT and to respiration from likely separate populations of DMH neurons and with an influence of central respiratory networks on BAT SNA that is mediated at a site caudal to the DMH. Supported by NIH grant NS40987.

Network recruitment to coherent oscillations in a hippocampal computer model

Coherent neural oscillations represent transient synchronization of local neuronal populations in both normal and pathological brain activity. These oscillations occur at or above gamma frequencies (>30 Hz) and often are propagated to neighboring tissue under circumstances that are both normal and abnormal, such as gamma binding or seizures. The mechanisms that generate and propagate these oscillations are poorly understood. In the present study we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of stochastic resonance and coherence resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. "Epileptic" high-frequency oscillations (HFOs), produced by pathologies such as increased synaptic activity and recurrent connections, were superior at recruiting neighboring tissue. "Normal" HFOs, associated with fast firing of inhibitory cells and sparse pyramidal cell firing, tended to suppress surrounding cells and showed very limited ability to recruit. These findings point to synaptic noise and physiological coupling as important targets for understanding the generation and propagation of both normal and pathological HFOs, suggesting potential new diagnostic and therapeutic approaches to human disorders such as epilepsy.

Representation of visual scenes by local neuronal populations in layer 2/3 of mouse visual cortex

How are visual scenes encoded in local neural networks of visual cortex? In rodents, visual cortex lacks a columnar organization so that processing of diverse features from a spot in visual space could be performed locally by populations of neighboring neurons. To examine how complex visual scenes are represented by local microcircuits in mouse visual cortex we measured visually evoked responses of layer 2/3 neuronal populations using 3D two-photon calcium imaging. Both natural and artificial movie scenes (10 seconds duration) evoked distributed and sparsely organized responses in local populations of 70–150 neurons within the sampled volumes. About 50% of neurons showed calcium transients during visual scene presentation, of which about half displayed reliable temporal activation patterns. The majority of the reliably responding neurons were activated primarily by one of the four visual scenes applied. Consequently, single-neurons performed poorly in decoding, which visual scene had been presented. In contrast, high levels of decoding performance (>80%) were reached when considering population responses, requiring about 80 randomly picked cells or 20 reliable responders. Furthermore, reliable responding neurons tended to have neighbors sharing the same stimulus preference. Because of this local redundancy, it was beneficial for efficient scene decoding to read out activity from spatially distributed rather than locally clustered neurons. Our results suggest a population code in layer 2/3 of visual cortex, where the visual environment is dynamically represented in the activation of distinct functional sub-networks.

Wiring of Divergent Networks in the Central Auditory System

Divergent axonal projections are found throughout the central auditory system. Here, we evaluate these branched projections in terms of their types, distribution, and putative physiological roles. In general, three patterns of axon collateralization are found: intricate local branching, long-distance collaterals, and branched axons (BAs) involved in feedback-control loops. Local collaterals in the auditory cortex may be involved in local processing and modulation of neuronal firing, while long-range collaterals are optimized for wide-dissemination of information. Rarely do axons branch to both ascending and descending targets. Branched projections to two or more widely separated nuclei or areas are numerically sparse but widespread. Finally, branching to contralateral targets is evident at multiple levels of the auditory pathway and may enhance binaural computations for sound localization. These patterns of axonal branching are comparable to those observed in other modalities. We conclude that the operations served by BAs are area- and nucleus-specific and may complement the divergent unbranched projections of local neuronal populations.

Local field potentials, BOLD and spiking activity – relationships and physiological mechanisms

AbstractExtracellular voltage fluctuations (local field potentials, LFPs) reflecting neural mass action are ubiquitous across species and brain regions. Numerous studies have characterized the properties of LFP signals in the cortex to study sensory and motor computations as well as cognitive processes like attention, perception and memory. In addition, its extracranial counterpart – the electroencephalogram – is widely used in clinical applications. However, the link between LFP signals and the underlying activity of local populations of neurons is still largely elusive. For the LFP to aid our understanding of cortical computation, however, we need to know as precisely as possible what aspects of neural mass action it reflects. In this chapter, we examine recent advances and results regarding the origin, the feature selectivity and the spatial resolution of the local field potential and discuss its relationship to local spiking activity as well as the BOLD signal used in fMRI. We place particular focus on the gamma-band of the local field potential since it has long been implicated to play an important role in sensory processing. We conclude that in contrast to spikes, the local field potential does not measure the output of the computation performed by a cortical circuit, but are rather indicative of the synaptic and dendritic processes, as well as the dynamics of cortical computation.