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Visual neurons respond more vigorously to an attended stimulus than an unattended one. How the brain prepares for response gain in anticipation of that stimulus is not well understood. One prominent proposal is that anticipation is characterized by gain-like modulations of spontaneous activity similar to gains in stimulus responses. Here we test an alternative idea: anticipation is characterized by a mixture of both increases and decreases of spontaneous firing rates. Such a strategy would be adaptive as it supports a simple linear scheme for disentangling internal, modulatory signals from external, sensory inputs. We recorded populations of V4 neurons in monkeys performing an attention task, and found that attention states are signaled by different mixtures of neurons across the population in the presence or absence of a stimulus. Our findings support a move from a stimulation-invariant account of anticipation towards a richer view of attentional modulation in a diverse neuronal population. Attention affects stimulus response gain, but its impact without sensory drive is less known. Here, the authors show that attention is coded diversely in a population and is distinct between unstimulated and stimulated contexts, providing a contrast to normalized gain models of attention.

Anticipating distractors during working memory maintenance is critical to reduce their disruptive effects. In this study, we aimed to identify the oscillatory correlates of this process across different spatial scales of neural activity. We simultaneously recorded local field potentials (LFP) from the lateral prefrontal cortex (LPFC) and electroencephalograms (EEG) from the scalp of monkeys performing a modified memory-guided saccade (MGS) task. The monkeys were required to remember the location of a target visual stimulus while anticipating distracting visual stimulus, flashed at 50% probability during the delay period. We found significant theta-band activity across spatial scales during anticipation of a distractor, closely linked with underlying working memory dynamics, through decoding and cross-temporal generalization analyses. EEG particularly reflected reactivation of memory around the anticipated time of a distractor, even in the absence of stimuli. During this anticipated time, beta-band activity exhibited transiently enhanced intrahemispheric communication between the LPFC and occipitoparietal brain areas. These oscillatory phenomena were observed only when the monkeys successfully performed the task, implicating their possible functional role in mitigating anticipated distractors. Our results demonstrate that distractor anticipation recruits multiple oscillatory processes across the brain during working memory maintenance, with a key activity observed predominantly in the theta and beta bands.

Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure.

The neural processing of biological motion (BM) is of profound experimental interest since it is often through the movement of another that we interpret their immediate intentions. Neuroimaging points to a specialized cortical network for processing biological motion. Here, high-density electrical mapping and source-analysis techniques were employed to interrogate the timing of information processing across this network. Participants viewed point-light-displays depicting standard body movements (e.g. jumping), while event-related potentials (ERPs) were recorded and compared to ERPs to scrambled motion control stimuli. In a pair of experiments, three major phases of BM-specific processing were identified: 1) The earliest phase of BM-sensitive modulation was characterized by a positive shift of the ERP between 100 and 200 ms after stimulus onset. This modulation was observed exclusively over the right hemisphere and source-analysis suggested a likely generator in close proximity to regions associated with general motion processing (KO/hMT). 2) The second phase of BM-sensitivity occurred from 200 to 350 ms, characterized by a robust negative-going ERP modulation over posterior middle temporal regions bilaterally. Source-analysis pointed to bilateral generators at or near the posterior superior temporal sulcus (STS). 3) A third phase of processing was evident only in our second experiment, where participants actively attended the BM aspect of the stimuli, and was manifest as a centro-parietal positive ERP deflection, likely related to later cognitive processes. These results point to very early sensory registration of biological motion, and highlight the interactive role of the posterior STS in analyzing the movements of other living organisms. ► The human visual system is exquisitely responsive to human motion. ► High-density EEG recordings show effects by 100 ms in right dorsal visual stream. ► Large-scale effects from 200 to 350 ms are sourced around bilateral hMT and STS.

Pairs of active neurons frequently fire action potentials or \"spikes\" nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons' fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron's firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.

The visual system can automatically interpolate or “fill-in” the boundaries of objects when inputs are fragmented or incomplete. A canonical class of visual stimuli known as illusory-contour (IC) stimuli has been extensively used to study this contour interpolation process. Visual evoked potential (VEP) studies have identified a neural signature of these boundary completion processes, the so-called IC-effect, which typically onsets at 90–110ms and is generated within the lateral occipital complex (LOC). Here we set out to determine the delimiting factors of automatic boundary completion with the use of illusory contour stimuli and high-density scalp recordings of brain activity. Retinal eccentricity, ratio of real to illusory contours (i.e. support ratio), and inducer diameter were each varied parametrically, and any resulting effects on the amplitude and latency of the IC-effect were examined. Somewhat surprisingly, the amplitude of the IC-effect was found to be impervious to all changes in these stimulus parameters, manipulations that are known to impact perceived illusion strength. Thus, this automatic stage of object processing appears to be a binary process in which, so-long as minimal conditions are met, contours are automatically completed. At the same time, the latency of the IC-effect was found to vary inversely with support ratio, likely reflecting the additional time necessary to interpolate across the relatively longer induced boundaries of the implied object. These data are interpreted in the context of a two stage object-recognition model that parses processing into an early automatic perceptual stage that is followed by a more effortful conceptual processing stage. ► VEPs delineate a 2-stage model of object processing. ► In the first, gaps in object contours are automatically closed. ► We tried to modulate a VEP index of this phase by varying contour parameters. ► Peak latency varied inversely with one manipulation, but amplitude was invariant.

The N1 component of the auditory evoked potential (AEP) is a robust and easily recorded metric of auditory sensory-perceptual processing. In patients with schizophrenia, a diminution in the amplitude of this component is a near-ubiquitous finding. A pair of recent studies has also shown this N1 deficit in first-degree relatives of schizophrenia probands, suggesting that the deficit may be linked to the underlying genetic risk of the disease rather than to the disease state itself. However, in both these studies, a significant proportion of the relatives had other psychiatric conditions. As such, although the N1 deficit represents an intriguing candidate endophenotype for schizophrenia, it remains to be shown whether it is present in a group of clinically unaffected first-degree relatives. In addition to testing first-degree relatives, we also sought to replicate the N1 deficit in a group of first-episode patients and in a group of chronic schizophrenia probands. Subject groups consisted of 35 patients with schizophrenia, 30 unaffected first-degree relatives, 13 first-episode patients, and 22 healthy controls. Subjects sat in a dimly lit room and listened to a series of simple 1,000-Hz tones, indicating with a button press whenever they heard a deviant tone (1,500 Hz; 17% probability), while the AEP was recorded from 72 scalp electrodes. Both chronic and first-episode patients showed clear N1 amplitude decrements relative to healthy control subjects. Crucially, unaffected first-degree relatives also showed a clear N1 deficit. This study provides further support for the proposal that the auditory N1 deficit in schizophrenia is linked to the underlying genetic risk of developing this disorder. In light of recent studies, these results point to the N1 deficit as an endophenotypic marker for schizophrenia. The potential future utility of this metric as one element of a multivariate endophenotype is discussed.

The relationship between EEG oscillations and underlying neural activity is unclear. The authors find a U-shaped relationship between the two in visual cortex that is linked to visuospatial attention performance in monkeys. A neural network model indicates a critical role for selective inputs to inhibitory neurons. A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales, from cortical columns to whole-brain networks, research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp electroencephalography (EEG) in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely used network model, we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we found that spike count correlation explained nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

Correlated variability in the spiking responses of pairs of neurons, also known as spike count correlation, is a key indicator of functional connectivity and a critical factor in population coding. Underscoring the importance of correlation as a measure for cognitive neuroscience research is the observation that spike count correlations are not fixed, but are rather modulated by perceptual and cognitive context. Yet while this context fluctuates from moment to moment, correlation must be calculated over multiple trials. This property undermines its utility as a dependent measure for investigations of cognitive processes which fluctuate on a trial-to-trial basis, such as selective attention. A measure of functional connectivity that can be assayed on a moment-to-moment basis is needed to investigate the single-trial dynamics of populations of spiking neurons. Here, we introduce the measure of population variance in normalized firing rate for this goal. We show using mathematical analysis, computer simulations and in vivo data how population variance in normalized firing rate is inversely related to the latent correlation in the population, and how this measure can be used to reliably classify trials from different typical correlation conditions, even when firing rate is held constant. We discuss the potential advantages for using population variance in normalized firing rate as a dependent measure for both basic and applied neuroscience research.

Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity's dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function.