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Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Here, we review data and modelling on the immediate neurophysiological effects of TES in vitro as well as in vivo in both humans and other animals. While it remains unclear how typical TES protocols affect neural activity, we propose that validated models of current flow should inform study design and artifacts should be carefully excluded during signal recording and analysis. Potential indirect effects of TES (e.g., peripheral stimulation) should be investigated in more detail and further explored in experimental designs. We also consider how novel technologies may stimulate the next generation of TES experiments and devices, thus enhancing validity, specificity, and reproducibility.

A critical step in the interpretation of the visual world is the integration of the various local motion signals generated by moving objects. This process is complicated by the fact that local velocity measurements can differ depending on contour orientation and spatial position. Specifically, any local motion detector can measure only the component of motion perpendicular to a contour that extends beyond its field of view. This "aperture problem" is particularly relevant to direction-selective neurons early in the visual pathways, where small receptive fields permit only a limited view of a moving object. Here we show that neurons in the middle temporal visual area (known as MT or V5) of the macaque brain reveal a dynamic solution to the aperture problem. MT neurons initially respond primarily to the component of motion perpendicular to a contour's orientation, but over a period of approximately 60 ms the responses gradually shift to encode the true stimulus direction, regardless of orientation. We also report a behavioural correlate of these neural responses: the initial velocity of pursuit eye movements deviates in a direction perpendicular to local contour orientation, suggesting that the earliest neural responses influence the oculomotor response.

Spike timing is thought to play a critical role in neural computation and communication. Methods for adjusting spike timing are therefore of great interest to researchers and clinicians alike. Transcranial electrical stimulation (tES) is a noninvasive technique that uses weak electric fields to manipulate brain activity. Early results have suggested that this technique can improve subjects' behavioral performance on a wide range of tasks and ameliorate some clinical conditions. Nevertheless, considerable skepticism remains about its efficacy, especially because the electric fields reaching the brain during tES are small, whereas the likelihood of indirect effects is large. Our understanding of its effects in humans is largely based on extrapolations from simple model systems and indirect measures of neural activity. As a result, fundamental questions remain about whether and how tES can influence neuronal activity in the human brain. Here, we demonstrate that tES, as typically applied to humans, affects the firing patterns of individual neurons in alert nonhuman primates, which are the best available animal model for the human brain. Specifically, tES consistently influences the timing, but not the rate, of spiking activity within the targeted brain region. Such effects are frequency-and location-specific and can reach deep brain structures; control experiments show that they cannot be explained by sensory stimulation or other indirect influences. These data thus provide a strong mechanistic rationale for the use of tES in humans and will help guide the development of future tES applications. tES | tACS | transcranial alternating current stimulation | spike timing | phase-locking

Single neurons carry out important sensory and motor functions related to the larger networks in which they are embedded. Understanding the relationships between single-neuron spiking and network activity is therefore of great importance and the latter can be readily estimated from low-frequency brain signals known as local field potentials (LFPs). In this work we examine a number of issues related to the estimation of spike and LFP signals. We show that spike trains and individual spikes contain power at the frequencies that are typically thought to be exclusively related to LFPs, such that simple frequency-domain filtering cannot be effectively used to separate the two signals. Ground-truth simulations indicate that the commonly used method of estimating the LFP signal by low-pass filtering the raw voltage signal leads to artifactual correlations between spikes and LFPs and that these correlations exert a powerful influence on popular metrics of spike-LFP synchronization. Similar artifactual results were seen in data obtained from electrophysiological recordings in macaque visual cortex, when low-pass filtering was used to estimate LFP signals. In contrast LFP tuning curves in response to sensory stimuli do not appear to be affected by spike contamination, either in simulations or in real data. To address the issue of spike contamination, we devised a novel Bayesian spike removal algorithm and confirmed its effectiveness in simulations and by applying it to the electrophysiological data. The algorithm, based on a rigorous mathematical framework, outperforms other methods of spike removal on most metrics of spike-LFP correlations. Following application of this spike removal algorithm, many of our electrophysiological recordings continued to exhibit spike-LFP correlations, confirming previous reports that such relationships are a genuine aspect of neuronal activity. Overall, these results show that careful preprocessing is necessary to remove spikes from LFP signals, but that when effective spike removal is used, spike-LFP correlations can potentially yield novel insights about brain function.

Visual neurons typically receive information from a limited portion of the retina, and such receptive fields are a key organizing principle for much of visual cortex. At the same time, there is strong evidence that receptive fields transiently shift around the time of saccades. The nature of the shift is controversial: Previous studies have found shifts consistent with a role for perceptual constancy; other studies suggest a role in the allocation of spatial attention. Here we present evidence that both the previously documented functions exist in individual neurons in primate cortical area V4. Remapping associated with perceptual constancy occurs for saccades in all directions, while attentional shifts mainly occur for neurons with receptive fields in the same hemifield as the saccade end point. The latter are relatively sluggish and can be observed even during saccade planning. Overall these results suggest a complex interplay of visual and extraretinal influences during the execution of saccades.

Visual neurons are often characterized in terms of their tuning for various stimulus properties, such as shape, color, and velocity. Generally, these tuning curves are further modulated by the overall intensity of the stimulus, such that increasing the contrast increases the firing rate, up to some maximum. In this paper, we describe the tuning of neurons in the middle temporal area (MT or V5) of macaque visual cortex for moving stimuli of varying contrast. We find that, for some MT neurons, tuning curves for stimulus direction, speed, and size are shaped in part by suppressive influences that are present at high stimulus contrast but weak or nonexistent at low contrast. For most neurons, the suppression is direction-specific and strongest for large, slow-moving stimuli. The surprising consequence of this phenomenon is that some MT neurons actually fire more vigorously to a large low-contrast stimulus than to one of high contrast. These results are consistent with recent perceptual observations, as well as with information-theoretic models, which hypothesize that the visual system seeks to reduce redundancy at high contrast while maintaining sensitivity at low contrast.

Neurons in the medial superior temporal (MST) area of the primate visual cortex respond selectively to complex motion patterns defined by expansion, rotation, and deformation. Consequently they are often hypothesized to be involved in important behavioral functions, such as encoding the velocities of moving objects and surfaces relative to the observer. However, the computations underlying such selectivity are unknown. In this work we have developed a unique, naturalistic motion stimulus and used it to probe the complex selectivity of MST neurons. The resulting data were then used to estimate the properties of the feed-forward inputs to each neuron. This analysis yielded models that successfully accounted for much of the observed stimulus selectivity, provided that the inputs were combined via a nonlinear integration mechanism that approximates a multiplicative interaction among MST inputs. In simulations we found that this type of integration has the functional role of improving estimates of the 3D velocity of moving objects. As this computation is of general utility for detecting complex stimulus features, we suggest that it may represent a fundamental aspect of hierarchical sensory processing.receptive field | optic flow I n the early stages of the primate visual system the receptive fields of neurons can be readily estimated from the responses to simple stimuli such as spots, bars, and gratings or even by hand mapping (1-3). However, for neurons farther along the visual pathways, the relationship between stimulus input and neuronal output is often far from obvious, particularly in areas that respond to complex stimuli such as faces, objects, or optic flow patterns (4-7). Uncovering this relationship is crucial for understanding the computations that underlie important behavioral functions such as object recognition and navigation.One well-known example of complex cortical processing is the range of selectivities found in the medial superior temporal (MST) area of the primate visual cortex. Previous work has shown that MST neurons are highly selective for visual stimuli composed of combinations of motion patterns such as expansion, deformation, translation, and rotation (8-12). Although this selectivity has been documented many times over the last 25 y, very little is known about the computations by which it is derived. One prevalent hypothesis is that the selectivity of MST neurons is determined by specific strategies used by the brain to calculate one's direction of motion, or heading, through the world (13-15). In these models, heading is computed by combining the output of detectors tuned to specific motion patterns, and these patterns are reflected in the internal structure of an MST neuron's receptive field.Although this hierarchical account of MST selectivity is appealingly simple, it has been difficult to confirm experimentally. Indeed previous studies have concluded that MST responses to complex stimuli often cannot be predicted, even qualitatively, from their responses to simple ones (7-9, 16). For ...