Patrick J. Mineault

Removal of Spurious Correlations Between Spikes and Local Field Potentials

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.

Hierarchical processing of complex motion along the primate dorsal visual pathway

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.

A Sensorimotor Role for Traveling Waves in Primate Visual Cortex

Traveling waves of neural activity are frequently observed to occur in concert with the presentation of a sensory stimulus or the execution of a movement. Although such waves have been studied for decades, little is known about their function. Here we show that traveling waves in the primate extrastriate visual cortex provide a means of integrating sensory and motor signals. Specifically, we describe a traveling wave of local field potential (LFP) activity in cortical area V4 of macaque monkeys that is triggered by the execution of saccadic eye movements. These waves sweep across the V4 retinotopic map, following a consistent path from the foveal to the peripheral representations of space; their amplitudes correlate with the direction and size of each saccade. Moreover, these waves are associated with a reorganization of the postsaccadic neuronal firing patterns, which follow a similar retinotopic progression, potentially prioritizing the processing of behaviorally relevant stimuli.

Spatial clustering of tuning in mouse primary visual cortex

The primary visual cortex of higher mammals is organized into two-dimensional maps, where the preference of cells for stimulus parameters is arranged regularly on the cortical surface. In contrast, the preference of neurons in the rodent appears to be arranged randomly, in what is termed a salt-and-pepper map. Here we revisited the spatial organization of receptive fields in mouse primary visual cortex by measuring the tuning of pyramidal neurons in the joint orientation and spatial frequency domain. We found that the similarity of tuning decreases as a function of cortical distance, revealing a weak but statistically significant spatial clustering. Clustering was also observed across different cortical depths, consistent with a columnar organization. Thus, the mouse visual cortex is not strictly a salt-and-pepper map. At least on a local scale, it resembles a degraded version of the organization seen in higher mammals, hinting at a possible common origin.

Enhanced Spatial Resolution During Locomotion and Heightened Attention in Mouse Primary Visual Cortex

We do not fully understand how behavioral state modulates the processing and transmission of sensory signals. Here, we studied the cortical representation of the retinal image in mice that spontaneously switched between a state of rest and a constricted pupil, and one of active locomotion and a dilated pupil, indicative of heightened attention. We measured the selectivity of neurons in primary visual cortex for orientation and spatial frequency, as well as their response gain, in these two behavioral states. Consistent with prior studies, we found that preferred orientation and spatial frequency remained invariant across states, whereas response gain increased during locomotion relative to rest. Surprisingly, relative gain, defined as the ratio between the gain during locomotion and the gain during rest, was not uniform across the population. Cells tuned to high spatial frequencies showed larger relative gain compared with those tuned to lower spatial frequencies. The preferential enhancement of high-spatial-frequency information was also reflected in our ability to decode the stimulus from population activity. Finally, we show that changes in gain originate from shifts in the operating point of neurons along a spiking nonlinearity as a function of behavioral state. Differences in the relative gain experienced by neurons with high and low spatial frequencies are due to corresponding differences in how these cells shift their operating points between behavioral states. SIGNIFICANCE STATEMENT How behavioral state modulates the processing and transmission of sensory signals remains poorly understood. Here, we show that the mean firing rate and neuronal gain increase during locomotion as a result in a shift of the operating point of neurons. We define relative gain as the ratio between the gain of neurons during locomotion and rest. Interestingly, relative gain is higher in cells with preferences for higher spatial frequencies than those with low-spatial-frequency selectivity. This means that, during a state of locomotion and heightened attention, the population activity in primary visual cortex can support better spatial acuity, a phenomenon that parallels the improved spatial resolution observed in human subjects during the allocation of spatial attention.

Local field potentials reflect multiple spatial scales in V4

Local field potentials (LFP) reflect the properties of neuronal circuits or columns recorded in a volume around a microelectrode (Buzsáki et al., 2012). The extent of this integration volume has been a subject of some debate, with estimates ranging from a few hundred microns (Katzner et al., 2009; Xing et al., 2009) to several millimeters (Kreiman et al., 2006). We estimated receptive fields (RFs) of multi-unit activity (MUA) and LFPs at an intermediate level of visual processing, in area V4 of two macaques. The spatial structure of LFP receptive fields varied greatly as a function of time lag following stimulus onset, with the retinotopy of LFPs matching that of MUAs at a restricted set of time lags. A model-based analysis of the LFPs allowed us to recover two distinct stimulus-triggered components: an MUA-like retinotopic component that originated in a small volume around the microelectrodes (~350 μm), and a second component that was shared across the entire V4 region; this second component had tuning properties unrelated to those of the MUAs. Our results suggest that the LFP reflects neural activity across multiple spatial scales, which both complicates its interpretation and offers new opportunities for investigating the large-scale structure of network processing.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Functional connectivity during surround suppression in macaque area V4

Surround suppression is a common feature of sensory neurons. For neurons of the visual cortex, it occurs when a visual stimulus extends beyond a neurons classical receptive field, reducing the neurons firing rate. While several studies have been attributing the suppression effect on horizontal, long-range lateral or feedback connections, the underlying circuitry for surround modulation remain unidentified. Since most of these models have been relying on single neuron recordings, the contribution of lateral connections can only be suggested from the surround field properties. A more straightforward approach would be to detect these connections and their dynamics using simultaneous recordings from multiple neurons in one or more visual areas. We have developed a method for estimating these connections and we analyzed data obtained from 100-electrode Utah arrays chronically implanted into area V4 of the macaque monkey. Using a method based on the nonlinear Volterra modeling approach, we computed estimates of the strength and statistical reliability of connections among neurons, including nonlinear interactions and excitatory and inhibitory connections. Our results thus far reveal a pattern of connectivity within V4 that conforms to the results of previous anatomical work: Excitatory connections are far more common than inhibitory connections (∼65%), stronger connections are found among neurons that are physically near one another, and connections are stronger among neurons with similar receptive field properties. However, this connectivity is capable of reorganizing on short time scales according to the stimulus: Stimuli that evoke strong suppression at the single-unit level introduce stronger inhibition among V4 neurons, identifying recurrent connectivity as the source of the suppression. Overall, these results provide insight into the dynamic nature of neuronal organization within V4 and its contribution to surround suppression.