Phase Locking of Multiple Single Neurons to the Local Field Potential in Cat V1

Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings

Recording neuronal activity using multiple electrodes has been widely used to understand the functional mechanisms of the brain. Increasing the number of electrodes allows us to decode more variety of functionalities. However, handling massive amounts of multichannel electrophysiological data is still challenging due to the limited hardware resources and unavoidable thermal tissue damage. Here, we present machine learning (ML)-based reconstruction of high-frequency neuronal spikes from subsampled low-frequency band signals. Inspired by the equivalence between high-frequency restoration and super-resolution in image processing, we applied a transformer ML model to neuronal data recorded from both in vitro cultures and in vivo male mouse brains. Even with the x8 downsampled datasets, our trained model reasonably estimated high-frequency information of spiking activity, including spike timing, waveform, and network connectivity. With our ML-based data reduction applicable to existing multichannel recording hardware while achieving neuronal signals of broad bandwidths, we expect to enable more comprehensive analysis and control of brain functions.

How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output

Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.

Spontaneous Spiking Is Governed by Broadband Fluctuations

Populations of cortical neurons generate rhythmic fluctuations in their ongoing spontaneous activity. These fluctuations can be seen in the local field potential (LFP), which reflects summed return currents from synaptic activity in the local population near a recording electrode. The LFP is spectrally broad, and many researchers view this breadth as containing many narrowband oscillatory components that may have distinct functional roles. This view is supported by the observation that the phase of narrowband oscillations is often correlated with cortical excitability and can relate to the timing of spiking activity and the fidelity of sensory evoked responses. Accordingly, researchers commonly tune in to these channels by narrowband filtering the LFP. Alternatively, neural activity may be fundamentally broadband and composed of transient, nonstationary rhythms that are difficult to approximate as oscillations. In this view, the instantaneous state of the broad ensemble relates directly to the excitability of the local population with no particular allegiance to any frequency band. To test between these alternatives, we asked whether the spiking activity of neocortical neurons in marmoset of either sex is better aligned with the phase of the LFP within narrow frequency bands or with a broadband measure. We find that the phase of broadband LFP fluctuations provides a better predictor of spike timing than the phase after filtering in narrow bands. These results challenge the view of the neocortex as a system composed of narrowband oscillators and supports a view in which neural activity fluctuations are intrinsically broadband.SIGNIFICANCE STATEMENT Research into the dynamical state of neural populations often attributes unique significance to the state of narrowband oscillatory components. However, rhythmic fluctuations in cortical activity are nonstationary and broad spectrum. We find that the timing of spontaneous spiking activity is better captured by the state of broadband fluctuations over any latent oscillatory component. These results suggest narrowband interpretations of rhythmic population activity may be limited, and broader representations may provide higher fidelity in describing moment-to-moment fluctuations in cortical activity.

Spike-Gamma Phase Relationship in the Visual Cortex

Gamma oscillations (30-70 Hz) have been hypothesized to play a role in cortical function. Most of the proposed mechanisms involve rhythmic modulation of neuronal excitability at gamma frequencies, leading to modulation of spike timing relative to the rhythm. I first show that the gamma band could be more privileged than other frequencies in observing spike-field interactions even in the absence of genuine gamma rhythmicity and discuss several biases in spike-gamma phase estimation. I then discuss the expected spike-gamma phase according to several hypotheses. Inconsistent with the phase-coding hypothesis (but not with others), the spike-gamma phase does not change with changes in stimulus intensity or attentional state, with spikes preferentially occurring 2-4 ms before the trough, but with substantial variability. However, this phase relationship is expected even when gamma is a byproduct of excitatory-inhibitory interactions. Given that gamma occurs in short bursts, I argue that the debate over the role of gamma is a matter of semantics. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Sensory representation of visual stimuli in the coupling of low-frequency phase to spike times

Neural synchronization has been engaged in several brain mechanisms. Previous studies have shown that the interaction between the time of spiking activity and phase of local field potentials (LFPs) plays a key role in many cognitive functions. However, the potential role of this spike-LFP phase coupling (SPC) in neural coding is not fully understood. Here, we sought to investigate the role of this SPC for encoding the sensory properties of visual stimuli. To this end, we measured SPC strength in the preferred and anti-preferred motion directions of stimulus presented inside the receptive field of middle temporal (MT) neurons. We found a selective response in terms of SPC strength for different directions of motion. Remarkably, this selective function is inverted with respect to the spiking activity. In other words, the least SPC occurs for the preferred direction (based on the spike rate), and vice versa; the strongest SPC is induced in the anti-preferred direction. Altogether, these findings imply that the neural system may use spike-LFP phase coupling in the primate visual cortex to encode sensory information.

Ultrasonic thalamic stimulation modulates neural activity of thalamus and motor cortex in the mouse

Objective.Previous studies have demonstrated that ultrasound thalamic stimulation (UTS) can treat disorders of consciousness. However, it is still unclear how UTS modulates neural activity in the thalamus and cortex.Approach.In this study, we performed UTS in mice and recorded the neural activities including spike and local field potential (LFP) of the thalamus and motor cortex (M1). We analyzed the firing rate of spikes and the power spectrum of LFPs and evaluated the coupling relationship between LFPs from the thalamus and M1 with Granger causality.Main results.Our results clearly indicate that UTS can directly induce neural activity in the thalamus and indirectly induce neural activity in the M1. We also found that there is a strong connection relationship of neural activity between thalamus and M1 under UTS.Significance.These results demonstrate that UTS can modulate the neural activity of the thalamus and M1 in mice. It has the potential to provide guidance for the ultrasound treatment of thalamus-related diseases.

Phase-Specific Microstimulation Differentially Modulates Beta Oscillations and Affects Behavior

It is widely accepted that Beta-band oscillations play a role in sensorimotor behavior. To further explore this role, we developed a hybrid platform to combine neural operant conditioning and phase-specific intracortical microstimulation (ICMS). We trained monkeys, implanted with 96 electrode arrays in the motor cortex, to volitionally enhance local field potential (LFP) Beta-band (20-30 Hz) activity at selected sites using a brain-machine interface. We find that Beta oscillations of LFP and single-unit spiking activity increase dramatically with brain-machine interface training and that pre-movement Beta power is anti-correlated with task performance. We also find that phase-specific ICMS modulates the power and phase of oscillations, shifting local networks between oscillatory and non-oscillatory states. Furthermore, ICMS induces phase-dependent effects in animal reaction times and success rates. These findings contribute to unraveling the functional role of cortical oscillations and to the future development of clinical tools for ameliorating abnormal neuronal activities in brain disease.

Adaptation Modulates Spike-Phase Coupling Tuning Curve in the Rat Primary Auditory Cortex

Adaptation is an important mechanism that causes a decrease in the neural response both in terms of local field potentials (LFP) and spiking activity. We previously showed this reduction effect in the tuning curve of the primary auditory cortex. Moreover, we revealed that a repeated stimulus reduces the neural response in terms of spike-phase coupling (SPC). In the current study, we examined the effect of adaptation on the SPC tuning curve. To this end, employing the phase-locking value (PLV) method, we estimated the spike-LFP coupling. The data was obtained by a simultaneous recording from four single-electrodes in the primary auditory cortex of 15 rats. We first investigated whether the neural system may use spike-LFP phase coupling in the primary auditory cortex to encode sensory information. Secondly, we investigated the effect of adaptation on this potential SPC tuning. Our data showed that the coupling between spikes’ times and the LFP phase in beta oscillations represents sensory information (different stimulus frequencies), with an inverted bell-shaped tuning curve. Furthermore, we showed that adaptation to a specific frequency modulates SPC tuning curve of the adapter and its neighboring frequencies. These findings could be useful for interpretation of feature representation in terms of SPC and the underlying neural mechanism of adaptation.

Simultaneous spike-time locking to multiple frequencies

Locking of neural firing is ubiquitously observed in the brain and occurs when neurons fire at a particular phase or in synchronization with an external signal. Here we study in detail the locking of single neurons to multiple distinct frequencies at the example of p-type electroreceptor afferents in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus (brown ghost knifefish). We find that electrosensory afferents and pyramidal cells in the electrosensory lateral line lobe (ELL) lock to multiple frequencies, including the electric organ discharge (EOD) frequency, beat, and stimulus itself. We identify key elements necessary for locking to multiple frequencies, study its limits, and provide concise mathematical models reproducing our main findings. Our findings provide another example of how rate and temporal codes can coexist and complement each other in single neurons and demonstrate that sensory coding in p-type electroreceptor afferents provides a much richer representation of the sensory environment than commonly assumed. Since the underlying mechanisms are not specific to the electrosensory system, our results could provide the basis for studying multiple frequency locking in other systems.NEW & NOTEWORTHY Locking of neuronal spikes to external and internal signals is a ubiquitous neurophysiological mechanism that has been extensively studied in several brain areas and species. Using experimental data from the electrosensory system and concise mathematical models, we analyze how a single neuron can simultaneously lock to multiple frequencies. Our findings demonstrate how temporal and rate codes can complement each other and lead to rich neuronal representations of sensory signals.

Layer-specific representation of long-lasting sustained activity in the rat auditory cortex

In the auditory system, distinct and reproducible transient activities responding to the onset of sound have long been the focus when characterizing the auditory cortex, i.e., tonotopic maps, subregions, and layer-specific representation. There is limited information on sustained activities because the rapid adaptation impairs reproducibility and the signal-to-noise ratio. We recently overcame this problem by focusing on neural synchrony and machine learning demonstrated that band-specific power and the phase locking value (PLV) represent sound information in a tonotopic and region-specific manner. Here, we attempted to reveal the layer-specific representation of sustained activities. A microelectrode array recorded sustained activities from layers 2/3, 4, and 5/6 of the rat auditory cortex. We characterized band-specific power and PLV patterns and applied sparse logistic regression (SLR) to discriminate (1) between the sound-induced and spontaneous activities and (2) five test frequencies from the sound-induced activities in each layer. SLR achieved the highest discrimination performance in high-gamma activities in layers 4 and 5/6, higher than in layer 2/3, indicating poor sound representation in layer 2/3. Moreover, the recording sites that contributed to the discrimination in layers 4 and 5/6 had a characteristic frequency similar to the test frequency and were often located in the belt area, indicating tonotopic and region-specific representation. These results indicate that information processing of sustained activities may depend on high-gamma oscillators, i.e., cortical inhibitory interneurons, and reflects layer-specific thalamocortical and corticocortical neural circuits in the auditory system, which may contribute to associative information processing beyond sound frequency in auditory perception.

Abnormal Phase Coupling in Parkinson's Disease and Normalization Effects of Subthreshold Vestibular Stimulation

The human brain is a highly dynamic structure requiring dynamic coordination between different neural systems to perform numerous cognitive and behavioral tasks. Emerging perspectives on basal ganglia (BG) and thalamic functions have highlighted their role in facilitating and mediating information transmission among cortical regions. Thus, changes in BG and thalamic structures can induce aberrant modulation of cortico-cortical interactions. Recent work in deep brain stimulation (DBS) has demonstrated that externally applied electrical current to BG structures can have multiple downstream effects in large-scale brain networks. In this work, we identified EEG-based altered resting-state cortical functional connectivity in Parkinson's disease (PD) and examined effects of dopaminergic medication and electrical vestibular stimulation (EVS), a non-invasive brain stimulation (NIBS) technique capable of stimulating the BG and thalamus through vestibular pathways. Resting EEG was collected from 16 PD subjects and 18 age-matched, healthy controls (HC) in four conditions: sham (no stimulation), EVS1 (4-8 Hz multisine), EVS2 (50-100 Hz multisine) and EVS3 (100-150 Hz multisine). The mean, variability, and entropy were extracted from time-varying phase locking value (PLV), a non-linear measure of pairwise functional connectivity, to probe abnormal cortical couplings in the PD subjects. We found the mean PLV of Cz and C3 electrodes were important for discrimination between PD and HC subjects. In addition, the PD subjects exhibited lower variability and entropy of PLV (mostly in theta and alpha bands) compared to the controls, which were correlated with their clinical characteristics. While levodopa medication was effective in normalizing the mean PLV only, all EVS stimuli normalized the mean, variability and entropy of PLV in the PD subject, with the exact extent and duration of improvement a function of stimulus type. These findings provide evidence demonstrating both low- and high-frequency EVS exert widespread influences on cortico-cortical connectivity, likely via subcortical activation. The improvement observed in PD in a stimulus-dependent manner suggests that EVS with optimized parameters may provide a new non-invasive means for neuromodulation of functional brain networks.

Phase shifts in high-beta- and low-gamma-band local field potentials predict the focus of visual spatial attention

The local field potential (LFP) contains rich information about activity in local neuronal populations. However, it has been challenging to establish direct links between LFP modulations and task-relevant behavior or cognitive processes, such as attention. We sought to determine whether LFP amplitude or phase modulations are predictive of the allocation of visual spatial attention. LFPs were recorded simultaneously in multiple early visual brain structures of alert macaque monkeys performing attention-demanding detection and discrimination tasks. Attention directed toward the receptive field of recorded neurons generated systematically larger phase shifts in high-beta- and low-gamma-frequency LFPs compared with LFP phase shifts on trials in which attention was directed away from the receptive field. This attention-mediated temporal advance corresponded to ~10 ms. LFP phase shifts also correlated with reaction times when monkeys were engaged in the tasks. Importantly, attentional modulation of LFP phase was consistent across monkeys, tasks, visual brain structures, and cortical layers. In contrast, attentional modulation of LFP amplitude varied across frequency bands, visual structures/layers, and tasks. Because LFP phase shifts were robust, consistent, and predictive of spatial attention, they could serve as a reliable marker for attention signals in the brain. NEW & NOTEWORTHY Local field potentials (LFPs) reflect the activity of spatially localized populations of neurons. Whether alterations in LFP activity are indicative of cognitive processes, such as attention, is unclear. We found that shifts in the phase of LFPs measured in multiple visual brain areas reliably predicted the focus of spatial attention. LFP phase shifts could therefore serve as a marker for behaviorally relevant attention signals in the brain.

Neuronal coding of multiscale temporal features in communication sequences within the bat auditory cortex

Experimental evidence supports that cortical oscillations represent multiscale temporal modulations existent in natural stimuli, yet little is known about the processing of these multiple timescales at a neuronal level. Here, using extracellular recordings from the auditory cortex (AC) of awake bats (Carollia perspicillata), we show the existence of three neuronal types which represent different levels of the temporal structure of conspecific vocalizations, and therefore constitute direct evidence of multiscale temporal processing of naturalistic stimuli by neurons in the AC. These neuronal subpopulations synchronize differently to local-field potentials, particularly in theta- and high frequency bands, and are informative to a different degree in terms of their spike rate. Interestingly, we also observed that both low and high frequency cortical oscillations can be highly informative about the listened calls. Our results suggest that multiscale neuronal processing allows for the precise and non-redundant representation of natural vocalizations in the AC.

Rhythm and Synchrony in a Cortical Network Model

We studied mechanisms for cortical gamma-band activity in the cerebral cortex and identified neurobiological factors that affect such activity. This was done by analyzing the behavior of a previously developed, data-driven, large-scale network model that simulated many visual functions of monkey V1 cortex (Chariker et al., 2016). Gamma activity was an emergent property of the model. The model's gamma activity, like that of the real cortex, was (1) episodic, (2) variable in frequency and phase, and (3) graded in power with stimulus variables like orientation. The spike firing of the model's neuronal population was only partially synchronous during multiple firing events (MFEs) that occurred at gamma rates. Detailed analysis of the model's MFEs showed that gamma-band activity was multidimensional in its sources. Most spikes were evoked by excitatory inputs. A large fraction of these inputs came from recurrent excitation within the local circuit, but feedforward and feedback excitation also contributed, either through direct pulsing or by raising the overall baseline. Inhibition was responsible for ending MFEs, but disinhibition led directly to only a small minority of the synchronized spikes. As a potential explanation for the wide range of gamma characteristics observed in different parts of cortex, we found that the relative rise times of AMPA and GABA synaptic conductances have a strong effect on the degree of synchrony in gamma.SIGNIFICANCE STATEMENT Canonical computations used throughout the cerebral cortex are performed in primary visual cortex (V1). Providing theoretical mechanisms for these computations will advance understanding of computation throughout cortex. We studied one dynamical feature, gamma-band rhythms, in a large-scale, data-driven, computational model of monkey V1. Our most significant conclusion is that the sources of gamma band activity are multidimensional. A second major finding is that the relative rise times of excitatory and inhibitory synaptic potentials have strong effects on spike synchrony and peak gamma band power. Insight gained from studying our V1 model can shed light on the functions of other cortical regions.

Translaminar circuits formed by the pyramidal cells in the superficial layers of cat visual cortex

Pyramidal cells in the superficial layers of the neocortex provide a major excitatory projection to layer 5, which contains the pyramidal cells that project to subcortical motor-related targets. Both structurally and functionally rather little is known about this interlaminar pathway, especially in higher mammals. Here, we made sparse ultrastructural reconstructions of the projection to layer 5 of three pyramidal neurons from layer 3 in cat V1 whose morphology, physiology, and synaptic connections with layers 2 and 3 were known. The dominant targets of the 74 identified synapses in layer 5 were the dendritic spines of pyramidal cells. The fractions of target spiny dendrites were 59, 61, and 84% for the three cells, with the remaining targets being dendrites of smooth neurons. These fractions were similar to the distribution of targets of unlabeled asymmetric synapses in the surrounding neuropil. Serial section reconstructions revealed that the target dendrites were heterogenous in morphology, indicating that different cell types are innervated. This new evidence indicates that the descending projection from the superficial layer pyramidal cells does not simply drive the output pyramidal cells that project to cortical and subcortical targets, but participates in the complex circuitry of the deep cortical layers.

How Close Are We to Understanding What (if Anything) γ Oscillations Do in Cortical Circuits?

γ oscillations, which can be identified by rhythmic electrical signals ∼30-100 Hz, consist of interactions between excitatory and inhibitory neurons that result in rhythmic inhibition capable of entraining firing within local cortical circuits. Many possible mechanisms have been described through which γ oscillations could act on cortical circuits to modulate their responses to input, alter their patterns of activity, and/or enhance the efficacy of their outputs onto downstream targets. Recently, several studies have observed changes in behavior after optogenetically manipulating neocortical γ oscillations. Now, future studies should determine whether these manipulations elicit physiological correlates associated with specific mechanisms through which γ oscillations are hypothesized to modulate cortical circuit function. There are numerous such mechanisms, so identifying which ones are actually engaged by optogenetic manipulations known to affect behavior would help flesh out exactly how γ oscillations contribute to cortical circuit function under normal and/or pathological conditions.

Thalamic Bursts Down-regulate Cortical Theta and Nociceptive Behavior

We tested the relation between pain behavior, theta (4-8 Hz) oscillations in somatosensory cortex and burst firing in thalamic neurons in vivo. Optically-induced thalamic bursts attenuated cortical theta and mechanical allodynia. It is proposed that thalamic bursts are an adaptive response to pain that de-synchronizes cortical theta and decreases sensory salience.

Neural Mechanisms of Selective Visual Attention

Selective visual attention describes the tendency of visual processing to be confined largely to stimuli that are relevant to behavior. It is among the most fundamental of cognitive functions, particularly in humans and other primates for whom vision is the dominant sense. We review recent progress in identifying the neural mechanisms of selective visual attention. We discuss evidence from studies of different varieties of selective attention and examine how these varieties alter the processing of stimuli by neurons within the visual system, current knowledge of their causal basis, and methods for assessing attentional dysfunctions. In addition, we identify some key questions that remain in identifying the neural mechanisms that give rise to the selective processing of visual information.

Artefactual origin of biphasic cortical spike-LFP correlation

Electrophysiological data acquisition systems introduce various distortions into the signals they record. While such distortions were discussed previously, their effects are often not appreciated. Here I show that the biphasic shape of cortical spike-triggered LFP average (stLFP), reported in multiple studies, is likely an artefact introduced by high-pass filter of the neural data acquisition system when the actual stLFP has a single trough around the zero lag.