Firing Rate Of Single Neurons Research Articles

Distractor-Specific Single Neuron Activity Predicts Visual Working Memory Task Outcomes


 
 
 
 This paper explores the relationship between neural activity and behavioral performance in the form of visual working memory (VWM) task outcomes, by answering the question: Are there any significant differences in the firing rates of individual neurons during the distractor presentation period of a VWM task between success and error trials that can predict the outcome of a trial? Distractor-specific single neuron firing rates during a VWM task were analyzed to answer this question. A logistic regression was used to identify the predictive capability of neural firing rate on trial outcome with the neural activity of 51 cells from the lateral prefrontal cortex (LPFC) of a primate. This study found that a best-fit logistic model could predict the behavioral performance of the primate (success or error of the VWM task) with 63.01% accuracy, with additional machine learning techniques producing scores upwards of 68% accuracy. Moreover, greater firing rates in response to the distractor, indicating less efficient distractor suppression, accompanied the error trials of the VWM task. This suggests that stronger neural responses to task-specific distractors can hinder the attentional filtering required for efficient working memory, supporting previous research that found that distractor suppression is a mechanism that heavily influences WM efficiency. These findings indicate that people, particularly children, with disorders that affect WM capacity such as ADHD may experience stronger neural responses to distractors, and therefore inefficient distractor suppression, at the single neuron level when engaging in goal-oriented behaviors, which can significantly impact learning and other developmental processes.
 
 
 

Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks.

Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.

Spiking burstiness and working memory in the human medial temporal lobe.

Persistent activity has commonly been considered to be a hallmark of working memory (WM). Recent evidence indicates that neuronal discharges in the medial temporal lobe (MTL) are compatible with WM neural patterns observed in cortical areas. However, the characterization of this activity rarely consists of measurements other than firing rates of single neurons. Moreover, a varied repertoire of firing dynamics has been reported in the MTL regions, which motivate the more detailed examination of the relationships between WM processes and discharge patterns undertaken here. Specifically, we investigate' at different resolution levels, firing irregularities in electrode recordings from the hippocampus, amygdala, and the entorhinal cortex of epileptic patients during a WM task. We show that some types of (ir)regularities predict response times of the patients depending on the trial periods under consideration. Prominent burst activity at the population level is observed in the amygdala and entorhinal cortex during memory retrieval. In general, regular and bursty neurons contribute to the decoding of the memory load, yet they display important differences across the three anatomical areas. Our results suggest that nonrandom (non-Poisson) patterns are relevant for WM, which calls for the development and use of statistics complementary to mere spike counts.

A Dopamine D1 Agonist Versus Methylphenidate in Modulating Prefrontal Cortical Working Memory.

Methylphenidate is used widely to treat symptoms of attention-deficit/hyperactivity disorder (ADHD), but like other stimulants has significant side effects. This study used a rodent model (spontaneously hypertensive rat) of spatial working memory (sWM) to compare the effects of methylphenidate with the novel dopamine D1-like receptor agonist 2-methyldihydrexidine. Acute oral administration of methylphenidate (1.5 mg/kg) caused sWM improvement in half of the tested rats, but impairment in the others. Both improvement or impairment were eliminated by administration of the D1 antagonist SCH39266 directly into the prefrontal cortex (PFC). Conversely, 2-methyldihydrexidine showed greater sWM improvement compared with methylphenidate without significant impairment in any subject. Its effects correlated negatively with vehicle-treated baseline performance (i.e., rats with lower baseline performance improved more than rats with higher baseline performance). These behavioral effects were associated with neural activities in the PFC. Single neuron firing rate was changed, leading to the alteration in neuronal preference to correct or error behavioral responses. Overall, 2-methyldihydrexidine was superior to methylphenidate in decreasing the neuronal preference, prospectively, in the animals whose behavior was improved. In contrast, methylphenidate, but not 2-methyldihydrexidine, significantly decreased neuronal preference, retrospectively, in those animals who had impaired performance. These results suggest that a D1 agonist may be more effective than methylphenidate in regulating sWM-related behavior through neural modulation of the PFC, and thus may be superior to methylphenidate or other stimulants as ADHD pharmacotherapy. SIGNIFICANCE STATEMENT: Methylphenidate is effective in ADHD by its indirect agonist stimulation of dopamine and/or adrenergic receptors, but the precise effects on specific targets are unclear. This study compared methylphenidate to a dopamine D1 receptor-selective agonist by investigating effects on working memory occurring via neural modulation in the prefrontal cortex. The data suggest that pharmacological treatment selectively targeting the dopamine D1 may offer a superior approach to ADHD pharmacotherapy.

Neurons in the mouse brain correlate with cryptocurrency price: a cautionary tale

In this paper I report the discovery of neurons which showed a neural correlate with ongoing fluctuations of Bitcoin and Ethereum prices at the time of the recording. I used the publicly available dataset of Neuropixel recordings by the Allen Institute to correlate the firing rate of single neurons with cryptocurrency price. Out of ~40.000 recorded single neurons, ~70% showed a significant correlation with Bitcoin or Ethereum prices. Even when using the conservative Bonferroni correction for multiple comparisons, ~35% of neurons showed a significant correlation, which is well above the expected false positive rate of 5%. These results were due to "nonsense correlations": when correlating two signals which both evolve slowly over time, the chances of finding a significant correlation between the two are much higher than when comparing signals which lack this property.

State-space optimal feedback control of optogenetically driven neural activity

Objective. The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse. Approach. Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch. Main results. This model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications. Significance. To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches laid out and tested here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.

An emergent neural coactivity code for dynamic memory.

Neural correlates of external variables provide potential internal codes that guide an animal’s behaviour. Notably, first-order features of neural activity, such as single-neuron firing rates, have been implicated in encoding information. However, the extent to which higher-order features, such as multi-neuron coactivity, play primary roles in encoding information or secondary roles in supporting single-neuron codes remains unclear. Here we show that millisecond-timescale coactivity amongst hippocampal CA1 neurons discriminates distinct millisecond-lived behavioural contingencies. This contingency discrimination was unrelated to the tuning of individual neurons but instead an emergent property of their coactivity. Contingency discriminating patterns were reactivated offline after learning and their reinstatement predicted trial-by-trial memory performance. Moreover, optogenetic suppression of inputs from the upstream CA3 region selectively during learning impaired coactivity-based contingency information in CA1 and subsequent dynamic memory retrieval. These findings identify coactivity as a primary feature of neural firing that discriminates distinct behaviourally-relevant variables and supports memory retrieval.

Neurons in the Nonhuman Primate Amygdala and Dorsal Anterior Cingulate Cortex Signal Aversive Memory Formation under Sedation.

Anesthetics aim to prevent memory of unpleasant experiences. The amygdala and dorsal anterior cingulate cortex participate in forging emotional and valence-driven memory formation. It was hypothesized that this circuitry maintains its role under sedation. Two nonhuman primates underwent aversive tone-odor conditioning under sedative states induced by ketamine or midazolam (1 to 8 and 0.1 to 0.8 mg/kg, respectively). The primary outcome was behavioral and neural evidence suggesting memory formation. This study simultaneously measured conditioned inspiratory changes and changes in firing rate of single neurons in the amygdala and the dorsal anterior cingulate cortex in response to an expected aversive olfactory stimulus appearing during acquisition and tested their retention after recovery. Aversive memory formation occurred in 26 of 59 sessions under anesthetics (16 of 29 and 10 of 30, 5 of 30 and 21 of 29 for midazolam and ketamine at low and high doses, respectively). Single-neuron responses in the amygdala and dorsal anterior cingulate cortex were positively correlated between acquisition and retention (amygdala, n = 101, r = 0.51, P < 0.001; dorsal anterior cingulate cortex, n = 121, r = 0.32, P < 0.001). Neural responses during acquisition under anesthetics were stronger in sessions exhibiting memory formation than those that did not (amygdala median response ratio, 0.52 versus 0.33, n = 101, P = 0.021; dorsal anterior cingulate cortex median response ratio, 0.48 versus 0.32, n = 121, P = 0.012). The change in firing rate of amygdala neurons during acquisition was correlated with the size of stimuli-conditioned inspiratory response during retention (n = 101, r = 0.22 P = 0.026). Thus, amygdala and dorsal anterior cingulate cortex responses during acquisition under anesthetics predicted retention. Respiratory unconditioned responses to the aversive odor anesthetics did not differ from saline controls. These results suggest that the amygdala-dorsal anterior cingulate cortex circuit maintains its role in acquisition and maintenance of aversive memories in nonhuman primates under sedation with ketamine and midazolam and that the stimulus valence is sufficient to drive memory formation.

Frequency-Specific Optogenetic Deep Brain Stimulation of Subthalamic Nucleus Improves Parkinsonian Motor Behaviors.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy for the motor symptoms of Parkinson's disease (PD). However, the neural elements mediating symptom relief are unclear. A previous study concluded that direct optogenetic activation of STN neurons was neither necessary nor sufficient for relief of parkinsonian symptoms. However, the kinetics of the channelrhodopsin-2 (ChR2) used for cell-specific activation are too slow to follow the high rates required for effective DBS, and thus the contribution of activation of STN neurons to the therapeutic effects of DBS remains unclear. We quantified the behavioral and neuronal effects of optogenetic STN DBS in female rats following unilateral 6-hydroxydopamine (6-OHDA) lesion using an ultrafast opsin (Chronos). Optogenetic STN DBS at 130 pulses per second (pps) reduced pathologic circling and ameliorated deficits in forelimb stepping similarly to electrical DBS, while optogenetic STN DBS with ChR2 did not produce behavioral effects. As with electrical DBS, optogenetic STN DBS exhibited a strong dependence on stimulation rate; high rates produced symptom relief while low rates were ineffective. High-rate optogenetic DBS generated both increases and decreases in firing rates of single neurons in STN, globus pallidus externa (GPe), and substantia nigra pars reticular (SNr), and disrupted β band oscillatory activity in STN and SNr. High-rate optogenetic STN DBS can indeed ameliorate parkinsonian motor symptoms through reduction of abnormal oscillatory activity in the STN-associated neural circuit, and these results highlight that the kinetic properties of opsins have a strong influence on the effects of optogenetic stimulation.SIGNIFICANCE STATEMENT Whether STN local cells contribute to the therapeutic effects of subthalamic nucleus (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) remains unclear. We re-examined the role of STN local cells in mediating the symptom-relieving effects of STN DBS using cell type-specific optogenetic stimulation with a much faster opsin, Chronos. Direct optogenetic stimulation of STN neurons was effective in treating the symptoms of parkinsonism in the 6-hydroxydopamine (6-OHDA) lesion rat. These results highlight that the kinetic properties of opsins can have a strong influence on the effects of optogenetic activation/inhibition and must be considered when employing optogenetic to study high-rate neural stimulation.

Closed-Loop Theta Stimulation in the Orbitofrontal Cortex Prevents Reward-Based Learning

Neuronal oscillations in the frontal cortex have been hypothesized to play a role in the organization of high-level cognition. Within the orbitofrontal cortex (OFC), there is a prominent oscillation in the theta frequency (4-8Hz) during reward-guided behavior, but it is unclear whether this oscillation has causal significance. One methodological challenge is that it is difficult to manipulate theta without affecting other neural signals, such as single-neuron firing rates. A potential solution is to use closed-loop control to record theta in real time and use this signal to control the application of electrical microstimulation to the OFC. Using this method, we show that theta oscillations in the OFC are critically important for reward-guided learning and that they are driven by theta oscillations in the hippocampus (HPC). The ability to disrupt OFC computations via spatially localized and temporally precise stimulation could lead to novel treatment strategies for neuropsychiatric disorders involving OFC dysfunction.

Considerations in using recurrent neural networks to probe neural dynamics.

Recurrent neural networks (RNNs) are increasingly being used to model complex cognitive and motor tasks performed by behaving animals. RNNs are trained to reproduce animal behavior while also capturing key statistics of empirically recorded neural activity. In this manner, the RNN can be viewed as an in silico circuit whose computational elements share similar motifs with the cortical area it is modeling. Furthermore, because the RNN's governing equations and parameters are fully known, they can be analyzed to propose hypotheses for how neural populations compute. In this context, we present important considerations when using RNNs to model motor behavior in a delayed reach task. First, by varying the network's nonlinear activation and rate regularization, we show that RNNs reproducing single-neuron firing rate motifs may not adequately capture important population motifs. Second, we find that even when RNNs reproduce key neurophysiological features on both the single neuron and population levels, they can do so through distinctly different dynamical mechanisms. To distinguish between these mechanisms, we show that an RNN consistent with a previously proposed dynamical mechanism is more robust to input noise. Finally, we show that these dynamics are sufficient for the RNN to generalize to tasks it was not trained on. Together, these results emphasize important considerations when using RNN models to probe neural dynamics.NEW & NOTEWORTHY Artificial neurons in a recurrent neural network (RNN) may resemble empirical single-unit activity but not adequately capture important features on the neural population level. Dynamics of RNNs can be visualized in low-dimensional projections to provide insight into the RNN's dynamical mechanism. RNNs trained in different ways may reproduce neurophysiological motifs but do so with distinctly different mechanisms. RNNs trained to only perform a delayed reach task can generalize to perform tasks where the target is switched or the target location is changed.

Neural Networks for Modeling Neural Spiking in S1 Cortex.

Somatosensation is composed of two distinct modalities: touch, arising from sensors in the skin, and proprioception, resulting primarily from sensors in the muscles, combined with these same cutaneous sensors. In contrast to the wealth of information about touch, we know quite less about the nature of the signals giving rise to proprioception at the cortical level. Likewise, while there is considerable interest in developing encoding models of touch-related neurons for application to brain machine interfaces, much less emphasis has been placed on an analogous proprioceptive interface. Here we investigate the use of Artificial Neural Networks (ANNs) to model the relationship between the firing rates of single neurons in area 2, a largely proprioceptive region of somatosensory cortex (S1) and several types of kinematic variables related to arm movement. To gain a better understanding of how these kinematic variables interact to create the proprioceptive responses recorded in our datasets, we train ANNs under different conditions, each involving a different set of input and output variables. We explore the kinematic variables that provide the best network performance, and find that the addition of information about joint angles and/or muscle lengths significantly improves the prediction of neural firing rates. Our results thus provide new insight regarding the complex representations of the limb motion in S1: that the firing rates of neurons in area 2 may be more closely related to the activity of peripheral sensors than it is to extrinsic hand position. In addition, we conduct numerical experiments to determine the sensitivity of ANN models to various choices of training design and hyper-parameters. Our results provide a baseline and new tools for future research that utilizes machine learning to better describe and understand the activity of neurons in S1.

Closed-Loop Theta Stimulation in Orbitofrontal Cortex Prevents Reward-Based Learning

Neuronal oscillations in frontal cortex have been hypothesized to play a role in the organization of high-level cognition. Within orbitofrontal cortex (OFC), there is a prominent oscillation in the theta frequency (4-8 Hz) during reward-guided behavior, but it is unclear whether this oscillation has causal significance. One methodological challenge is that it is difficult to manipulate theta without affecting other neural signals, such as single neuron firing rates. A potential solution is to use closed-loop control to record theta in real-time and use this signal to control the application of electrical microstimulation to OFC. Using this method, we show that theta oscillations in OFC are critically important for reward-guided learning and that they are driven by theta oscillations in hippocampus (HPC). The ability to disrupt OFC computations via spatially localized and temporally precise stimulation, could lead to novel treatment strategies for neuropsychiatric disorders involving OFC dysfunction.

Differential responsivity of neurons in perirhinal cortex, lateral entorhinal cortex, and dentate gyrus during time-bridging learning.

Many studies have focused on the function of hippocampal region CA1 as a critical site for associative memory, but much less is known about changes in the afferents to CA1. Here we report the activity of multiple single neurons from perirhinal and entorhinal cortex and from dentate gyrus during trace eyeblink conditioning as well as consolidated recall, and in pseudo-conditioned control rabbits. We also report an analysis of theta activity filtered from the local field potential (LFP). Our results show early associative changes in single-neuron firing rate as well as theta oscillations in lateral entorhinal cortex (EC) and dentate gyrus (DG), and increases in the number of responsive neurons in perirhinal cortex. In both EC and DG, a subset of neurons from conditioned animals exhibited an elevated baseline firing rate and large responses to the conditioned stimulus and trace period. A similar population of cells has been seen in DG and in medial, but not lateral, EC during spatial tasks, suggesting that lateral EC contains cells responsive to a temporal associative task. In contrast to recent studies in our laboratory that found significant CA1 contributions to long-term memory, the activity profiles of neurons within EC and DG were similar for conditioned and pseudoconditioned rabbits during post-consolidation sessions. Collectively these results demonstrate that individual subregions of medial temporal lobe differentially support new and remotely acquired memories. Neuron firing profiles were similar on training trials when conditioned responses were and were not exhibited, demonstrating that these temporal lobe regions represent the CS-US association and do not control the behavioral response. The analysis of theta activity revealed that theta power was modulated by the conditioning stimuli in both the conditioned and pseudoconditioned groups and that although both groups exhibited a resetting of phase to the corneal airpuff, only the conditioned group exhibited a resetting of phase to the whisker conditioned stimulus.

Single Neuron Firing Rate Statistics in Motor Cortex During Execution and Observation of Movement.

Mirror neurons, which fire during both the execution and observation of movement, are believed to play an important role in motor processing and learning. However, much work still remains to understand the similarities and differences in how these neurons compute in the motor cortex during movement execution and observation. Here, we performed experiments where a monkey both executes and observes a center-out-and-back task within the same experimental session. By recording from putatively the same neural population, we were able to analyze and compare single neuron statistics between movement execution and observation. We found that a majority of neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) have statistically different firing rate statistics between movement execution and observation. As a result of this difference, we then wondered if neurons during movement observation exhibited a similar characteristic to those during movement execution: changing of preferred directions as a function of movement speed. Interestingly, we found that while observed movement speed is encoded in the neural population, it only alters a small proportion of the neuron's firing rate statistics. These results suggest that neural populations in Ml and PMd process information related to movement differently between execution and observation.

Primary sensorimotor cortex exhibits complex dependencies of spike-field coherence on neuronal firing rates, field power, and behavior

Spike-field coherence (SFC) is widely used to assess cortico-cortical interactions during sensorimotor behavioral tasks by measuring the consistency of the relative phases between the spike train of a neuron and the concurrent local field potentials (LFPs). Interpretations of SFC as a measure of functional connectivity are complicated by theoretical work suggesting that estimates of SFC depend on overall neuronal activity. We evaluated the dependence of SFC on neuronal firing rates, LFP power, and behavior in the primary motor (MIo) and primary somatosensory (SIo) areas of the orofacial sensorimotor cortex of monkeys ( Macaca mulatta) during performance of a tongue-protrusion task. Although we occasionally observed monotonically increasing linear relationships between coherence and firing rate, we most often found highly complex, nonmonotonic relationships in both SIo and MIo and sometimes even found that coherence decreased with increasing firing rate. The lack of linear relationships was also true for both LFP power and tongue-protrusive force. Moreover, the ratio between maximal firing rate and the firing rate at peak coherence deviated significantly from unity, indicating that MIo and SIo neurons achieved maximal SFC at a submaximal level of spiking. Overall, these results point to complex relationships of SFC to firing rates, LFP power, and behavior during sensorimotor cortico-cortical interactions: coherence is a measure of functional connectivity whose magnitude is not a mere monotonic reflection of changes in firing rate, LFP power, or the relevantly controlled behavioral parameter. NEW & NOTEWORTHY The concern that estimates of spike-field coherence depend on the firing rates of single neurons has influenced analytical methods employed by experimental studies investigating the functional interactions between cortical areas. Our study shows that the overwhelming majority of the estimated spike-field coherence exhibited complex relations with firing rates of neurons in the orofacial sensorimotor cortex. The lack of monotonic relations was also evident after testing the influence of local field potential power and force on spike-field coherence.

Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex during steady-state movement preparation.

Previous studies on the origin and properties of spatial patterns in motor cortex β-local field potential (β-LFP) oscillations have focused on planar traveling waves. However, it is unclear 1) whether β-LFP waves are limited to plane waves, or even 2) whether they are propagating waves of excito-excitatory activity, i.e., primarily traveling waves in excitable media; they could reflect, instead, reorganization in the relative phases of transient oscillations at different spatial sites. We addressed these two problems in β-LFPs recorded via microelectrode arrays implanted in three adjacent motor cortex areas of nonhuman primates during steady-state movement preparation. Our findings are fourfold: 1) β-LFP wave patterns emerged as transient events, despite stable firing rates of single neurons concurrently recorded during the same periods. 2) β-LFP waves showed a richer variety of spatial dynamics, including rotating and complex waves. 3) β-LFP wave patterns showed no characteristic wavelength, presenting instead a range of scales with global zero-lag phase synchrony as a limiting case, features surprising for purely excito-excitatory waves but consistent with waves in coupled oscillator systems. 4) Furthermore, excito-excitatory traveling waves induced by optogenetic stimulation in motor cortex showed, in contrast, a characteristic wavelength and reduced phase synchrony. Overall, β-LFP wave statistics differed from those of induced traveling waves in excitable media recorded under the same microelectrode array setup. Our findings suggest phase reorganization in neural coupled oscillators contribute significantly to the origin of transient β-LFP spatial dynamics during preparatory steady states and outline important constraints for spatially extended models of β-LFP dynamics in motor cortex. NEW & NOTEWORTHY We show that a rich variety of transient β-local field potential (β-LFP) wave patterns emerge in motor cortex during preparatory steady states, despite stable neuronal firing rates. Furthermore, unlike optogenetically induced traveling waves, β-LFP waves showed no characteristic wavelength, presenting instead a range of scales with global phase synchrony as a limiting case. Overall, our statistical analyses suggest that transient phase reorganization in neural coupled oscillators, beyond purely excito-excitatory traveling waves, contribute significantly to the origin of motor cortex β-LFP wave patterns.

Neurons in Primate Entorhinal Cortex Represent Gaze Position in Multiple Spatial Reference Frames.

Primates rely predominantly on vision to gather information from the environment and neurons representing visual space and gaze position are found in many brain areas. Within the medial temporal lobe, a brain region critical for memory, neurons in the entorhinal cortex of macaque monkeys exhibit spatial selectivity for gaze position. Specifically, the firing rate of single neurons reflects fixation location within a visual image (Killian et al., 2012). In the rodents, entorhinal cells such as grid cells, border cells, and head direction cells show spatial representations aligned to visual environmental features instead of the body (Hafting et al., 2005; Sargolini et al., 2006; Solstad et al., 2008; Diehl et al., 2017). However, it is not known whether similar allocentric representations exist in primate entorhinal cortex. Here, we recorded neural activity in the entorhinal cortex in two male rhesus monkeys during a naturalistic, free-viewing task. Our data reveal that a majority of entorhinal neurons represent gaze position and that simultaneously recorded neurons represent gaze position relative to distinct spatial reference frames, with some neurons aligned to the visual image and others aligned to the monkey's head position. Our results also show that entorhinal neural activity can be used to predict gaze position with a high degree of accuracy. These findings demonstrate that visuospatial representation is a fundamental property of entorhinal neurons in primates and suggest that entorhinal cortex may support relational memory and motor planning by coding attentional locus in distinct, behaviorally relevant frames of reference.SIGNIFICANCE STATEMENT The entorhinal cortex, a brain area important for memory, shows striking spatial activity in rodents through grid cells, border cells, head direction cells, and nongrid spatial cells. The majority of entorhinal neurons signal the location of a rodent relative to visual environmental cues, representing the location of the animal relative to space in the world instead of the body. Recently, we found that entorhinal neurons can signal location of gaze while a monkey explores images visually. Here, we report that spatial entorhinal neurons are widespread in the monkey and these neurons are capable of showing a world-based spatial reference frame locked to the bounds of explored images. These results help connect the extensive findings in rodents to the primate.

A Quadrantic Bias in Prefrontal Representation of Visual-Mnemonic Space.

Single neurons in primate dorsolateral prefrontal cortex (dLPFC) are known to encode working memory (WM) representations of visual space. Psychophysical studies have shown that the horizontal and vertical meridians of the visual field can bias spatial information maintained in WM. However, most studies and models have tacitly assumed that dLPFC neurons represent mnemonic space homogenously. The anatomical organization of these representations has also eluded clear parametric description. We investigated these issues by recording from neuronal ensembles in macaque dLPFC with microelectrode arrays while subjects performed an oculomotor delayed-response task. We found that spatial WM representations in macaque dLPFC are biased by the vertical and horizontal meridians of the visual field, dividing mnemonic space into quadrants. This bias is reflected in single neuron firing rates, neuronal ensemble representations, the spike count correlation structure, and eye movement patterns. We also found that dLPFC representations of mnemonic space cluster anatomically in a nonretinotopic manner that partially reflects the organization of visual space. These results provide an explanation for known WM biases, and reveal novel principles of WM representation in prefrontal neuronal ensembles and across the cortical surface, as well as the need to reconceptualize models of WM to accommodate the observed representational biases.

Transcranial Alternating Current Stimulation Attenuates Neuronal Adaptation.

We previously showed that brief application of 2 mA (peak-to-peak) transcranial currents alternating at 10 Hz significantly reduces motion adaptation in humans. This is but one of many behavioral studies showing that weak currents applied to the scalp modulate neural processing. Transcranial stimulation has been shown to improve perception, learning, and a range of clinical symptoms. Few studies, however, have measured the neural consequences of transcranial current stimulation. We capitalized on the strong link between motion perception and neural activity in the middle temporal (MT) area of the macaque monkey to study the neural mechanisms that underlie the behavioral consequences of transcranial alternating current stimulation. First, we observed that 2 mA currents generated substantial intracranial fields, which were much stronger in the stimulated hemisphere (0.12 V/m) than on the opposite side of the brain (0.03 V/m). Second, we found that brief application of transcranial alternating current stimulation at 10 Hz reduced spike-frequency adaptation of MT neurons and led to a broadband increase in the power spectrum of local field potentials. Together, these findings provide a direct demonstration that weak electric fields applied to the scalp significantly affect neural processing in the primate brain and that this includes a hitherto unknown mechanism that attenuates sensory adaptation.SIGNIFICANCE STATEMENT Transcranial stimulation has been claimed to improve perception, learning, and a range of clinical symptoms. Little is known, however, how transcranial current stimulation generates such effects, and the search for better stimulation protocols proceeds largely by trial and error. We investigated, for the first time, the neural consequences of stimulation in the monkey brain. We found that even brief application of alternating current stimulation reduced the effects of adaptation on single-neuron firing rates and local field potentials; this mechanistic insight explains previous behavioral findings and suggests a novel way to modulate neural information processing using transcranial currents. In addition, by developing an animal model to help understand transcranial stimulation, this study will aid the rational design of stimulation protocols for the treatment of mental illnesses, and the improvement of perception and learning.