Comparison of recordings from microelectrode arrays and single electrodes in the visual cortex

Neuronal growth patterns and synapse formation are mediated by distinct activity-dependent mechanisms

All brain functions in animals rely upon neuronal connectivity that is established during early development. Although the activity-dependent mechanisms are deemed important for brain development and adult synaptic plasticity, the precise cellular and molecular mechanisms remain however, largely unknown. This lack of fundamental knowledge regarding developmental neuronal assembly owes its existence to the complexity of the mammalian brain as cell-cell interactions between individual neurons cannot be investigated directly. Here, we used individually identified synaptic partners from Lymnaea stagnalis to interrogate the role of neuronal activity patterns over an extended time period during various growth time points and synaptogenesis. Using intracellular recordings, microelectrode arrays, and time-lapse imaging, we identified unique patterns of activity throughout neurite outgrowth and synapse formation. Perturbation of voltage-gated Ca2+ channels compromised neuronal growth patterns which also invoked a protein kinase A mediated pathway. Our findings underscore the importance of unique activity patterns in regulating neuronal growth, neurite branching, and synapse formation, and identify the underlying cellular and molecular mechanisms.

Dual parallel stream-specific and generalized effects of corticogeniculate feedback on LGN neurons in primate and carnivore

Sensory circuits are organized in parallel, e.g. parallel streams relay feedforward visual information from retina to cortex. Corticogeniculate (CG) feedback is also organized in parallel; however, stream-specific influences of CG feedback remained unresolved. We utilized optogenetics to manipulate CG feedback in monkeys while recording geniculate responses to a comprehensive set of visual stimuli designed to probe stream-specific responses. Here we show that CG feedback improved the spatial resolution of magnocellular, but not parvocellular neurons. Optogenetically enhancing CG feedback increased extraclassical surround suppression, shrunk classical receptive fields, and increased preferred spatial frequencies among magnocellular neurons. Optogenetically suppressing CG feedback reduced surround suppression. Enhancing CG feedback in female ferrets revealed similar stream-specific effects in geniculate Y, but not X neurons. Furthermore, optogenetically enhancing CG feedback improved temporal response precision across neuronal types. These results support dual functional roles for CG feedback in enhancing spatial resolution in a stream-specific manner and improving temporal precision broadly.

Cortical direction selectivity increases from the input to the output layers of visual cortex

Sensitivity to motion direction is a feature of visual neurons that is essential for motion perception. Recent studies have suggested that direction selectivity is re-established at multiple stages throughout the visual hierarchy, which contradicts the traditional assumption that direction selectivity in later stages largely derives from that in earlier stages. By recording laminar responses in areas 17 and 18 of anesthetized cats of both sexes, we aimed to understand how direction selectivity is processed and relayed across 2 successive stages: the input layers and the output layers within the early visual cortices. We found a strong relationship between the strength of direction selectivity in the output layers and the input layers, as well as the preservation of preferred directions across the input and output layers. Moreover, direction selectivity was enhanced in the output layers compared to the input layers, with the response strength maintained in the preferred direction but reduced in other directions and under blank stimuli. We identified a direction-tuned gain mechanism for interlaminar signal transmission, which likely originated from both feedforward connections across the input and output layers and recurrent connections within the output layers. This direction-tuned gain, coupled with nonlinearity, contributed to the enhanced direction selectivity in the output layers. Our findings suggest that direction selectivity in later cortical stages partially inherits characteristics from earlier cortical stages and is further refined by intracortical connections.

Enhancing biocompatibility of the brain-machine interface: A review

In vivo implantation of microelectrodes opens the door to studying neural circuits and restoring damaged neural pathways through direct electrical stimulation and recording. Although some neuroprostheses have achieved clinical success, electrode material properties, inflammatory response, and glial scar formation at the electrode-tissue interfaces affect performance and sustainability. Those challenges can be addressed by improving some of the materials' mechanical, physical, chemical, and electrical properties. This paper reviews materials and designs of current microelectrodes and discusses perspectives to advance neuroprosthetics performance.

Recent Visual Experience Reshapes V4 Neuronal Activity and Improves Perceptual Performance

Recent visual experience heavily influences our visual perception, but how neuronal activity is reshaped to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while two male rhesus macaque monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in the mid-level visual cortex.

Short-latency preference for faces in primate superior colliculus depends on visual cortex

Face processing is fundamental to primates and has been extensively studied in higher-order visual cortex. Here, we report that visual neurons in the midbrain superior colliculus (SC) of macaque monkeys display a preference for images of faces. This preference emerges within 40 ms of stimulus onset-well before "face patches" in visual cortex-and, at the population level, can be used to distinguish faces from other visual objects with accuracies of ∼80%. This short-latency face preference in SC depends on signals routed through early visual cortex because inactivating the lateral geniculate nucleus, the key relay from retina to cortex, virtually eliminates visual responses in SC, including face-related activity. These results reveal an unexpected circuit in the primate visual system for rapidly detecting faces in the periphery, complementing the higher-order areas needed for recognizing individual faces.

Recent visual experience reshapes V4 neuronal activity and improves perceptual performance

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

Neuronal representation of visual working memory content in the primate primary visual cortex

The human ability to perceive vivid memories as if they "float" before our eyes, even in the absence of actual visual stimuli, captivates the imagination. To determine the neural substrates underlying visual memories, we investigated the neuronal representation of working memory content in the primary visual cortex of monkeys. Our study revealed that neurons exhibit unique responses to different memory contents, using firing patterns distinct from those observed during the perception of external visual stimuli. Moreover, this neuronal representation evolves with alterations in the recalled content and extends beyond the retinotopic areas typically reserved for processing external visual input. These discoveries shed light on the visual encoding of memories and indicate avenues for understanding the remarkable power of the mind's eye.

Investigation of a chondroitin sulfate-based bioactive coating for neural interface applications

Invasive neural implants allow for high-resolution bidirectional communication with the nervous tissue and have demonstrated the ability to record neural activity, stimulate neurons, and sense neurochemical species with high spatial selectivity and resolution. However, upon implantation, they are exposed to a foreign body response which can disrupt the seamless integration of the device with the native tissue and lead to deterioration in device functionality for chronic implantation. Modifying the device surface by incorporating bioactive coatings has been a promising approach to camouflage the device and improve integration while maintaining device performance. In this work, we explored the novel application of a chondroitin sulfate (CS) based hydrophilic coating, with anti-fouling and neurite-growth promoting properties for neural recording electrodes. CS-coated samples exhibited significantly reduced protein-fouling in vitro which was maintained for up to 4-weeks. Cell culture studies revealed a significant increase in neurite attachment and outgrowth and a significant decrease in microglia attachment and activation for the CS group as compared to the control. After 1-week of in vivo implantation in the mouse cortex, the coated probes demonstrated significantly lower biofouling as compared to uncoated controls. Like the in vitro results, increased neuronal population (neuronal nuclei and neurofilament) and decreased microglial activation were observed. To assess the coating's effect on the recording performance of silicon microelectrodes, we implanted coated and uncoated electrodes in the mouse striatum for 1 week and performed impedance and recording measurements. We observed significantly lower impedance in the coated group, likely due to the increased wettability of the coated surface. The peak-to-peak amplitude and the noise floor levels were both lower in the CS group compared to the controls, which led to a comparable signal-to-noise ratio between the two groups. The overall single unit yield (% channels recording a single unit) was 74% for the CS and 67% for the control group on day 1. Taken together, this study demonstrates the effectiveness of the polysaccharide-based coating in reducing biofouling and improving biocompatibility for neural electrode devices.

Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

Dense local, recurrent connections are a major feature of cortical circuits, yet how they affect neurons' responses has been unclear, with some studies reporting weak recurrent effects, some reporting amplification, and others indicating local suppression. Here, we show that optogenetic input to mouse V1 excitatory neurons generates salt-and-pepper patterns of both excitation and suppression. Responses in individual neurons are not strongly predicted by that neuron's direct input. A balanced-state network model reconciles a set of diverse observations: the observed dynamics, suppressed responses, decoupling of input and output, and long tail of excited responses. The model shows recurrent excitatory-excitatory connections are strong and also variable across neurons. Together, these results demonstrate that excitatory recurrent connections can have major effects on cortical computations by shaping and changing neurons' responses to input.

A 14-Bit, 12 V-to-100 V Voltage Compliance Electrical Stimulator with Redundant Digital Calibration

Electrical stimulation is an important technique for modulating the functions of the nervous system through electrical stimulus. To implement a more competitive prototype that can tackle the domain-specific difficulties of existing electrical stimulators, three key techniques are proposed in this work. Firstly, a load-adaptive power saving technique called over-voltage detection is implemented to automatically adjust the supply voltage. Secondly, redundant digital calibration (RDC) is proposed to improve current accuracy and ensure safety during long-term electrical stimulation without costing too much circuit area and power. Thirdly, a flexible waveform generator is designed to provide arbitrary stimulus waveforms for particular applications. Measurement results show the stimulator can adjust the supply voltage from 12 V to 100 V automatically, and the measured effective resolution of the stimulation current reaches 14 bits in a full range of 6.5 mA. Without applying charge balancing techniques, the average mismatch between the cathodic and anodic current pulses in biphasic stimulus is 0.0427%. The proposed electrical stimulator can generate arbitrary stimulus waveforms, including sine, triangle, rectangle, etc., and it is supposed to be competitive for implantable and wearable devices.

Characterization of extracellular spike waveforms recorded in wallaby primary visual cortex

Extracellular recordings were made from 642 units in the primary visual cortex (V1) of a highly visual marsupial, the Tammar wallaby. The receptive field (RF) characteristics of the cells were objectively estimated using the non-linear input model (NIM), and these were correlated with spike shapes. We found that wallaby cortical units had 68% regular spiking (RS), 12% fast spiking (FS), 4% triphasic spiking (TS), 5% compound spiking (CS) and 11% positive spiking (PS). RS waveforms are most often associated with recordings from pyramidal or spiny stellate cell bodies, suggesting that recordings from these cell types dominate in the wallaby cortex. In wallaby, 70-80% of FS and RS cells had orientation selective RFs and had evenly distributed linear and nonlinear RFs. We found that 47% of wallaby PS units were non-orientation selective and they were dominated by linear RFs. Previous studies suggest that the PS units represent recordings from the axon terminals of non-orientation selective cells originating in the lateral geniculate nucleus (LGN). If this is also true in wallaby, as strongly suggested by their low response latencies and bursty spiking properties, the results suggest that significantly more neurons in wallaby LGN are already orientation selective. In wallaby, less than 10% of recorded spikes had triphasic (TS) or sluggish compound spiking (CS) waveforms. These units had a mixture of orientation selective and non-oriented properties, and their cellular origins remain difficult to classify.

Dynamic Recruitment of the Feedforward and Recurrent Mechanism for Black-White Asymmetry in the Primary Visual Cortex

Black and white information is asymmetrically distributed in natural scenes, evokes asymmetric neuronal responses, and causes asymmetric perceptions. Recognizing the universality and essentiality of black-white asymmetry in visual information processing, the neural substrates for black-white asymmetry remain unclear. To disentangle the role of the feedforward and recurrent mechanisms in the generation of cortical black-white asymmetry, we recorded the V1 laminar responses and LGN responses of anesthetized cats of both sexes. In a cortical column, we found that black-white asymmetry starts at the input layer and becomes more pronounced in the output layer. We also found distinct dynamics of black-white asymmetry between the output layer and the input layer. Specifically, black responses dominate in all layers after stimulus onset. After stimulus offset, black and white responses are balanced in the input layer, but black responses still dominate in the output layer. Compared with that in the input layer, the rebound response in the output layer is significantly suppressed. The relative suppression strength evoked by white stimuli is notably stronger and depends on the location within the ON-OFF cortical map. A model with delayed and polarity-selective cortical suppression explains black-white asymmetry in the output layer, within which prominent recurrent connections are identified by Granger causality analysis. In addition to black-white asymmetry in response strength, the interlaminar differences in spatial receptive field varied dynamically. Our findings suggest that the feedforward and recurrent mechanisms are dynamically recruited for the generation of black-white asymmetry in V1.SIGNIFICANCE STATEMENT Black-white asymmetry is universal and essential in visual information processing, yet the neural substrates for cortical black-white asymmetry remain unknown. Leveraging V1 laminar recordings, we provided the first laminar pattern of black-white asymmetry in cat V1 and found distinct dynamics of black-white asymmetry between the output layer and the input layer. Comparing black-white asymmetry across three visual hierarchies, the LGN, V1 input layer, and V1 output layer, we demonstrated that the feedforward and recurrent mechanisms are dynamically recruited for the generation of cortical black-white asymmetry. Our findings not only enhance our understanding of laminar processing within a cortical column but also elucidate how feedforward connections and recurrent connections interact to shape neuronal response properties.

Rogue Waves: A Unifying Model of Human Seizure Propagation

Multiple Sources of Fast Traveling Waves During Human Seizures: Resolving a Controversy Schlafly ED, Marshall FA, Merricks EM, Eden UT, Cash SS, Schevon CA, Kramer MA. J Neurosci . 2022;42(36):6966-6982. doi:10.1523/JNEUROSCI.0338-22.2022 During human seizures organized waves of voltage activity rapidly sweep across the cortex. Two contradictory theories describe the source of these fast traveling waves: either a slowly advancing narrow region of multiunit activity (an ictal wavefront) or a fixed cortical location. Limited observations and different analyses prevent resolution of these incompatible theories. Here we address this disagreement by combining the methods and microelectrode array recordings (N = 11 patients, 2 females, N = 31 seizures) from previous human studies to analyze the traveling wave source. We find—inconsistent with both existing theories—a transient relationship between the ictal wavefront and traveling waves, and multiple stable directions of traveling waves in many seizures. Using a computational model that combines elements of both existing theories, we show that interactions between an ictal wavefront and fixed source reproduce the traveling wave dynamics observed in vivo. We conclude that combining both existing theories can generate the diversity of ictal traveling waves. Significance Statement: The source of voltage discharges that propagate across cortex during human seizures remains unknown. Two candidate theories exist, each proposing a different discharge source. Support for each theory consists of observations from a small number of human subject recordings, analyzed with separately developed methods. How the different, limited data and different analysis methods impact the evidence for each theory is unclear. To resolve these differences, we combine the unique, human microelectrode array recordings collected separately for each theory and analyze these combined data with a unified approach. We show that neither existing theory adequately describes the data. We then propose a new theory that unifies existing proposals and successfully reproduces the voltage discharge dynamics observed in vivo.

Identifying properties of pattern completion neurons in a computational model of the visual cortex

Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles' role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair's power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.

Correlated variability in primate superior colliculus depends on functional class

Correlated variability in neuronal activity (spike count correlations, r_SC) can constrain how information is read out from populations of neurons. Traditionally, r_SC is reported as a single value summarizing a brain area. However, single values, like summary statistics, stand to obscure underlying features of the constituent elements. We predict that in brain areas containing distinct neuronal subpopulations, different subpopulations will exhibit distinct levels of r_SC that are not captured by the population r_SC. We tested this idea in macaque superior colliculus (SC), a structure containing several functional classes (i.e., subpopulations) of neurons. We found that during saccade tasks, different functional classes exhibited differing degrees of r_SC. "Delay class" neurons displayed the highest r_SC, especially during saccades that relied on working memory. Such dependence of r_SC on functional class and cognitive demand underscores the importance of taking functional subpopulations into account when attempting to model or infer population coding principles.

Feature representation under crowding in macaque V1 and V4 neuronal populations

Visual perception depends strongly on spatial context. A profound example is visual crowding, whereby the presence of nearby stimuli impairs the discriminability of object features. Despite extensive work on perceptual crowding and the spatial integrative properties of visual cortical neurons, the link between these two aspects of visual processing remains unclear. To understand better the neural basis of crowding, we recorded activity simultaneously from neuronal populations in V1 and V4 of fixating macaque monkeys. We assessed the information available from the measured responses about the orientation of a visual target both for targets presented in isolation and amid distractors. Both single neuron and population responses had less information about target orientation when distractors were present. Information loss was moderate in V1 and more substantial in V4. Information loss could be traced to systematic divisive and additive changes in neuronal tuning. Additive and multiplicative changes in tuning were more severe in V4; in addition, tuning exhibited other, non-affine transformations that were greater in V4, further restricting the ability of a fixed sensory readout strategy to extract accurate feature information across displays. Our results provide a direct test of crowding effects at different stages of the visual hierarchy. They reveal how crowded visual environments alter the spiking activity of cortical populations by which sensory stimuli are encoded and connect these changes to established mechanisms of neuronal spatial integration.

Electric Fields Induced in the Brain by Transcranial Electric Stimulation: A Review of In Vivo Recordings

Transcranial electrical stimulation (tES) techniques, such as direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), cause neurophysiological and behavioral modifications as responses to the electric field are induced in the brain. Estimations of such electric fields are based mainly on computational studies, and in vivo measurements have been used to expand the current knowledge. Here, we review the current tDCS- and tACS-induced electric fields estimations as they are recorded in humans and non-human primates using intracerebral electrodes. Direct currents and alternating currents were applied with heterogeneous protocols, and the recording procedures were characterized by a tentative methodology. However, for the clinical stimulation protocols, an injected current seems to reach the brain, even at deep structures. The stimulation parameters (e.g., intensity, frequency and phase), the electrodes’ positions and personal anatomy determine whether the intensities might be high enough to affect both neuronal and non-neuronal cell activity, also deep brain structures.

The eyes reflect an internal cognitive state hidden in the population activity of cortical neurons

Decades of research have shown that global brain states such as arousal can be indexed by measuring the properties of the eyes. The spiking responses of neurons throughout the brain have been associated with the pupil, small fixational saccades, and vigor in eye movements, but it has been difficult to isolate how internal states affect the eyes, and vice versa. While recording from populations of neurons in the visual and prefrontal cortex (PFC), we recently identified a latent dimension of neural activity called "slow drift," which appears to reflect a shift in a global brain state. Here, we asked if slow drift is correlated with the action of the eyes in distinct behavioral tasks. We recorded from visual cortex (V4) while monkeys performed a change detection task, and PFC, while they performed a memory-guided saccade task. In both tasks, slow drift was associated with the size of the pupil and the microsaccade rate, two external indicators of the internal state of the animal. These results show that metrics related to the action of the eyes are associated with a dominant and task-independent mode of neural activity that can be accessed in the population activity of neurons across the cortex.

Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex

The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. It resolves the process of competing for sensory information about objects perceived as targets and distractors. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.

EEG Signals Index a Global Signature of Arousal Embedded in Neuronal Population Recordings

Electroencephalography (EEG) has long been used to index brain states, from early studies describing activity in the presence and absence of visual stimulation to modern work employing complex perceptual tasks. These studies have shed light on brain-wide signals but often lack explanatory power at the single neuron level. Similarly, single neuron recordings can suffer from an inability to measure brain-wide signals accessible using EEG. Here, we combined these techniques while monkeys performed a change detection task and discovered a novel link between spontaneous EEG activity and a neural signal embedded in the spiking responses of neuronal populations. This "slow drift" was associated with fluctuations in the subjects' arousal levels over time: decreases in prestimulus α power were accompanied by increases in pupil size and decreases in microsaccade rate. These results show that brain-wide EEG signals can be used to index modes of activity present in single neuron recordings, that in turn reflect global changes in brain state that influence perception and behavior.

Fabrication of High-Density Out-of-Plane Microneedle Arrays with Various Heights and Diverse Cross-Sectional Shapes

Out-of-plane microneedle structures are widely used in various applications such as transcutaneous drug delivery and neural signal recording for brain machine interface. This work presents a novel but simple method to fabricate high-density silicon (Si) microneedle arrays with various heights and diverse cross-sectional shapes depending on photomask pattern designs. The proposed fabrication method is composed of a single photolithography and two subsequent deep reactive ion etching (DRIE) steps. First, a photoresist layer was patterned on a Si substrate to define areas to be etched, which will eventually determine the final location and shape of each individual microneedle. Then, the 1st DRIE step created deep trenches with a highly anisotropic etching of the Si substrate. Subsequently, the photoresist was removed for more isotropic etching; the 2nd DRIE isolated and sharpened microneedles from the predefined trench structures. Depending on diverse photomask designs, the 2nd DRIE formed arrays of microneedles that have various height distributions, as well as diverse cross-sectional shapes across the substrate. With these simple steps, high-aspect ratio microneedles were created in the high density of up to 625 microneedles mm^-2 on a Si wafer. Insertion tests showed a small force as low as ~ 172 µN/microneedle is required for microneedle arrays to penetrate the dura mater of a mouse brain. To demonstrate a feasibility of drug delivery application, we also implemented silk microneedle arrays using molding processes. The fabrication method of the present study is expected to be broadly applicable to create microneedle structures for drug delivery, neuroprosthetic devices, and so on.

Modulation of Spike Count Correlations Between Macaque Primary Visual Cortex Neurons by Difficulty of Attentional Task

Studies have shown that spatial attention remarkably affects the trial-to-trial response variability shared between neurons. Difficulty in the attentional task adjusts how much concentration we maintain on what is currently important and what is filtered as irrelevant sensory information. However, how task difficulty mediates the interactions between neurons with separated receptive fields (RFs) that are attended to or attended away is still not clear. We examined spike count correlations between single-unit activities recorded simultaneously in the primary visual cortex (V1) while monkeys performed a spatial attention task with two levels of difficulty. Moreover, the RFs of the two neurons recorded were non-overlapping to allow us to study fluctuations in the correlated responses between competing visual inputs when the focus of attention was allocated to the RF of one neuron. While increasing difficulty in the spatial attention task, spike count correlations were either decreased to become negative between neuronal pairs, implying competition among them, with one neuron (or none) exhibiting attentional enhancement of firing rate, or increased to become positive, suggesting inter-neuronal cooperation, with one of the pair showing attentional suppression of spiking responses. Besides, the modulation of spike count correlations by task difficulty was independent of the attended locations. These findings provide evidence that task difficulty affects the functional interactions between different neuronal pools in V1 when selective attention resolves the spatial competition.

Bridging neuronal correlations and dimensionality reduction

Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.

Preparatory activity links the frontal eye field response with small amplitude motor unit recruitment of neck muscles during gaze planning

A hallmark of intelligent behavior is that we can separate intention from action. To understand the mechanism that gates the flow of information between motor planning and execution, we compared the activity of frontal eye field neurons with motor unit activity from neck muscles in the presence of an intervening delay period in which spatial information regarding the target was available to plan a response. Although spatially specific delay period activity was present in the activity of frontal eye field neurons, it was absent in motor unit activity. Nonetheless, motor unit activity was correlated with the time it took to initiate saccades. Interestingly, we observed a heterogeneity of responses among motor units, such that only units with smaller amplitudes showed a clear modulation during the delay period. These small amplitude motor units also had higher spontaneous activity compared with the units which showed modulation only during the movement epoch. Taken together, our results suggest the activity of smaller motor units convey temporal information and explains how the delay period primes muscle activity leading to faster reaction times.NEW & NOTEWORTHY This study shows that the temporal aspects of a motor plan in the oculomotor circuitry can be accessed by peripheral neck muscles hundreds of milliseconds before the instruction to initiate a saccadic eye movement. The coupling between central and peripheral processes during the delay time is mediated by the recruitment pattern of motor units with smaller amplitude. These findings suggest that information processed in cortical areas could be read from periphery before execution.

A convolutional neural network for estimating synaptic connectivity from spike trains

The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

Hydrogel facilitated bioelectronic integration

The recent advances in bio-integratable electronics are creating new opportunities for investigating and directing biologically significant processes, yet their performance to date is still limited by the inherent physiochemical and signaling mismatches at the heterogeneous interfaces. Hydrogels represent a unique category of materials to bridge the gap between biological and electronic systems because of their structural/functional similarity to biological tissues and design versatility to accommodate cross-system communication. In this review, we discuss the latest progress in the engineering of hydrogel interfaces for bioelectronics development that promotes (1) structural compatibility, where the mechanical and chemical properties of hydrogels can be modulated to achieve coherent, chronically stable biotic-abiotic junctions; and (2) interfacial signal transduction, where the charge and mass transport within the hydrogel mediators can be rationally programmed to condition/amplify the bioderived signals and enhance the electrical/electrochemical coupling. We will further discuss the application of functional hydrogels in complex physiological environments for bioelectronic integration across different scales/biological levels. These ongoing research efforts have the potential to blur the distinction between living systems and artificial electronics, and ultimately decode and regulate biological functioning for both fundamental inquiries and biomedical applications.

Analysis of extracellular spike waveforms and associated receptive fields of neurons in cat primary visual cortex

KEY POINTS

Extracellular spikes recorded in the visual cortex (Area 17/18, V1) are commonly classified into either regular-spiking (RS) or fast-spiking (FS). Using multi-electrode arrays positioned in cat V1 and a broadband stimulus, we show that there is also a distinct class with positive-spiking (PS) waveforms. PS units were associated mainly with non-oriented receptive fields while RS and FS units had orientation-selective receptive fields. We suggest that PS units are recordings of axons originating from the thalamus. This conclusion was reinforced by our finding that we could record PS units after cortical silencing, but not record RS and FS units. The importance of our findings is that we were able to correlate spike shapes with receptive field characteristics with high precision using multi-electrode extracellular recording techniques. This allows considerable increases in the amount of information that can be extracted from future cortical experiments.

ABSTRACT

Extracellular spike waveforms from recordings in the visual cortex have been classified into either regular-spiking (RS) or fast-spiking (FS) units. While both these types of spike waveforms are negative-dominant, we show that there are also distinct classes of spike waveforms in visual Area 17/18 (V1) of anaesthetised cats with positive-dominant waveforms, which are not regularly reported. The spatial receptive fields (RFs) of these different spike waveform types were estimated, which objectively revealed the existence of oriented and non-oriented RFs. We found that units with positive-dominant spikes, which have been associated with recordings from axons in the literature, had mostly non-oriented RFs (84%), which are similar to the centre-surround RFs observed in the dorsal lateral geniculate nucleus (dLGN). Thus, we hypothesise that these positive-dominant waveforms may be recordings from dLGN afferents. We recorded from V1 before and after the application of muscimol (a cortical silencer) and found that the positive-dominant spikes (PS) remained while the RS and FS cells did not. We also noted that the PS units had spiking characteristics normally associated with dLGN units (i.e. higher response spike rates, lower response latencies and higher proportion of burst spikes). Our findings show quantitatively that it is possible to correlate the RF properties of cortical neurons with particular spike waveforms. This has implications for how extracellular recordings should be interpreted and complex experiments can now be contemplated that would have been very challenging previously, such as assessing the feedforward connectivity between brain areas in the same location of cortical tissue.

Effect of Cross-Orientation Normalization on Different Neural Measures in Macaque Primary Visual Cortex

Divisive normalization is a canonical mechanism that can explain a variety of sensory phenomena. While normalization models have been used to explain spiking activity in response to different stimulus/behavioral conditions in multiple brain areas, it is unclear whether similar models can also explain modulation in population-level neural measures such as power at various frequencies in local field potentials (LFPs) or steady-state visually evoked potential (SSVEP) that is produced by flickering stimuli and popular in electroencephalogram studies. To address this, we manipulated normalization strength by presenting static as well as flickering orthogonal superimposed gratings (plaids) at varying contrasts to 2 female monkeys while recording multiunit activity (MUA) and LFP from the primary visual cortex and quantified the modulation in MUA, gamma (32-80 Hz), high-gamma (104-248 Hz) power, as well as SSVEP. Even under similar stimulus conditions, normalization strength was different for the 4 measures and increased as: spikes, high-gamma, SSVEP, and gamma. However, these results could be explained using a normalization model that was modified for population responses, by varying the tuned normalization parameter and semisaturation constant. Our results show that different neural measures can reflect the effect of stimulus normalization in different ways, which can be modeled by a simple normalization model.

Performance in even a simple perceptual task depends on mouse secondary visual areas

Primary visual cortex (V1) in the mouse projects to numerous brain areas, including several secondary visual areas, frontal cortex, and basal ganglia. While it has been demonstrated that optogenetic silencing of V1 strongly impairs visually guided behavior, it is not known which downstream areas are required for visual behaviors. Here we trained mice to perform a contrast-increment change detection task, for which substantial stimulus information is present in V1. Optogenetic silencing of visual responses in secondary visual areas revealed that their activity is required for even this simple visual task. In vivo electrophysiology showed that, although inhibiting secondary visual areas could produce some feedback effects in V1, the principal effect was profound suppression at the location of the optogenetic light. The results show that pathways through secondary visual areas are necessary for even simple visual behaviors.

Texture Interpolation for Probing Visual Perception

Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (i.e. the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures. Compared to other CNN-based approaches, our interpolation method appears to match more closely the geometry of texture perception, and our mathematical framework is better suited to study its statistical nature. We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.

Within- and between-hemifield generalization of repetition suppression in inferior temporal cortex

The decrease in response with stimulus repetition is a common property observed in many sensory brain areas. This repetition suppression (RS) is ubiquitous in neurons of macaque inferior temporal (IT) cortex, the end-stage of the ventral visual pathway. The neural mechanisms of RS in IT are still unclear, and one possibility is that it is inherited from areas upstream to IT that show also RS. Since neurons in IT have larger receptive fields compared with earlier visual areas, we examined the inheritance hypothesis by presenting adapter and test stimuli at widely different spatial locations along both vertical and horizontal meridians and across hemifields. RS was present for distances between adapter and test stimuli up to 22° and when the two stimuli were presented in different hemifields. Also, we examined the position tolerance of the stimulus selectivity of adaptation by comparing the responses to a test stimulus following the same (repetition trial) or a different (alternation trial) adapter at a position different from the test stimulus. Stimulus-selective adaptation was still present and consistently stronger in the later phase of the response for distances up to 18°. Finally, we observed stimulus-selective adaptation in repetition trials even without a measurable excitatory response to the adapter stimulus. To accommodate these and previous data, we propose that at least part of the stimulus-selective adaptation in IT is based on short-term plasticity mechanisms within IT and/or reflects top-down activity from areas downstream to IT.NEW & NOTEWORTHY Neurons in inferior temporal cortex reduce their response when stimuli are repeated. To assess whether this repetition suppression is inherited from upstream visual areas, we examined the extent of its spatial generalization. We observed stimulus-selective adaptation when adapter and test stimuli were presented at widely different spatial positions and in different hemifields. These data suggest that at least part of the repetition suppression originates within inferior temporal cortex and/or reflects feedback from downstream areas.

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex

An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.

Dynamic shifts of visual and saccadic signals in prefrontal cortical regions 8Ar and FEF

Active vision is a fundamental process by which primates gather information about the external world. Multiple brain regions have been studied in the context of simple active vision tasks in which a visual target's appearance is temporally separated from saccade execution. Most neurons have tight spatial registration between visual and saccadic signals, and in areas such as prefrontal cortex (PFC), some neurons show persistent delay activity that links visual and motor epochs and has been proposed as a basis for spatial working memory. Many PFC neurons also show rich dynamics, which have been attributed to alternative working memory codes and the representation of other task variables. Our study investigated the transition between processing a visual stimulus and generating an eye movement in populations of PFC neurons in macaque monkeys performing a memory guided saccade task. We found that neurons in two subregions of PFC, the frontal eye fields (FEF) and area 8Ar, differed in their dynamics and spatial response profiles. These dynamics could be attributed largely to shifts in the spatial profile of visual and motor responses in individual neurons. This led to visual and motor codes for particular spatial locations that were instantiated by different mixtures of neurons, which could be important in PFC's flexible role in multiple sensory, cognitive, and motor tasks.NEW & NOTEWORTHY A central question in neuroscience is how the brain transitions from sensory representations to motor outputs. The prefrontal cortex contains neurons that have long been implicated as important in this transition and in working memory. We found evidence for rich and diverse tuning in these neurons, which was often spatially misaligned between visual and saccadic responses. This feature may play an important role in flexible working memory capabilities.

Frequency-separated principal component analysis of cortical population activity

A hallmark of neocortical activity is the presence of low-dimensional fluctuations in firing rate that are coordinated across neurons. However, the impact of these fluctuations on sensory processing remains unclear. Here, we examined fluctuations in populations of orientation-selective neurons from anesthetized macaque primary visual cortex (V1) during stimulus viewing as well as spontaneous activity. We introduce a novel approach termed frequency-separated principal component analysis (FS-PCA) to characterize these fluctuations. This method unveiled a distribution of components with a broad range of frequencies whose eigenvalues and variance followed an approximate power law. During stimulus viewing, subpopulations of V1 neurons correlated either positively or negatively with low-dimensional fluctuations. These two subpopulations displayed distinct activation properties and noise correlations in response to sensory input. Together, results suggest that slow, low-dimensional fluctuations in V1 population activity shape the response of individual neurons to oriented stimuli and may impact the transmission of sensory information to downstream regions of the primary visual system.NEW & NOTEWORTHY A method termed frequency-separated principal component analysis (FS-PCA) is introduced for analyzing populations of simultaneously recorded neurons. This framework extends standard principal component analysis by extracting components of activity delimited to specific frequency bands. FS-PCA revealed that circuits of the primary visual cortex generate a broad range of components dominated by low-frequency activity. Furthermore, low-dimensional fluctuations in population activity modulated the response of individual neurons to sensory input.

Inhibition stabilization is a widespread property of cortical networks

Many cortical network models use recurrent coupling strong enough to require inhibition for stabilization. Yet it has been experimentally unclear whether inhibition-stabilized network (ISN) models describe cortical function well across areas and states. Here, we test several ISN predictions, including the counterintuitive (paradoxical) suppression of inhibitory firing in response to optogenetic inhibitory stimulation. We find clear evidence for ISN operation in mouse visual, somatosensory, and motor cortex. Simple two-population ISN models describe the data well and let us quantify coupling strength. Although some models predict a non-ISN to ISN transition with increasingly strong sensory stimuli, we find ISN effects without sensory stimulation and even during light anesthesia. Additionally, average paradoxical effects result only with transgenic, not viral, opsin expression in parvalbumin (PV)-positive neurons; theory and expression data show this is consistent with ISN operation. Taken together, these results show strong coupling and inhibition stabilization are common features of the cortex.

A neural network for online spike classification that improves decoding accuracy

Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network's stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network's labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our network's classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding.NEW & NOTEWORTHY Although there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both off-line data processing and online decoding.

Comparison of tuning properties of gamma and high-gamma power in local field potential (LFP) versus electrocorticogram (ECoG) in visual cortex

Electrocorticogram (ECoG), obtained from macroelectrodes placed on the cortex, is typically used in drug-resistant epilepsy patients, and is increasingly being used to study cognition in humans. These studies often use power in gamma (30-70 Hz) or high-gamma (>80 Hz) ranges to make inferences about neural processing. However, while the stimulus tuning properties of gamma/high-gamma power have been well characterized in local field potential (LFP; obtained from microelectrodes), analogous characterization has not been done for ECoG. Using a hybrid array containing both micro and ECoG electrodes implanted in the primary visual cortex of two female macaques (for some stimulus conditions, separate ECoG and microelectrode arrays in two additional male macaques were also used), we compared the stimulus tuning preferences of gamma/high-gamma power in LFP versus ECoG in up to four monkeys, and found them to be surprisingly similar. High-gamma power, thought to index the average firing rate around the electrode, was highest for the smallest stimulus (0.3° radius), and decreased with increasing size in both LFP and ECoG, suggesting local origins of both signals. Further, gamma oscillations were similarly tuned in LFP and ECoG to stimulus orientation, contrast and spatial frequency. This tuning was significantly weaker in electroencephalogram (EEG), suggesting that ECoG is more like LFP than EEG. Overall, our results validate the use of ECoG in clinical and basic cognitive research.

Interaction between steady-state visually evoked potentials at nearby flicker frequencies

Steady-state visually evoked potential (SSVEP) studies routinely employ simultaneous presentation of two temporally modulated stimuli, with SSVEP amplitude modulations serving to index top-down cognitive processes. However, the nature of SSVEP amplitude modulations as a function of competing temporal frequency (TF) has not been systematically studied, especially in relation to the normalization framework which has been extensively used to explain visual responses to multiple stimuli. We recorded spikes and local field potential (LFP) from the primary visual cortex (V1) as well as EEG from two awake macaque monkeys while they passively fixated plaid stimuli with components counterphasing at different TFs. We observed asymmetric SSVEP response suppression by competing TFs (greater suppression for lower TFs), which further depended on the relative orientations of plaid components. A tuned normalization model, adapted to SSVEP responses, provided a good account of the suppression. Our results provide new insights into processing of temporally modulated visual stimuli.

Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex

Neural signals recorded at different scales contain information about environment and behavior and have been used to control Brain Machine Interfaces with varying degrees of success. However, a direct comparison of their efficacy has not been possible due to different recording setups, tasks, species, etc. To address this, we implanted customized arrays having both microelectrodes and electrocorticogram (ECoG) electrodes in the primary visual cortex of 2 female macaque monkeys, and also recorded electroencephalogram (EEG), while they viewed a variety of naturalistic images and parametric gratings. Surprisingly, ECoG had higher information and decodability than all other signals. Combining a few ECoG electrodes allowed more accurate decoding than combining a much larger number of microelectrodes. Control analyses showed that higher decoding accuracy of ECoG compared with local field potential was not because of differences in low-level visual features captured by them but instead because of larger spatial summation of the ECoG. Information was high in the 30-80 Hz range and at lower frequencies. Information in different frequencies and scales was nonredundant. These results have strong implications for Brain Machine Interface applications and for study of population representation of visual stimuli.SIGNIFICANCE STATEMENT Electrophysiological signals captured across scales by different recording electrodes are regularly used for Brain Machine Interfaces, but the information content varies due to electrode size and location. A systematic comparison of their efficiency for Brain Machine Interfaces is important but technically challenging. Here, we recorded simultaneous signals across four scales: spikes, local field potential, electrocorticogram (ECoG), and EEG, and compared their information and decoding accuracy for a large variety of naturalistic stimuli. We found that ECoGs were highly informative and outperformed other signals in information content and decoding accuracy.

A simple linear readout of MT supports motion direction-discrimination performance

Motion discrimination is a well-established model system for investigating how sensory signals are used to form perceptual decisions. Classic studies relating single-neuron activity in the middle temporal area (MT) to perceptual decisions have suggested that a simple linear readout could underlie motion discrimination behavior. A theoretically optimal readout, in contrast, would take into account the correlations between neurons and the sensitivity of individual neurons at each time point. However, it remains unknown how sophisticated the readout needs to be to support actual motion-discrimination behavior or to approach optimal performance. In this study, we evaluated the performance of various neurally plausible decoders, trained to discriminate motion direction from small ensembles of simultaneously recorded MT neurons. We found that decoding the stimulus without knowledge of the interneuronal correlations was sufficient to match an optimal (correlation aware) decoder. Additionally, a decoder could match the psychophysical performance of the animals with flat integration of up to half the stimulus and inherited temporal dynamics from the time-varying MT responses. These results demonstrate that simple, linear decoders operating on small ensembles of neurons can match both psychophysical performance and optimal sensitivity without taking correlations into account and that such simple read-out mechanisms can exhibit complex temporal properties inherited from the sensory dynamics themselves.NEW & NOTEWORTHY Motion perception depends on the ability to decode the activity of neurons in the middle temporal area. Theoretically optimal decoding requires knowledge of the sensitivity of neurons and interneuronal correlations. We report that a simple correlation-blind decoder performs as well as the optimal decoder for coarse motion discrimination. Additionally, the decoder could match the psychophysical performance with moderate temporal integration and dynamics inherited from sensory responses.

Altered functional interactions between neurons in primary visual cortex of macaque monkeys with experimental amblyopia

Amblyopia, a disorder in which vision through one of the eyes is degraded, arises because of defective processing of information by the visual system. Amblyopia often develops in humans after early misalignment of the eyes (strabismus) and can be simulated in macaque monkeys by artificially inducing strabismus. In such amblyopic animals, single-unit responses in primary visual cortex (V1) are appreciably reduced when evoked by the amblyopic eye compared with the other (fellow) eye. However, this degradation in single V1 neuron responsivity is not commensurate with the marked losses in visual sensitivity and resolution measured behaviorally. Here we explored the idea that changes in patterns of coordinated activity across populations of V1 neurons may contribute to degraded visual representations in amblyopia, potentially making it more difficult to read out evoked activity to support perceptual decisions. We studied the visually evoked activity of V1 neuronal populations in three macaques (Macaca nemestrina) with strabismic amblyopia and in one control animal. Activity driven through the amblyopic eye was diminished, and these responses also showed more interneuronal correlation at all stimulus contrasts than responses driven through the fellow eye or responses in the control animal. A decoding analysis showed that responses driven through the amblyopic eye carried less visual information than other responses. Our results suggest that part of the reduced visual capacity of amblyopes may be due to changes in the patterns of functional interaction among neurons in V1.NEW & NOTEWORTHY Previous work on the neurophysiological basis of amblyopia has largely focused on relating behavioral deficits to changes in visual processing by single neurons in visual cortex. In this study, we recorded simultaneously from populations of primary visual cortical (V1) neurons in macaques with amblyopia. We found changes in the strength and pattern of shared response variability between neurons. These changes in neuronal interactions could impair the visual representations of V1 populations driven by the amblyopic eye.

Verification of a Rapidly Multiplexed Circuit for Scalable Action Potential Recording

This report presents characterizations of in vivo neural recordings performed with a CMOS multichannel neural recording chip that uses rapid multiplexing directly at the electrodes, without any pre-amplification or buffering. Neural recordings were taken from a 16-channel microwire array implanted in rodent cortex, with comparison to a gold-standard commercial bench-top recording system. We were able to record well-isolated threshold crossings from 10 multiplexed electrodes and typical local field potential waveforms from 16, with strong agreement with the standard system (average SNR = 2.59 and 3.07 respectively). For 10 electrodes, the circuit achieves an effective area per channel of 0.0077 mm², which is >5x smaller than typical multichannel chips. Extensive characterizations of noise and signal quality are presented and compared to fundamental theory, as well as results from in vivo and in vitro experiments. By demonstrating the validation of rapid multiplexing directly at the electrodes, this report confirms it as a promising approach for reducing circuit area in massively-multichannel neural recording systems, which is crucial for scaling recording site density and achieving large-scale sensing of brain activity with high spatiotemporal resolution.

Modeling stimulus-dependent variability improves decoding of population neural responses

OBJECTIVE

Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding.

APPROACH

In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor  =  1). Here we use two alternative models-the Conway-Maxwell-Poisson (CMP) model and negative binomial (NB) model-to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor  >  1) or, for the CMP model, less than the mean (Fano factor  <  1).

MAIN RESULTS

We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%-12% compared to the Poisson model.

SIGNIFICANCE

Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.

A Critical Review of Microelectrode Arrays and Strategies for Improving Neural Interfaces

Though neural interface systems (NISs) can provide a potential solution for mitigating the effects of limb loss and central nervous system damage, the microelectrode array (MEA) component of NISs remains a significant limiting factor to their widespread clinical applications. Several strategies can be applied to MEA designs to increase their biocompatibility. Herein, an overview of NISs and their applications is provided, along with a detailed discussion of strategies for alleviating the foreign body response (FBR) and abnormalities seen at the interface of MEAs and the brain tissue following MEA implantation. Various surface modifications, including natural/synthetic surface coatings, hydrogels, and topography alterations, have shown to be highly successful in improving neural cell adhesion, reducing gliosis, and increasing MEA longevity. Different MEA surface geometries, such as those seen in the Utah and Michigan arrays, can help alleviate the resultant FBR by reducing insertion damage, while providing new avenues for improving MEA recording performance and resolution. Increasing overall flexibility of MEAs as well as reducing their stiffness is also shown to reduce MEA induced micromotion along with FBR severity. By combining multiple different properties into a single MEA, the severity and duration of an FBR postimplantation can be reduced substantially.

Modulation of local field potentials and neuronal activity in primate hippocampus during saccades

Primates use saccades to gather information about objects and their relative spatial arrangement, a process essential for visual perception and memory. It has been proposed that signals linked to saccades reset the phase of local field potential (LFP) oscillations in the hippocampus, providing a temporal window for visual signals to activate neurons in this region and influence memory formation. We investigated this issue by measuring hippocampal LFPs and spikes in two macaques performing different tasks with unconstrained eye movements. We found that LFP phase clustering (PC) in the alpha/beta (8-16 Hz) frequencies followed foveation onsets, while PC in frequencies lower than 8 Hz followed spontaneous saccades, even on a homogeneous background. Saccades to a solid grey background were not followed by increases in local neuronal firing, whereas saccades toward appearing visual stimuli were. Finally, saccade parameters correlated with LFPs phase and amplitude: saccade direction correlated with delta (≤4 Hz) phase, and saccade amplitude with theta (4-8 Hz) power. Our results suggest that signals linked to saccades reach the hippocampus, producing synchronization of delta/theta LFPs without a general activation of local neurons. Moreover, some visual inputs co-occurring with saccades produce LFP synchronization in the alpha/beta bands and elevated neuronal firing. Our findings support the hypothesis that saccade-related signals enact sensory input-dependent plasticity and therefore memory formation in the primate hippocampus.

Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Our understanding of the neural basis of perceptual decision making has been built in part on relating co-fluctuations of single neuron responses to perceptual decisions on a trial-by-trial basis. The strength of this relationship is often compared across neurons or brain areas, recorded in different sessions, animals, or variants of a task. We sought to extend our understanding of perceptual decision making in three ways. First, we measured neuronal activity simultaneously in early [primary visual cortex (V1)] and midlevel (V4) visual cortex while macaque monkeys performed a fine orientation discrimination perceptual task. This allowed a direct comparison of choice signals in these two areas, including their dynamics. Second, we asked how our ability to predict animals' decisions would be improved by considering small simultaneously-recorded neuronal populations rather than individual units. Finally, we asked whether predictions would be improved by taking into account the animals' choice and reward histories, which can strongly influence decision making. We found that responses of individual V4 neurons were weakly predictive of decisions, but only in a brief epoch between stimulus offset and the indication of choice. In V1, few neurons showed significant decision-related activity. Analysis of neuronal population responses revealed robust choice-related information in V4 and substantially weaker signals in V1. Including choice- and reward-history information improved performance further, particularly when the recorded populations contained little decision-related information. Our work shows the power of using neuronal populations and decision history when relating neuronal responses to the perceptual decisions they are thought to underlie.SIGNIFICANCE STATEMENT Decades of research has provided a rich description of how visual information is represented in the visual cortex. Yet how cortical responses relate to visual perception remains poorly understood. Here we relate fluctuations in small neuronal population responses, recorded simultaneously in primary visual cortex (V1) and area V4 of monkeys, to perceptual reports in an orientation discrimination task. Choice-related signals were robust in V4, particularly late in the behavioral trial, but not in V1. Models that include both neuronal responses and choice-history information were able to predict a substantial portion of decisions. Our work shows the power of integrating information across neurons and including decision history in relating neuronal responses to perceptual decisions.

Distinct Sources of Variability Affect Eye Movement Preparation

The sequence of events leading to an eye movement to a target begins the moment visual information has reached the brain, well in advance of the eye movement itself. The process by which visual information is encoded and used to generate a motor plan has been the focus of substantial interest partly because of the rapid and reproducible nature of saccadic eye movements, and the key role that they play in primate behavior. Signals related to eye movements are present in much of the primate brain, yet most neurophysiological studies of the transition from vision to eye movements have measured the activity of one neuron at a time. Less is known about how the coordinated action of populations of neurons contribute to the initiation of eye movements. One cortical area of particular interest in this process is the frontal eye fields, a region of prefrontal cortex that has descending projections to oculomotor control centers. We recorded from populations of frontal eye field neurons in macaque monkeys engaged in a memory-guided saccade task. We found a variety of neurons with visually evoked responses, saccade-aligned responses, and mixtures of both. We took advantage of the simultaneous nature of the recordings to measure variability in individual neurons and pairs of neurons from trial-to-trial, as well as the moment-to-moment population activity structure. We found that these measures were related to saccadic reaction times, suggesting that the population-level organization of frontal eye field activity is important for the transition from perception to movement.SIGNIFICANCE STATEMENT The transition from perception to action involves coordination among neurons across the brain. In the case of eye movements, visual and motor signals coexist in individual neurons as well as in neighboring neurons. We used a task designed to compartmentalize the visual and motor aspects of this transition and studied populations of neurons in the frontal eye fields, a key cortical area containing neurons that are implicated in the transition from vision to eye movements. We found that the time required for subjects to produce an eye movement could be predicted from the statistics of the neuronal response of populations of frontal eye field neurons, suggesting that these neurons coordinate their activity to optimize the transition from perception to action.