Improving data quality in neuronal population recordings

Aberration correction in long GRIN lens-based microendoscopes for extended field-of-view two-photon imaging in deep brain regions

Two-photon (2P) fluorescence imaging through gradient index (GRIN) lens-based endoscopes is fundamental to investigate the functional properties of neural populations in deep brain circuits. However, GRIN lenses have intrinsic optical aberrations, which severely degrade their imaging performance. GRIN aberrations decrease the signal-to-noise ratio (SNR) and spatial resolution of fluorescence signals, especially in lateral portions of the field-of-view (FOV), leading to restricted FOV and smaller number of recorded neurons. This is especially relevant for GRIN lenses of several millimeters in length, which are needed to reach the deeper regions of the rodent brain. We have previously demonstrated a novel method to enlarge the FOV and improve the spatial resolution of 2P microendoscopes based on GRIN lenses of length <4.1 mm (Antonini et al., 2020). However, previously developed microendoscopes were too short to reach the most ventral regions of the mouse brain. In this study, we combined optical simulations with fabrication of aspherical polymer microlenses through three-dimensional (3D) microprinting to correct for optical aberrations in long (length >6 mm) GRIN lens-based microendoscopes (diameter, 500 µm). Long corrected microendoscopes had improved spatial resolution, enabling imaging in significantly enlarged FOVs. Moreover, using synthetic calcium data we showed that aberration correction enabled detection of cells with higher SNR of fluorescent signals and decreased cross-contamination between neurons. Finally, we applied long corrected microendoscopes to perform large-scale and high-precision recordings of calcium signals in populations of neurons in the olfactory cortex, a brain region laying approximately 5 mm from the brain surface, of awake head-fixed mice. Long corrected microendoscopes are powerful new tools enabling population imaging with unprecedented large FOV and high spatial resolution in the most ventral regions of the mouse brain.

Compositional architecture: Orthogonal neural codes for task context and spatial memory in prefrontal cortex

The prefrontal cortex (PFC) is crucial for maintaining working memory across diverse cognitive tasks, yet how it adapts to varying task demands remains unclear. Compositional theories propose that cognitive processes in neural network rely on shared components that can be reused to support different behaviors. However, previous studies have suggested that working memory components are task specific, challenging this framework. Here, we revisit this question using a population-based approach. We recorded neural activity in macaque monkeys performing two spatial working memory tasks with opposing goals: one requiring movement toward previously presented spatial locations (look task) and the other requiring avoidance of those locations (no-look task). Despite differences in task demands, we found that spatial memory representations were largely conserved at the population level, with a common low-dimensional neural subspace encoding memory across both tasks. In parallel, task identity was encoded in an orthogonal subspace, providing a stable and independent representation of contextual information. These results provide neural evidence for a compositional model of working memory, where representational geometry enables the efficient and flexible reuse of mnemonic codes across behavioral contexts while maintaining an independent representation of context.

Visual experience orthogonalizes visual cortical stimulus responses via population code transformation

Sensory and behavioral experience can alter visual cortical stimulus coding, but the precise form of this plasticity is unclear. We measured orientation tuning in 4,000-neuron populations of mouse V1 before and after training on a visuomotor task. Changes to single-cell tuning curves appeared complex, including development of asymmetries and of multiple peaks. Nevertheless, these complex tuning curve transformations can be explained by a simple equation: a convex transformation suppressing responses to task stimuli specifically in cells responding at intermediate levels. The strength of the transformation varies across trials, suggesting a dynamic circuit mechanism rather than static synaptic plasticity. The transformation results in sparsening and orthogonalization of population codes for task stimuli. It cannot improve the performance of an optimal stimulus decoder, which is already perfect even for naive codes, but it improves the performance of a suboptimal decoder model with inductive bias as might be found in downstream readout circuits.

Bio-inspired electronics: Soft, biohybrid, and "living" neural interfaces

Neural interface technologies are increasingly evolving towards bio-inspired approaches to enhance integration and long-term functionality. Recent strategies merge soft materials with tissue engineering to realize biologically-active and/or cell-containing living layers at the tissue-device interface that enable seamless biointegration and novel cell-mediated therapeutic opportunities. This review maps the field of bio-inspired electronics and discusses key recent developments in tissue-like and regenerative bioelectronics, from soft biomaterials and surface-functionalized bioactive coatings to cell-containing 'biohybrid' and 'all-living' interfaces. We define and contextualize key terminology in this emerging field and highlight how biological and living components can bridge the gap to clinical translation.

Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization

Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.

A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems

Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm2 per channel.

Multifunctional Nanomesh Enables Cellular-Resolution, Elastic Neuroelectronics

Silicone-based devices have the potential to achieve an ideal interface with nervous tissue but suffer from scalability, primarily due to the mechanical mismatch between established electronic materials and soft elastomer substrates. This study presents a novel approach using conventional electrode materials through multifunctional nanomesh to achieve reliable elastic microelectrodes directly on polydimethylsiloxane (PDMS) silicone with an unprecedented cellular resolution. This engineered nanomesh features an in-plane nanoscale mesh pattern, physically embodied by a stack of three thin-film materials by design, namely Parylene-C for mechanical buffering, gold (Au) for electrical conduction, and Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) for improved electrochemical interfacing. Nanomesh elastic neuroelectronics are validated using single-unit recording from the small and curvilinear epidural surface of mouse dorsal root ganglia (DRG) with device self-conformed and superior recording quality compared to plastic control devices requiring manual pressing is demonstrated. Electrode scaling studies from in vivo epidural recording further revealed the need for cellular resolution for high-fidelity recording of single-unit activities and compound action potentials. In addition to creating a minimally invasive device to effectively interface with DRG sensory afferents at a single-cell resolution, this study establishes nanomeshing as a practical pathway to leverage traditional electrode materials for a new class of elastic neuroelectronics.

A nonoscillatory, millisecond-scale embedding of brain state provides insight into behavior

The most robust and reliable signatures of brain states are enriched in rhythms between 0.1 and 20 Hz. Here we address the possibility that the fundamental unit of brain state could be at the scale of milliseconds and micrometers. By analyzing high-resolution neural activity recorded in ten mouse brain regions over 24 h, we reveal that brain states are reliably identifiable (embedded) in fast, nonoscillatory activity. Sleep and wake states could be classified from 100 to 101 ms of neuronal activity sampled from 100 µm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high-frequency embedding is robust to substates, sharp-wave ripples and cortical on/off states. Individual regions intermittently switched states independently of the rest of the brain, and such brief state discontinuities coincided with brief behavioral discontinuities. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation.

High-Fidelity Bioelectrodes with Bidirectional Ion-Electron Transduction Capability by Integrating Multiple Charge-Transfer Processes

Bioelectronics is an exciting field that bridges the gap between physiological activities and external electronic devices, striving for high resolution, high conformability, scalability, and ease of integration. One crucial component in bioelectronics is bioelectrodes, designed to convert neural activity into electronic signals or vice versa. Previously reported bioelectrodes have struggled to meet several essential requirements simultaneously: high-fidelity signal transduction, high charge injection capability, strain resistance, and multifunctionality. This work introduces a novel strategy for fabricating superior bioelectrodes by merging multiple charge-transfer processes. The resulting bioelectrodes offer accurate ion-to-electron transduction for capturing electrophysiological signals, dependable charge injection capability for neuromodulation, consistent electrode potential for artifact rejection and biomolecule sensing, and high transparency for seamless integration with optoelectronics. Furthermore, the bioelectrode can be designed to be strain-insensitive by isolating signal transduction from electron transportation. The innovative concept presented in this work holds great promise for extending to other electrode materials and paves the way for the advancement of multimodal bioelectronics.

Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice

Objective.This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain-computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.Approach.This study utilized single-shank, 16-channel,100µm site-spacing Michigan-style microelectrodes (3 mm length, 703µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11-13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Multi-day Neuron Tracking in High Density Electrophysiology Recordings using EMD

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from one to 47 days, with an 84% average recovery rate.

A hippocampus-accumbens code guides goal-directed appetitive behavior

The dorsal hippocampus (dHPC) is a key brain region for the expression of spatial memories, such as navigating towards a learned reward location. The nucleus accumbens (NAc) is a prominent projection target of dHPC and implicated in value-based action selection. Yet, the contents of the dHPC→NAc information stream and their acute role in behavior remain largely unknown. Here, we found that optogenetic stimulation of the dHPC→NAc pathway while mice navigated towards a learned reward location was both necessary and sufficient for spatial memory-related appetitive behaviors. To understand the task-relevant coding properties of individual NAc-projecting hippocampal neurons (dHPC→NAc), we used in vivo dual-color two-photon imaging. In contrast to other dHPC neurons, the dHPC→NAc subpopulation contained more place cells, with enriched spatial tuning properties. This subpopulation also showed enhanced coding of non-spatial task-relevant behaviors such as deceleration and appetitive licking. A generalized linear model revealed enhanced conjunctive coding in dHPC→NAc neurons which improved the identification of the reward zone. We propose that dHPC routes specific reward-related spatial and behavioral state information to guide NAc action selection.

High-density transparent graphene arrays for predicting cellular calcium activity at depth from surface potential recordings

Optically transparent neural microelectrodes have facilitated simultaneous electrophysiological recordings from the brain surface with the optical imaging and stimulation of neural activity. A remaining challenge is to scale down the electrode dimensions to the single-cell size and increase the density to record neural activity with high spatial resolution across large areas to capture nonlinear neural dynamics. Here we developed transparent graphene microelectrodes with ultrasmall openings and a large, transparent recording area without any gold extensions in the field of view with high-density microelectrode arrays up to 256 channels. We used platinum nanoparticles to overcome the quantum capacitance limit of graphene and to scale down the microelectrode diameter to 20 µm. An interlayer-doped double-layer graphene was introduced to prevent open-circuit failures. We conducted multimodal experiments, combining the recordings of cortical potentials of microelectrode arrays with two-photon calcium imaging of the mouse visual cortex. Our results revealed that visually evoked responses are spatially localized for high-frequency bands, particularly for the multiunit activity band. The multiunit activity power was found to be correlated with cellular calcium activity. Leveraging this, we employed dimensionality reduction techniques and neural networks to demonstrate that single-cell and average calcium activities can be decoded from surface potentials recorded by high-density transparent graphene arrays.

Tuning Microelectrodes' Impedance to Improve Fast Ripples Recording

Epilepsy is a chronic neurological disorder characterized by recurrent seizures resulting from abnormal neuronal hyperexcitability. In the case of pharmacoresistant epilepsy requiring resection surgery, the identification of the Epileptogenic Zone (EZ) is critical. Fast Ripples (FRs; 200-600 Hz) are one of the promising biomarkers that can aid in EZ delineation. However, recording FRs requires physically small electrodes. These microelectrodes suffer from high impedance, which significantly impacts FRs' observability and detection. In this study, we investigated the potential of a conductive polymer coating to enhance FR observability. We employed biophysical modeling to compare two types of microelectrodes: Gold (Au) and Au coated with the conductive polymer poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (Au/PEDOT:PSS). These electrodes were then implanted into the CA1 hippocampal neural network of epileptic mice to record FRs during epileptogenesis. The results showed that the polymer-coated electrodes had a two-order lower impedance as well as a higher transfer function amplitude and cut-off frequency. Consequently, FRs recorded with the PEDOT:PSS-coated microelectrode yielded significantly higher signal energy compared to the uncoated one. The PEDOT:PSS coating improved the observability of the recorded FRs and thus their detection. This work paves the way for the development of signal-specific microelectrode designs that allow for better targeting of pathological biomarkers.

Biohybrid tongue based on hypothalamic neuronal network-on-a-chip for real-time blood glucose sensing and assessment

The expression of sweet receptors in the hypothalamus has been implicated in energy homeostasis control and the pathogenesis of obesity and diabetes. However, the exact mechanism by which hypothalamic glucose-sensing neurons function remains unclear. Conventional detection methods, such as fiber photometry, optogenetics, brain-machine interfaces, patch clamp and calcium imaging, pose limitations for real-time glucose perception due to their complexity, cytotoxicity and so on. Therefore, this study proposes a biohybrid tongue based on hypothalamic neuronal network (HNN)-on-a-chip coupling with microelectrode array (MEA) for real-time glucose perception. Hypothalamic neuronal cultures were cultivated on a two-dimensional "brain-on-chip" device, enabling the formation of neuronal networks and electrophysiological signal detection. Additionally, we investigated the endogenous expression of sweet taste receptors (T1R2/T1R3) in hypothalamic neuronal cells, providing the basis for the biohybrid tongue based on HNN-on-a-chip's sweetness detection capabilities. The spike signal response to sucrose and glucose stimulation was detected, and concentration-dependent responses were explored with glucose concentrations ranging from 0.01 mM to 8 mM. MEAs allow for real-time recordings, enabling the observation of dynamic changes in neuronal responses to glucose fluctuations over time. The biohybrid tongue based on HNN-on-a-chip can measure various parameters, including spike frequency and amplitude, providing insights into neuronal firing patterns and excitability. Moreover, hypothalamic glucoregulatory neurons that sense and respond to changes in blood glucose was identified, including glucose-excited neurons (GE-Neurons) and glucose-inhibited neurons (GI-Neurons). The detection range for GE-Neurons spans from 0.4 to 6 mM, while GI-Neurons demonstrate sensitivity within the range of 1-8 mM. And the glucose detection limit was firmly established at 0.01 mM. Through non-linear regression analysis, the IC50 for GI-Neurons' spike firing was determined to be 4.18 mM. In conclusion, the biohybrid tongue based on HNN-on-a-chip offers a valuable in vitro tool for studying hypothalamic neurons, elucidating glucose sensing mechanisms, and understanding hypothalamic neuronal function.

Neuronal K+-Cl- cotransporter KCC2 as a promising drug target for epilepsy treatment

Epilepsy is a prevalent neurological disorder characterized by unprovoked seizures. γ-Aminobutyric acid (GABA) serves as the primary fast inhibitory neurotransmitter in the brain, and GABA binding to the GABAA receptor (GABAAR) regulates Cl- and bicarbonate (HCO3-) influx or efflux through the channel pore, leading to GABAergic inhibition or excitation, respectively. The neuron-specific K+-Cl- cotransporter 2 (KCC2) is essential for maintaining a low intracellular Cl- concentration, ensuring GABAAR-mediated inhibition. Impaired KCC2 function results in GABAergic excitation associated with epileptic activity. Loss-of-function mutations and altered expression of KCC2 lead to elevated [Cl-]i and compromised synaptic inhibition, contributing to epilepsy pathogenesis in human patients. KCC2 antagonism studies demonstrate the necessity of limiting neuronal hyperexcitability within the brain, as reduced KCC2 functioning leads to seizure activity. Strategies focusing on direct (enhancing KCC2 activation) and indirect KCC2 modulation (altering KCC2 phosphorylation and transcription) have proven effective in attenuating seizure severity and exhibiting anti-convulsant properties. These findings highlight KCC2 as a promising therapeutic target for treating epilepsy. Recent advances in understanding KCC2 regulatory mechanisms, particularly via signaling pathways such as WNK, PKC, BDNF, and its receptor TrkB, have led to the discovery of novel small molecules that modulate KCC2. Inhibiting WNK kinase or utilizing newly discovered KCC2 agonists has demonstrated KCC2 activation and seizure attenuation in animal models. This review discusses the role of KCC2 in epilepsy and evaluates its potential as a drug target for epilepsy treatment by exploring various strategies to regulate KCC2 activity.

A neural code for time and space in the human brain

Time and space are primary dimensions of human experience. Separate lines of investigation have identified neural correlates of time and space, yet little is known about how these representations converge during self-guided experience. Here, 10 subjects with intracranially implanted microelectrodes play a timed, virtual navigation game featuring object search and retrieval tasks separated by fixed delays. Time cells and place cells activate in parallel during timed navigation intervals, whereas a separate time cell sequence spans inter-task delays. The prevalence, firing rates, and behavioral coding strengths of time cells and place cells are indistinguishable-yet time cells selectively remap between search and retrieval tasks, while place cell responses remain stable. Thus, the brain can represent time and space as overlapping but dissociable dimensions. Time cells and place cells may constitute a biological basis for the cognitive map of spatiotemporal context onto which memories are written.

Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes

Objective

This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.

Approach

This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.

Main Results

The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.

Significance

This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

A selection and targeting framework of cortical locations for line-scanning fMRI

Depth-resolved functional magnetic resonance imaging (fMRI) is an emerging field growing in popularity given the potential of separating signals from different computational processes in cerebral cortex. Conventional acquisition schemes suffer from low spatial and temporal resolutions. Line-scanning methods allow depth-resolved fMRI by sacrificing spatial coverage to sample blood oxygenated level-dependent (BOLD) responses at ultra-high temporal and spatial resolution. For neuroscience applications, it is critical to be able to place the line accurately to (1) sample the right neural population and (2) target that neural population with tailored stimuli or tasks. To this end, we devised a multi-session framework where a target cortical location is selected based on anatomical and functional properties. The line is then positioned according to this information in a separate second session, and we tailor the experiment to focus on the target location. Anatomically, the precision of the line placement was confirmed by projecting a nominal representation of the acquired line back onto the surface. Functional estimates of neural selectivities in the line, as quantified by a visual population-receptive field model, resembled the target selectivities well for most subjects. This functional precision was quantified in detail by estimating the distance between the visual field location of the targeted vertex and the location in visual cortex (V1) that most closely resembled the line-scanning estimates; this distance was on average ~5.5 mm. Given the dimensions of the line, differences in acquisition, session, and stimulus design, this validates that line-scanning can be used to probe local neural sensitivities across sessions. In summary, we present an accurate framework for line-scanning MRI; we believe such a framework is required to harness the full potential of line-scanning and maximize its utility. Furthermore, this approach bridges canonical fMRI experiments with electrophysiological experiments, which in turn allows novel avenues for studying human physiology non-invasively.

Conducting Polymer-Hydrogel Interpenetrating Networks for Improving the Electrode-Neural Interface

Implantable neural microelectrodes are recognized as a bridge for information exchange between inner organisms and outer devices. Combined with novel modulation technologies such as optogenetics, it offers a highly precise methodology for the dissection of brain functions. However, achieving chronically effective and stable microelectrodes to explore the electrophysiological characteristics of specific neurons in free-behaving animals continually poses great challenges. To resolve this, poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)/poly(vinyl alcohol) (PEDOT/PSS/PVA) interpenetrating conducting polymer networks (ICPN) are fabricated via a hydrogel scaffold precoating and electrochemical polymerization process to improve the performance of neural microelectrodes. The ICPN films exhibit robust interfacial adhesion, a significantly lower electrochemical impedance, superior mechanical properties, and improved electrochemical stability compared to the pure poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)(PEDOT/PSS) films, which may be attributed to the three-dimensional (3D) porous microstructure of the ICPN. Hippocampal neurons and rat pheochromocytoma cells (PC12 cells) adhesion on ICPN and neurite outgrowth are observed, indicating enhanced biocompatibility. Furthermore, alleviated tissue response at the electrode-neural tissue interface and improved recording signal quality are confirmed by histological and electrophysiological studies, respectively. Owing to these merits, optogenetic modulations and electrophysiological recordings are performed in vivo, and an anxiolytic effect of hippocampal glutamatergic neurons on behavior is shown. This study demonstrates the effectiveness and advantages of ICPN-modified neural implants for in vivo applications.

Seeing beyond the spikes: reconstructing the complete spatiotemporal membrane potential distribution from paired intra- and extracellular recordings

Although electrophysiologists have been recording intracellular neural activity routinely ever since the ground-breaking work of Hodgkin and Huxley, and extracellular multichannel electrodes have also been used frequently and extensively, a practical experimental method to track changes in membrane potential along a complete single neuron is still lacking. Instead of obtaining multiple intracellular measurements on the same neuron, we propose an alternative method by combining single-channel somatic patch-clamp and multichannel extracellular potential recordings. In this work, we show that it is possible to reconstruct the complete spatiotemporal distribution of the membrane potential of a single neuron with the spatial resolution of an extracellular probe during action potential generation. Moreover, the reconstruction of the membrane potential allows us to distinguish between the two major but previously hidden components of the current source density (CSD) distribution: the resistive and the capacitive currents. This distinction provides a clue to the clear interpretation of the CSD analysis, because the resistive component corresponds to transmembrane ionic currents (all the synaptic, voltage-sensitive and passive currents), whereas capacitive currents are considered to be the main contributors of counter-currents. We validate our model-based reconstruction approach on simulations and demonstrate its application to experimental data obtained in vitro via paired extracellular and intracellular recordings from a single pyramidal cell of the rat hippocampus. In perspective, the estimation of the spatial distribution of resistive membrane currents makes it possible to distiguish between active and passive sinks and sources of the CSD map and the localization of the synaptic input currents, which make the neuron fire. KEY POINTS: A new computational method is introduced to calculate the unbiased current source density distribution on a single neuron with known morphology. The relationship between extracellular and intracellular electric potential is determined via mathematical formalism, and a new reconstruction method is applied to reveal the full spatiotemporal distribution of the membrane potential and the resistive and capacitive current components. The new reconstruction method was validated on simulations. Simultaneous and colocalized whole-cell patch-clamp and multichannel silicon probe recordings were performed from the same pyramidal neuron in the rat hippocampal CA1 region, in vitro. The method was applied in experimental measurements and returned precise and distinctive characteristics of various intracellular phenomena, such as action potential generation, signal back-propagation and the initial dendritic depolarization preceding the somatic action potential.

A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior

Sleep and wake are understood to be slow, long-lasting processes that span the entire brain. Brain states correlate with many neurophysiological changes, yet the most robust and reliable signature of state is enriched in rhythms between 0.1 and 20 Hz. The possibility that the fundamental unit of brain state could be a reliable structure at the scale of milliseconds and microns has not been addressed due to the physical limits associated with oscillation-based definitions. Here, by analyzing high resolution neural activity recorded in 10 anatomically and functionally diverse regions of the murine brain over 24 h, we reveal a mechanistically distinct embedding of state in the brain. Sleep and wake states can be accurately classified from on the order of 100 to 101 ms of neuronal activity sampled from 100 μm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high frequency embedding is robust to substates and rapid events such as sharp wave ripples and cortical ON/OFF states. To ascertain whether such fast and local structure is meaningful, we leveraged our observation that individual circuits intermittently switch states independently of the rest of the brain. Brief state discontinuities in subsets of circuits correspond with brief behavioral discontinuities during both sleep and wake. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation, and that this resolution can contribute to an understanding of cognition and behavior.

A deep learning network based on CNN and sliding window LSTM for spike sorting

Spike sorting plays an essential role to obtain electrophysiological activity of single neuron in the fields of neural signal decoding. With the development of electrode array, large numbers of spikes are recorded simultaneously, which rises the need for accurate automatic and generalization algorithms. Hence, this paper proposes a spike sorting model with convolutional neural network (CNN) and a spike classification model with combination of CNN and Long-Short Term Memory (LSTM). The recall rate of our detector could reach 94.40% in low noise level dataset. Although the recall declined with the increasing noise level, our model still presented higher feasibility and better robustness than other models. In addition, the results of our classification model presented an accuracy of greater than 99% in simulated data and an average accuracy of about 95% in experimental data, suggesting our classifier outperforms the current "WMsorting" and other deep learning models. Moreover, the performance of our whole algorithm was evaluated through simulated data and the results shows that the accuracy of spike sorting reached about 97%. It is noteworthy to say that, this proposed algorithm could be used to achieve accurate and robust automated spike detection and spike classification.

Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology

Objective.Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.Approach.Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12+ weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.Main results.Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7 ± 4.6µm,X-± S) in layer V motor cortex.Significance.Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.

A modular architecture for organizing, processing and sharing neurophysiology data

We describe an architecture for organizing, integrating and sharing neurophysiology data within a single laboratory or across a group of collaborators. It comprises a database linking data files to metadata and electronic laboratory notes; a module collecting data from multiple laboratories into one location; a protocol for searching and sharing data and a module for automatic analyses that populates a website. These modules can be used together or individually, by single laboratories or worldwide collaborations.

Subdural CMOS optical probe (SCOPe) for bidirectional neural interfacing

Optical neurotechnologies use light to interface with neurons and can monitor and manipulate neural activity with high spatial-temporal precision over large cortical extents. While there has been significant progress in miniaturizing microscope for head-mounted configurations, these existing devices are still very bulky and could never be fully implanted. Any viable translation of these technologies to human use will require a much more noninvasive, fully implantable form factor. Here, we leverage advances in microelectronics and heterogeneous optoelectronic packaging to develop a transformative, ultrathin, miniaturized device for bidirectional optical stimulation and recording: the subdural CMOS Optical Probe (SCOPe). By being thin enough to lie entirely within the subdural space of the primate brain, SCOPe defines a path for the eventual human translation of a new generation of brain-machine interfaces based on light.

Respiratory entrainment of units in the mouse parietal cortex depends on vigilance state

Synchronous oscillations are essential for coordinated activity in neuronal networks and, hence, for behavior and cognition. While most network oscillations are generated within the central nervous system, recent evidence shows that rhythmic body processes strongly influence activity patterns throughout the brain. A major factor is respiration (Resp), which entrains multiple brain regions at the mesoscopic (local field potential) and single-cell levels. However, it is largely unknown how such Resp-driven rhythms interact or compete with internal brain oscillations, especially those with similar frequency domains. In mice, Resp and theta (θ) oscillations have overlapping frequencies and co-occur in various brain regions. Here, we investigated the effects of Resp and θ on neuronal discharges in the mouse parietal cortex during four behavioral states which either show prominent θ (REM sleep and active waking (AW)) or lack significant θ (NREM sleep and waking immobility (WI)). We report a pronounced state-dependence of spike modulation by both rhythms. During REM sleep, θ effects on unit discharges dominate, while during AW, Resp has a larger influence, despite the concomitant presence of θ oscillations. In most states, unit modulation by θ or Resp increases with mean firing rate. The preferred timing of Resp-entrained discharges (inspiration versus expiration) varies between states, indicating state-specific and different underlying mechanisms. Our findings show that neurons in an associative cortex area are differentially and state-dependently modulated by two fundamentally different processes: brain-endogenous θ oscillations and rhythmic somatic feedback signals from Resp.

NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging

The development of two-photon microscopy and Ca²⁺ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca²⁺ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca²⁺ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca²⁺ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca²⁺ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process.

Diversity in spatial frequency, temporal frequency, and speed tuning across mouse visual cortical areas and layers

The mouse visual system consists of several visual cortical areas thought to be specialized for different visual features and/or tasks. Previous studies have revealed differences between primary visual cortex (V1) and other higher visual areas, namely, anterolateral (AL) and posteromedial (PM), and their tuning preferences for spatial and temporal frequency. However, these differences have primarily been characterized using methods that are biased toward superficial layers of cortex, such as two-photon calcium imaging. Fewer studies have investigated cell types in deeper layers of these areas and their tuning preferences. Because superficial versus deep-layer neurons and different types of deep-layer neurons are known to have different feedforward and feedback inputs and outputs, comparing the tuning preferences of these groups is important for understanding cortical visual information processing. In this study, we used extracellular electrophysiology and two-photon calcium imaging targeted toward two different layer 5 cell classes to characterize their tuning properties in V1, AL, and PM. We find that deep-layer neurons, similar to superficial layer neurons, are also specialized for different spatial and temporal frequencies, with the strongest differences between AL and V1, and AL and PM, but not V1 and PM. However, we note that the deep-layer neuron populations preferred a larger range of SFs and TFs compared to previous studies. We also find that extratelencephalically projecting layer 5 neurons are more direction selective than intratelencephalically projecting layer 5 neurons.

In Vivo Penetrating Microelectrodes for Brain Electrophysiology

In recent decades, microelectrodes have been widely used in neuroscience to understand the mechanisms behind brain functions, as well as the relationship between neural activity and behavior, perception and cognition. However, the recording of neuronal activity over a long period of time is limited for various reasons. In this review, we briefly consider the types of penetrating chronic microelectrodes, as well as the conductive and insulating materials for microelectrode manufacturing. Additionally, we consider the effects of penetrating microelectrode implantation on brain tissue. In conclusion, we review recent advances in the field of in vivo microelectrodes.

Neuronal Activity Reporters as Drug Screening Platforms

Understanding how neuronal activity changes and detecting such changes in both normal and disease conditions is of fundamental importance to the field of neuroscience. Neuronal activity plays important roles in the formation and function of both synapses and circuits, and dysregulation of these processes has been linked to a number of debilitating diseases such as autism, schizophrenia, and epilepsy. Despite advances in our understanding of synapse biology and in how it is altered in disease, the development of therapeutics for these diseases has not advanced apace. Many neuronal activity assays have been developed over the years using a variety of platforms and approaches, but major limitations persist. Current assays, such as fluorescence indicators are not designed to monitor neuronal activity over a long time, they are typically low-throughput or lack sensitivity. These are major barriers to the development of new therapies, as drug screening needs to be both high-throughput to screen through libraries of compounds, and longitudinal to detect any effects that may emerge after continued application of the drug. This review will cover existing assays for measuring neuronal activity and highlight a live-cell assay recently developed. This assay can be performed with easily accessible lab equipment, is both scalable and longitudinal, and can be combined with most other established methods.

Classification of overlapping spikes using convolutional neural networks and long short term memory

Spike sorting is one of the key techniques to understand brain activity. In this paper, we propose a novel deep learning approach based on convolutional neural networks (CNN) and long short term memory (LSTM) to implement overlapping spike sorting. The results of the simulated data demonstrated that the clustering accuracy was greater than 99.9% and 99.0% for non-overlapping spikes and overlapping spikes, respectively. Moreover, the proposed method performed better than our previous deep learning approach named "1D-CNN". In addition, the experimental data recorded from the primary visual cortex of a macaque monkey were used to evaluate the proposed method in a practical application. It was shown that the method could successfully isolate most overlapping spikes of different neurons (ranging from two to five). In summary, the CNN + LSTM method proposed in this paper is of great advantage for overlapping spike sorting with high accuracy. It lays the foundation for application in more challenging works, such as distinguishing the simultaneous recordings of multichannel neuronal activities.

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML's popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

ICoRD: Iterative correlation-based ROI detection method for the extraction of neural signals in calcium imaging

OBJECTIVE

In vivo calcium imaging is a standard neuroimaging technique that allows selective observation of target neuronal activities. In calcium imaging, neuron activation signals provide key information for the investigation of neural circuits. For efficient extraction of the calcium signals of neurons, selective detection of the region of interest (ROI) pixels corresponding to the active subcellular region of the target neuron is essential. However, current ROI detection methods for calcium imaging data exhibit a relatively low signal extraction performance from neurons with a low signal-to-noise power ratio (SNR). This is problematic because a low SNR is unavoidable in many biological experiments.

APPROACH

Therefore, we propose an iterative correlation-based ROI detection (ICoRD) method that robustly extracts the calcium signal of the target neuron from a calcium imaging series with severe noise.

MAIN RESULTS

ICoRD extracts calcium signals closer to the ground-truth calcium signal than the conventional method from simulated calcium imaging data in all low SNR ranges. Additionally, this study confirmed that ICoRD robustly extracts activation signals against noise, even within in vivo environments.

SIGNIFICANCE

ICoRD showed reliable detection from neurons with a low SNR and sparse activation, which were not detected by conventional methods. ICoRD will facilitate our understanding of neural circuit activity by providing significantly improved ROI detection in noisy images.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

High-density single-unit human cortical recordings using the Neuropixels probe

The action potential is a fundamental unit of neural computation. Even though significant advances have been made in recording large numbers of individual neurons in animal models, translation of these methodologies to humans has been limited because of clinical constraints and electrode reliability. Here, we present a reliable method for intraoperative recording of dozens of neurons in humans using the Neuropixels probe, yielding up to ∼100 simultaneously recorded single units. Most single units were active within 1 min of reaching target depth. The motion of the electrode array had a strong inverse correlation with yield, identifying a major challenge and opportunity to further increase the probe utility. Cell pairs active close in time were spatially closer in most recordings, demonstrating the power to resolve complex cortical dynamics. Altogether, this approach provides access to population single-unit activity across the depth of human neocortex at scales previously only accessible in animal models.

Selective representations of texture and motion in mouse higher visual areas

The mouse visual cortex contains interconnected higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine the representations of complex visual stimuli by L2/3 neurons across mouse higher visual areas, measured using large-field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how receptive field models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.

Implicit mechanisms of intention

High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions. Here, we study the timing of human posterior parietal cortex single-neuron activity recorded from implanted microelectrode arrays relative to the explicit urge to initiate movement. Participants were free to choose when to move, whether to move, and what to move, and they retrospectively reported the time they felt the urge to move. We replicate prior studies by showing that posterior parietal cortex (PPC) neural activity sharply rises hundreds of milliseconds before the reported urge. However, we find that this "preconscious" activity is part of a dynamic neural population response that initiates much earlier, when the participant first chooses to perform the task. Together with details of neural timing, our results suggest that PPC encodes an internal model of the motor planning network that transforms high-level task objectives into appropriate motor behavior. These new data challenge traditional interpretations of early neural activity and offer a more holistic perspective on the interplay between choice, behavior, and their neural underpinnings. Our results have important implications for translating BMIs into more complex real-world environments. We find that early neural dynamics are sufficient to drive BMI movements before the participant intends to initiate movement. Appropriate algorithms ensure that BMI movements align with the subject's awareness of choice.

Calcium Imaging and Electrophysiology of hippocampal Activity under Anesthesia and natural Sleep in Mice

The acute effects of anesthesia and their underlying mechanisms are still not fully understood. Thus, comprehensive analysis and efficient generalization require their description in various brain regions. Here we describe a large-scale, annotated collection of 2-photon calcium imaging data and multi-electrode, extracellular electrophysiological recordings in CA1 of the murine hippocampus under three distinct anesthetics (Isoflurane, Ketamine/Xylazine and Medetomidine/Midazolam/Fentanyl), during natural sleep, and wakefulness. We cover several aspects of data quality standardization and provide a set of tools for autonomous validation, along with analysis workflows for reuse and data exploration. The datasets described here capture various aspects of neural activity in hundreds of pyramidal cells at single cell resolution. In addition to relevance for basic biological research, the dataset may find utility in computational neuroscience as a benchmark for models of anesthesia and sleep.

A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging

In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true for large field-of-view (FOV) imaging. Here, we present CITE-On (Cell Identification and Trace Extraction Online), a convolutional neural network-based algorithm for fast automatic cell identification, segmentation, identity tracking, and trace extraction in two-photon calcium imaging data. CITE-On processes thousands of cells online, including during mesoscopic two-photon imaging, and extracts functional measurements from most neurons in the FOV. Applied to publicly available datasets, the offline version of CITE-On achieves performance similar to that of state-of-the-art methods for offline analysis. Moreover, CITE-On generalizes across calcium indicators, brain regions, and acquisition parameters in anesthetized and awake head-fixed mice. CITE-On represents a powerful tool to speed up image analysis and facilitate closed-loop approaches, for example in combined all-optical imaging and manipulation experiments.

Representing the dynamics of high-dimensional data with non-redundant wavelets

A crucial question in data science is to extract meaningful information embedded in high-dimensional data into a low-dimensional set of features that can represent the original data at different levels. Wavelet analysis is a pervasive method for decomposing time-series signals into a few levels with detailed temporal resolution. However, obtained wavelets are intertwined and over-represented across levels for each sample and across different samples within one population. Here, using neuroscience data of simulated spikes, experimental spikes, calcium imaging signals, and human electrocorticography signals, we leveraged conditional mutual information between wavelets for feature selection. The meaningfulness of selected features was verified to decode stimulus or condition with high accuracy yet using only a small set of features. These results provide a new way of wavelet analysis for extracting essential features of the dynamics of spatiotemporal neural data, which then enables to support novel model design of machine learning with representative features.

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WCMI can extract meaningful information from high-dimensional data

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Extracted features from neural signals are non-redundant

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Simple decoders can read out these features with superb accuracy

One of the essential questions in data science is to extract meaningful information from high-dimensional data. A useful approach is to represent data using a few features that maintain the crucial information. The leading property of spatiotemporal data is foremost ever-changing dynamics in time. Wavelet analysis, as a classical method for disentangling time series, can capture temporal dynamics with detail. Here, we leveraged conditional mutual information between wavelets to select a small subset of non-redundant features. We demonstrated the efficiency and effectiveness of features using various types of neuroscience data with different sampling frequencies at the level of the single cell, cell population, and coarse-scale brain activity. Our results shed new insights into representing the dynamics of spatiotemporal data using a few fundamental features extracted by wavelet analysis, which may have wide implications to other types of data with rich temporal dynamics.

Differential modulation of parietal cortex activity by respiration and θ-oscillations

The simultaneous, local integration of information from widespread brain regions is an essential feature of cortical computation and particularly relevant for multimodal association areas such as the posterior parietal cortex. Slow, rhythmic fluctuations in the local field potentials (LFP) are assumed to constitute a global signal aiding interregional communication through the long-range synchronization of neuronal activity. Recent work demonstrated the brain-wide presence of a novel class of slow neuronal oscillations which are entrained by nasal respiration. However, whether there are differences in the influence of the respiration-entrained rhythm (RR) and the endogenous theta (θ) rhythm over local networks is unknown. In this work, we aimed at characterizing the impact of both classes of oscillations on neuronal activity in the posterior parietal cortex of mice. We focused our investigations on a θ-dominated state (REM sleep) and an RR-dominated state (wake immobility). Using linear silicon probes implanted along the dorsoventral cortical axis, we found that the LFP-depth distributions of both rhythms show differences in amplitude and coherence but no phase shift. Using tetrode recordings, we demonstrate that a substantial fraction of parietal neurons is modulated by either RR or θ or even by both rhythms simultaneously. Interestingly, the phase and cortical depth-dependence of spike-field coupling differ for these oscillations. We further show through intracellular recordings in urethane-anesthetized mice that synaptic inhibition is likely to play a role in generating respiration-entrainment at the membrane potential level. We conclude that θ and respiration differentially affect neuronal activity in the parietal cortex.

Assessing the Feasibility of Developing in vivo Neuroprobes for Parallel Intracellular Recording and Stimulation: A Perspective

Developing novel neuroprobes that enable parallel multisite, long-term intracellular recording and stimulation of neurons in freely behaving animals is a neuroscientist's dream. When fulfilled, it is expected to significantly enhance brain research at fundamental mechanistic levels including that of subthreshold signaling and computations. Here we assess the feasibility of merging the advantages of in vitro vertical nanopillar technologies that support intracellular recordings with contemporary concepts of in vivo extracellular field potential recordings to generate the dream neuroprobes that read the entire electrophysiological signaling repertoire.

Neurons learn by predicting future activity

Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.

Directed functional and structural connectivity in a large-scale model for the mouse cortex

Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas composed of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

We analyzed the relationship between structural and directed functional connectivity by evaluating the effectiveness of generalized partial directed coherence (GPDC) to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. We show that GPDC values provide a reasonable estimate of structural connectivity even in a typical electrophysiological recording scenario containing few areas. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

Large-scale neural recordings call for new insights to link brain and behavior

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

Ultrastructural Analysis of Neuroimplant-Parenchyma Interfaces Uncover Remarkable Neuroregeneration Along-With Barriers That Limit the Implant Electrophysiological Functions

Despite increasing use of in vivo multielectrode array (MEA) implants for basic research and medical applications, the critical structural interfaces formed between the implants and the brain parenchyma, remain elusive. Prevailing view assumes that formation of multicellular inflammatory encapsulating-scar around the implants [the foreign body response (FBR)] degrades the implant electrophysiological functions. Using gold mushroom shaped microelectrodes (gMμEs) based perforated polyimide MEA platforms (PPMPs) that in contrast to standard probes can be thin sectioned along with the interfacing parenchyma; we examined here for the first time the interfaces formed between brains parenchyma and implanted 3D vertical microelectrode platforms at the ultrastructural level. Our study demonstrates remarkable regenerative processes including neuritogenesis, axon myelination, synapse formation and capillaries regrowth in contact and around the implant. In parallel, we document that individual microglia adhere tightly and engulf the gMμEs. Modeling of the formed microglia-electrode junctions suggest that this configuration suffice to account for the low and deteriorating recording qualities of in vivo MEA implants. These observations help define the anticipated hurdles to adapting the advantageous 3D in vitro vertical-electrode technologies to in vivo settings, and suggest that improving the recording qualities and durability of planar or 3D in vivo electrode implants will require developing approaches to eliminate the insulating microglia junctions.