Biology subjects

Bair, W.

Publications and source records attributed to Bair, W..

7 recordsLinked to original sources

bioRxiv2025

Functional clusters for shape, texture, and motion encoding in macaque V2

Macaque primary visual cortex (V1) exhibits exquisite columnar organization, while midlevel area V4 does not. Here we investigated the functional organization and representational bases of intervening area V2 with high-density Neuropixels recordings and a variety of visual stimuli--shape, texture, drifting grating, and translational motion patches. We observed dense clusters of similarly tuned neurons often spanning [~]500 {micro}m for shape and motion stimuli, and larger for texture stimuli, consistent with a columnar structure. In terms of representational bases, V2 responses were largely explained by stimulus features based on local image statistics: shape tuning is well-modeled by a linear combination of orientation filters, and direction selectivity is stronger with surface compared to object motion, in striking contrast to V4. Overall, our results support the progression from columns to sparse clusters as neuronal representations transform from encoding local features and feature conjunctions in V1/V2 to a high-dimensional object-based code in V4. Significance StatementBy recording hundreds of neurons simultaneously across layers of macaque visual area V2, we show the first evidence of exquisite fine-scale functional clusters that encode higher-order shape, texture, and motion features, extending well beyond the classic orientation-selective columns seen in area V1. Comparative analyses with V4 further reveal distinct representational bases and organization patterns between adjacent cortical areas, offering insights into how columnar organization is preserved in early visual areas (V1 and V2) but markedly attenuated in higher-order cortex such as V4.

neuroscience↗

bioRxiv2025

Multimodal optical imaging and modulation through Smart Dura in non-human primates

Multimodal neural interfaces that integrate electrical and optical functionalities are promising tools for neuroscientific and clinical applications that involve recording and manipulating neuronal activity. However, most technologies for multimodal implementation are largely restricted to small animal models and lack the ability to translate to the larger brains of non-human primates (NHPs). Smart Dura, a recently developed large-scale neural interface for NHPs, enables high-density electrophysiological recordings and broad optical accessibility, providing multiscale information with enhanced spatiotemporal resolution. In this paper, the multimodal capabilities of Smart Dura are demonstrated through integration with multiphoton imaging, optical coherence tomography angiography (OCTA), and intrinsic signal optical imaging (ISOI), as well as optical manipulations such as photothrombotic lesioning and optogenetics. Through the transparent Smart Dura, in vivo fluorescence vascular imaging is achieved down to depths of 200 and 550 m using two-photon and three-photon microscopy, respectively. When combined with simultaneous electrophysiology, Smart Dura also enables assessment of vascular and neural dynamics via OCTA and ISOI, the induction of ischemic stroke, and the application of optogenetic neuromodulation across a wide cortical area of 20 mm in diameter. These capabilities support comprehensive investigations of brain dynamics in NHPs, advancing translational neurotechnology for human applications.

neuroscience↗

bioRxiv2025

Smart Dura: a functional artificial dura for multimodal neural recording and modulation

A multi-modal neural interface capable of long-term recording and stimulation is essential for advancing brain monitoring and developing targeted therapeutics. Among the traditional electrophysiological methods, micro-electrocorticography (ECoG) is appealing for chronic applications because it provides a good compromise between invasiveness and high-resolution neural recording. When combining ECoG with optical technologies, such as calcium imaging and optogenetics, this multi-modal approach enables the simultaneous collection of neural activity from individual neurons and the ability to perform cell-specific manipulation. While previous efforts have focused on multi-modal interfaces for small animal models, scaling these technologies to larger brains, of primates, remains challenging. In this paper, we present a multi-modal neural interface, named Smart Dura, a functional version of the commonly used artificial dura with integrated electrophysiological electrodes for large cortical area coverage for the NHP brain. The Smart Dura is fabricated using a thin-film microfabrication process to monolithically integrate a micron-scale electrode array into a soft, flexible, and transparent substrate with high-density electrodes (up to 256 electrodes) while providing matched mechanical compliance with the native tissue and achieving high optical transparency (exceeding 97%). Our in vivo experiments demonstrate electrophysiological recording capabilities combined with neuromodulation, as well as optical transparency via multiphoton imaging. This work paves the way toward a chronic neural interface that can provide large-scale, bidirectional interfacing for multimodal and closed-loop neuromodulation capabilities to study cortical brain activity in non-human primates, with the potential for translation to humans.

neuroscience↗

bioRxiv2023

Microscale visualization of cellular features in adult macaque visual cortex

Expansion microscopy and light sheet imaging enable fine-scale resolution of intracellular features that comprise neural circuits. Most current techniques visualize sparsely distributed features across whole brains or densely distributed features within individual brain regions. Here, we visualize dense distributions of immunolabeled proteins across early visual cortical areas in adult macaque monkeys. This process may be combined with multiphoton or magnetic resonance imaging to produce multimodal atlases in large, gyrencephalic brains.

neuroscience↗

bioRxiv2023

Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density ([~]10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neurons electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to [~]3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

neuroscience↗

bioRxiv2020

The unbiased estimation of the fraction of variance explained by a model

The correlation coefficient squared, r2, is often used to validate quantitative models on neural data. Yet it is biased by trial-to-trial variability: as trial-to-trial variability increases, measured correlation to a models predictions decreases; therefore, models that perfectly explain neural tuning can appear to perform poorly. Many solutions to this problem have been proposed, but some prior methods overestimate model fit, the utility of even the best performing methods is limited by the lack of confidence intervals and asymptotic analysis, and no consensus has been reached on which is the least biased estimator. We provide a new estimator, [Formula], that outperforms all prior estimators in our testing, and we provide confidence intervals and asymptotic guarantees. We apply our estimator to a variety of neural data to validate its utility. We find that neural noise is often so great that confidence intervals of the estimator cover the entire possible range of values ([0, 1]), preventing meaningful evaluation of the quality of a models predictions. We demonstrate the use of the signal-to-noise ratio (SNR) as a quality metric for making quantitative comparisons across neural recordings. Analyzing a variety of neural data sets, we find ~ 40% or less of some neural recordings do not pass even a liberal SNR criterion. Author SummaryQuantifying the similarity between a model and noisy data is fundamental to the verification of advances in scientific understanding of biological phenomena, and it is particularly relevant to modeling neuronal responses. A ubiquitous metric of similarity is the correlation coefficient. Here we point out how the correlation coefficient depends on a variety of factors that are irrelevant to the similarity between a model and data. While neuroscientists have recognized this problem and proposed corrected methods, no consensus has been reached as to which are effective. Prior methods have wide variation in their precision, and even the most successful methods lack confidence intervals, leaving uncertainty about the reliability of any particular estimate. We address these issues by developing a new estimator along with an associated confidence interval that outperforms all prior methods.

neuroscience↗

bioRxiv2019

Artiphysiology reveals visual preferences underlying V4-like blur selectivity in a deep convolutional neural network

Blurry visual scenes arise from many causes and image blur is known to be important for scene interpretation, yet there have been very few studies of the visual encoding of blur in biological visual systems or in artificial visual systems trained for object recognition. Recently, a study of single neurons in the visual cortex of macaque monkeys found that a significant fraction of neurons were more responsive to blurred visual stimuli than to sharply defined stimuli. This raises two questions: (1) what types of visual features in natural scenes might underlie blur selectivity in macaque cortex, and (2) can blur selectivity be found in artificial neural networks using the simple artificial stimuli previously applied in vivo? To answer these questions, we presented simple shape stimuli to the widely studied deep convolutional neural network (CNN) known as AlexNet and used deconv-net visualization to identify features that are critical for driving blur selective units. We found that a substantial number of units in the CNN were selective for blur and that several categories of blur selectivity emerged in early-to-middle processing stages. Prominent among these is a set of units selective for spatial boundaries defined by blur contrast. These blur-contrast boundary units may serve the important task of segmentation in natural photographic images and may relate to a large body of literature on second-order boundary detection. Our results lead to the prediction that such units could exist in the visual cortex but have yet to be well characterized and localized, and they provide direction for future neurophysiological tests of blur selectivity in vivo.

neuroscience↗