Publications by authors named "Shawn R Olsen"

Recent studies have found dramatic cell-type-specific responses to stimulus novelty, highlighting the importance of analyzing the cortical circuitry at this granularity to understand brain function. Although initial work characterized activity by cell type, the alterations in cortical circuitry due to interacting novelty effects remain unclear. We investigated circuit mechanisms underlying the observed neural dynamics in response to novel stimuli using a large-scale public dataset of electrophysiological recordings in behaving mice and a population network model.

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While visual responses to familiar and novel stimuli have been extensively studied, it is unknown how neuronal representations of familiar stimuli are affected when they are interleaved with novel images. We examined a large-scale dataset from mice performing a visual go/no-go change detection task. After training with eight images, six novel images were interleaved with two familiar ones.

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To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. Although new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe skull-shaped hemispheric implants enabling large-scale electrophysiology datasets (SHIELD).

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Detecting novelty is ethologically useful for an organism's survival. Recent experiments characterize how different types of novelty over timescales from seconds to weeks are reflected in the activity of excitatory and inhibitory neuron types. Here, we introduce a learning mechanism, familiarity-modulated synapses (FMSs), consisting of multiplicative modulations dependent on presynaptic or pre/postsynaptic neuron activity.

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Recent studies have found dramatic cell-type specific responses to stimulus novelty, highlighting the importance of analyzing the cortical circuitry at the cell-type specific level of granularity to understand brain function. Although initial work classified and characterized activity for each cell type, the specific alterations in cortical circuitry-particularly when multiple novelty effects interact-remain unclear. To address this gap, we employed a large-scale public dataset of electrophysiological recordings in the visual cortex of awake, behaving mice using Neuropixels probes and designed population network models to investigate the observed changes in neural dynamics in response to a combination of distinct forms of novelty.

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Visual masking can reveal the timescale of perception, but the underlying circuit mechanisms are not understood. Here we describe a backward masking task in mice and humans in which the location of a stimulus is potently masked. Humans report reduced subjective visibility that tracks behavioral deficits.

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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 ().

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Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular, and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types.

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Perturbational complexity analysis predicts the presence of consciousness in volunteers and patients by stimulating the brain with brief pulses, recording EEG responses, and computing their spatiotemporal complexity. We examined the underlying neural circuits in mice by directly stimulating cortex while recording with EEG and Neuropixels probes during wakefulness and isoflurane anesthesia. When mice are awake, stimulation of deep cortical layers reliably evokes locally a brief pulse of excitation, followed by a biphasic sequence of 120 ms profound period and a excitation.

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Multi-electrode arrays such as Neuropixels probes enable electrophysiological recordings from large populations of single neurons with high temporal resolution. By using such probes, the activity from functionally interacting, yet distinct, brain regions can be measured simultaneously by inserting multiple probes into the same subject. However, the use of multiple probes in small animals such as mice requires the removal of a sizable fraction of the skull, while also minimizing tissue damage and keeping the brain stable during the recordings.

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The claustrum is a small subcortical structure with widespread connections to disparate regions of the cortex. However, the impact of the claustrum on cortical activity is not fully understood, particularly beyond frontal areas. Here, using optogenetics and multi-regional Neuropixels recordings from over 15,000 cortical neurons in awake mice, we demonstrate that the effect of claustrum input to the cortex differs depending on brain area, layer, and cell type.

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We propose centralized brain observatories for large-scale recordings of neural activity in mice and non-human primates coupled with cloud-based data analysis and sharing. Such observatories will advance reproducible systems neuroscience and democratize access to the most advanced tools and data.

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The visual cortex is hierarchically organized, yet the presence of extensive recurrent and parallel pathways make it challenging to decipher how signals flow between neuronal populations. Here, we tracked the flow of spiking activity recorded from six interconnected levels of the mouse visual hierarchy. By analyzing leading and lagging spike-timing relationships among pairs of simultaneously recorded neurons, we created a cellular-scale directed network graph.

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The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task.

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Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes.

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The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli.

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To study the mechanisms of perception and cognition, neural measurements must be made during behavior. A goal of the is to map the activity of distinct cortical cell classes underlying visual and behavioral processing. Here we describe standardized methodology for training head-fixed mice on a visual change detection task, and we use our paradigm to characterize learning and behavior of five GCaMP6-expressing transgenic lines.

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Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic simulation of the awake mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically detailed or point neurons.

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Cortical circuits can flexibly change with experience and learning, but the effects on specific cell types, including distinct inhibitory types, are not well understood. Here we investigated how excitatory and VIP inhibitory cells in layer 2/3 of mouse visual cortex were impacted by visual experience in the context of a behavioral task. Mice learned a visual change detection task with a set of eight natural scene images.

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Higher-order thalamic nuclei, such as the visual pulvinar, play essential roles in cortical function by connecting functionally related cortical and subcortical brain regions. A coherent framework describing pulvinar function remains elusive because of its anatomical complexity and involvement in diverse cognitive processes. We combined large-scale anatomical circuit mapping with high-density electrophysiological recordings to dissect a homolog of the pulvinar in mice, the lateral posterior thalamic nucleus (LP).

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Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morphoelectrical properties of neurons measured on spatially dense electrode arrays have the potential to distinguish neuron types.

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Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments.

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Visually guided perceptual decisions involve the sequential activation of a hierarchy of cortical areas. It has been hypothesized that a brief time window of activity in each area is sufficient to enable the decision but direct measurements of this time window are lacking. To address this question, we develop a visual discrimination task in mice that depends on visual cortex and in which we precisely control the time window of visual cortical activity as the animal performs the task at different levels of difficulty.

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Mammalian visual behaviors, as well as responses in the neural systems underlying these behaviors, are driven by luminance and color contrast. With constantly improving tools for measuring activity in cell-type-specific populations in the mouse during visual behavior, it is important to define the extent of luminance and color information that is behaviorally accessible to the mouse. A non-uniform distribution of cone opsins in the mouse retina potentially complicates both luminance and color sensitivity; opposing gradients of short (UV-shifted) and middle (blue/green) cone opsins suggest that color discrimination and wavelength-specific luminance contrast sensitivity may differ with retinotopic location.

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Almost all areas of the neocortex are connected with the claustrum, a nucleus located between the neocortex and the striatum, yet the functions of corticoclaustral and claustrocortical connections remain largely obscure. As major efforts to model the neocortex are currently underway, it has become increasingly important to incorporate the corticoclaustral system into theories of cortical function. This Mini-Symposium was motivated by a series of recent studies which have sparked new hypotheses regarding the function of claustral circuits.

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