Biology subjects

Panzeri, S.

Publications and source records attributed to Panzeri, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

ASTRA: a deep learning algorithm for fast semantic segmentation of large-scale astrocytic networks

Changes in the intracellular calcium concentration are a fundamental fingerprint of astrocytes, the main type of glial cell. Astrocyte calcium signals can be measured with two-photon microscopy, occur in anatomically restricted subcellular regions, and are coordinated across astrocytic networks. However, current analytical tools to identify the astrocytic subcellular regions where calcium signals occur are time-consuming and extensively rely on user-defined parameters. These limitations limit reproducibility and prevent scalability to large datasets and fields-of-view. Here, we present Astrocytic calcium Spatio-Temporal Rapid Analysis (ASTRA), a novel software combining deep learning with image feature engineering for fast and fully automated semantic segmentation of two-photon calcium imaging recordings of astrocytes. We applied ASTRA to several two-photon microscopy datasets and found that ASTRA performed rapid detection and segmentation of astrocytic cell somata and processes with performance close to that of human experts, outperformed state-of-the-art algorithms for the analysis of astrocytic and neuronal calcium data, and generalized across indicators and acquisition parameters. We also applied ASTRA to the first report of two-photon mesoscopic imaging of hundreds of astrocytes in awake mice, documenting large-scale redundant and synergistic interactions in extended astrocytic networks. ASTRA is a powerful tool enabling closed-loop and large-scale reproducible investigation of astrocytic morphology and function.

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bioRxiv2022

NIT: an open-source tool for information theoretic analysis of neural population data

Information theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To facilitate its application, here we present Neuroscience Information Toolbox (NIT), a new toolbox for the accurate information theoretical analysis of neural data. NIT contains widely used tools such as limited sampling bias corrections and discretization of neural probabilities for the calculation of stimulus coding in low-dimensional representation of neural activity (e.g. Local Field Potentials or the activity of small neural population).Importantly, it adds a range of recent tools for quantifying information encoding by large populations of neurons or brain areas, for the directed transmission of information between neurons or areas, and for the calculation of Partial Information Decompositions to quantify the behavioral relevance of neural information and the synergy and redundancy among neurons and brain areas. Further, because information theoretic algorithms have been previously validated mainly with electrophysiological recordings, here we used realistic simulations and analysis of real data to study how to optimally apply information theory to the analysis of two-photon calcium imaging data, which are particularly challenging due to their lower signal-to-noise and temporal resolution. We also included algorithms (based on parametric and non-parametric copulas) to compute robustly information specifically with analog signals such as calcium traces. We provide indications on how to best process calcium imaging traces and to apply NIT depending on the type of calcium indicator, imaging frame rate and firing rate levels. In sum, NIT provides a toolbox for the comprehensive and effective information theoretic analysis of all kinds of neural data, including calcium imaging.

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bioRxiv2022

Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning

Cortical neurons in primary sensory cortex carry not only sensory but also behavior-related information. However, it remains unclear how these types of information emerge and are integrated with one another over learning and what the relative contribution of activity in individual cells versus neuronal populations is in this process. Current evidence supports two opposing views of learning-related changes: 1) sensory information increases in primary cortex or 2) sensory information remains stable in primary cortex but its readout efficiency in association cortices increases. Here, we investigate these questions in primary sensory cortex during learning of a sensory task. Over the course of weeks, we imaged neuronal activity at different depths within layers 2 and 3 of the mouse vibrissal primary somatosensory cortex (vS1) before, during, and after training on a whisker-based object-localization task. We leveraged information theoretical analysis to quantify stimulus and behavior-related information in vS1 and estimate how much neural activity encoding sensory information is used to inform perceptual choices as sensory learning progresses. We also quantified the extent to which these types of information are supported by an individual neuron or population code. We found that, while sensory information rises progressively from the start of training, choice information is only present in the final stages of learning and is increasingly supported by a population code. Moreover, we demonstrate that not only the increase in available information, but also a more efficient readout of such information in primary sensory cortex mediate sensory learning. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

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bioRxiv2022

Population coding strategies in human tactile afferents

Sensory information is conveyed by populations of neurons, and coding strategies cannot always be deduced when considering individual neurons. Moreover, information coding depends on the number of neurons available and on the composition of the population when multiple classes with different response properties are available. Here, we study population coding in human tactile afferents by employing a recently developed simulator of mechanoreceptor firing activity. First, we demonstrate that the optimal afferent density for conveying maximal information depends on the tactile feature under consideration and the afferent class coding this feature. Second, we find that information is spread across different classes for all tactile features, such that combining information from multiple afferent classes improves information transmission, and is often more efficient than increasing the density of afferents from the same class. Finally, we test the importance of timing precision and afferent identity in the population code to probe whether temporal and spatial information can be traded against each other. Destroying temporal information turns out to be more destructive than removing spatial information, and the contribution of either cannot be completely recovered from the other. Overall, our results suggest that both optimal afferent innervation densities and the composition of the population depend in complex ways on the tactile features in question, potentially accounting for the variety in which tactile peripheral populations are assembled in different regions across the body.

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bioRxiv2022

A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

Resting state fMRI activity in mammals exhibits rich dynamics on a fast, frame-by-frame timescale of seconds, including the robust emergence of recurring fMRI co-activation patterns (CAPs). To understand how such dynamics emerges from the underlying anatomical cortico-cortical connectivity, we developed a whole-cortex model of resting-state fMRI activity in the mouse. Our model implemented neural input-output nonlinearities and excitatory-inhibitory interactions within cortical regions, as well as directed anatomical connectivity between regions inferred from the Allen mouse brain atlas. We found that, even if the model parameters were fitted to explain static properties of fMRI activity on the timescale of minutes, the model generated rich frame-by-frame attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we found that empirical mouse fMRI time series exhibited analogous signatures of attractor dynamics, and that model attractors recapitulated the topographical organization of empirical fMRI CAPs. The model established key relationships between attractor dynamics, CAPs and features of the directed cortico-cortical intra- and inter-hemispheric anatomical connectivity. Specifically, we found that neglecting fiber directionality severely affected the number of models attractors and their ability to explain CAPs. Furthermore, modifying inter-hemispheric anatomical connectivity strength by decreasing or increasing it from the value of real mouse anatomical data, resulted in fewer attractors generated by cortico-cortical interactions and reduced non-homotopic features of the attractors generated by the model, which were important for better predicting empirical CAPs. These results offer novel theoretical insight into the dynamic organization of resting state fMRI in the mouse brain and suggest that the frame-wise BOLD activity captured by CAPs reflects an emerging property of cortical dynamics resulting from directed cortico-cortical interactions. Author summaryWhole-brain activity at rest transitions on a timescale of seconds between stereotyped co-activation patterns (CAPs), each with a distinct spatial profile of co-activation across different brain regions. CAPs have been robustly reported across datasets and mammalian species, including humans and mice. However, the significance and origin of these patterns remain unknown. Here we studied the origin of CAPs using a computational model of the whole cortex based on real-world directed measurements of mouse cortical anatomical connectivity. We found that we could explain the formation and topography of CAPs in terms of attractors (that is, states the network tends to converge to) that reflect the information in the anatomical connections between cortical regions. Attractors and CAPs are significantly influenced by the directionality of connectivity (available in mice but not in humans) and the strength of inter-hemispheric coupling. Together, our findings provide a novel possible explanation of the mechanisms of CAP generation which relates to cortico-cortical connectivity, and suggest that CAPs are at least in part an emergent property of cortico-cortical interactions which can be generated also in absence of the action of arousing modulatory nuclei.

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bioRxiv2022

Synaptic wiring motifs in posterior parietal cortex support decision-making

The posterior parietal cortex (PPC) exhibits choice-selective activity during perceptual decision-making tasks. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here, we combined virtual reality behavior, two-photon calcium imaging, high throughput electron microscopy, and circuit modeling to analyze how synaptic connectivity between neurons in PPC relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. Using circuit models, we show that opponent inhibition amplifies selective inputs and induces competition between neural populations with opposite selectivity, thereby improving the encoding of trial-type information. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

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bioRxiv2022

A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions

Decision-making requires flexibility to rapidly switch sensorimotor associations depending on behavioral goals stored in memory. We identified cortical areas and neural activity patterns that mediate this flexibility during virtual-navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. An optogenetics screen identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid sensorimotor switching by encoding a conjunction of a current and remembered visual cue that predicted the mouses navigational choice from trial-to-trial. Their activity formed efficient population codes before correct, but not incorrect, choices. These neurons were distributed across posterior cortex, even V1, but were densest in RSC and sparsest in PPC. We propose the flexibility of navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network, centered in RSC.

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bioRxiv2022

Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex

Recent studies using intracellular recordings in awake behaving mice revealed that cortical network states, defined based on membrane potential features, modulate sensory responses and perceptual outcomes. Single cell intracellular recordings are difficult to achieve and have low yield compared to extracellular recordings of population signals, such as local field potentials (LFPs). However, it is currently unclear how to identify these behaviorally-relevant network states from the LFP. We used simultaneous LFP and intracellular recordings in the somatosensory cortex of awake mice to design and calibrate a model-based analysis method, the Network State Index (NSI), that enables network state classification from the LFP. We used the NSI to analyze the relationship between single-cell (intracellular) and population (LFP) signals over different network states of wakefulness. We found that graded levels of population signal faithfully predicted the levels of single cell depolarization in non-rhythmic regimes whereas, in delta ([2-4 Hz]) oscillatory regimes, the graded levels of rhythmicity in the LFP mapped into a stereotypical oscillatory pattern of membrane potential. Finally, we showed that the variability of network states, beyond the occurrence of slow oscillatory activity, critically shaped the average correlations between single cell and population signals. NSI-based characterization provides a ready-to-use tool to understand from LFP recordings how the modulation of local network dynamics shapes the flexibility of sensory processing during behavior. Significance statementSensation during behaviour is strongly modulated by the animals internal state. Such context-dependent modulation of sensory processing is believed to largely stem from top-down control of network states in sensory cortices, with different network states being associated with distinct computational properties of the circuit. So far, a detailed characterization of network states in the awake cortex has mostly been achieved through single-cell intracellular recordings, which however cannot be easily recorded. Here, we developed a new method to classify network states from the easily accessible extracellular LFP recordings of population activity. Given the widespread use of LFPs, our work provides a critical methodology to greatly expand our understanding of the mechanisms underlying state-dependent computations in neocortex.

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bioRxiv2021

Sequential Transmission of Task-Relevant Information in Cortical Neuronal Networks

During auditory task performance, cortical processing of task-relevant information enables mammals to recognize sensory input and flexibly select behavioral responses. In mouse auditory cortex, small neuronal networks encode behavioral choice during a pure-tone detection task, but it is poorly understood how neuronal networks encode behavioral choice during a pure-tone discrimination task where tones have to be categorized into targets and non-targets. While the interactions between networked neurons are thought to encode behavioral choice, it remains unclear how patterns of neuronal network activity indicate the transmission of task-relevant information within the network. To this end, we trained mice to behaviorally discriminate target vs. non-target pure-tones while we used in vivo 2-photon imaging to record neuronal population activity in primary auditory cortex layer 2/3. We found that during task performance, a specialized subset of neurons transiently encoded intersection information, i.e., sensory information that was used to inform behavioral choice. Granger causality analysis showed that these neurons formed functional networks in which task-relevant information was transmitted sequentially between neurons. Differences in network structure between target and non-target sounds encoded behavioral choice. Correct behavioral choices were associated with shorter timescale communication between neurons. In summary, we find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both sensory input and behavioral choice.

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bioRxiv2021

Unique spatiotemporal fMRI dynamics in the awake mouse brain

Human imaging studies have shown that spontaneous brain activity exhibits stereotypic spatiotemporal reorganization in awake, conscious conditions with respect to minimally conscious states. However, whether and how this phenomenon can be generalized to lower mammalian species, remains unclear. Leveraging a robust protocol for resting-state fMRI (rsfMRI) mapping in non-anesthetized, head-fixed mice, we investigated functional network topography and dynamic structure of spontaneous brain activity in wakeful animals. We found that rsfMRI networks in the awake state, while anatomically comparable to those observed under anesthesia, are topologically configured to maximize interregional communication, departing from the underlying community structure of the mouse axonal connectome. We further report that rsfMRI activity in wakeful animals exhibits unique spatiotemporal dynamics characterized by a state-dependent, dominant occurrence of coactivation patterns encompassing a prominent participation of arousal-related forebrain nuclei, and functional anti-coordination between visual-auditory and polymodal cortical areas. We finally show that rsfMRI dynamics in awake mice exhibits a stereotypical temporal structure, in which state-dominant coactivation patterns are configured as network attractors. These findings suggest that spontaneous brain activity in awake mice is critically shaped by state-specific involvement of basal forebrain arousal systems, and document that its dynamic structure recapitulates distinctive, evolutionarily-relevant principles that are predictive of conscious states in higher mammalian species.

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bioRxiv2021

Glial place cells: complementary encoding of spatial information in hippocampal astrocytes

Calcium dynamics into astrocytes influence the activity of nearby neuronal structures. However, because previous reports show that astrocytic calcium signals largely mirror neighboring neuronal activity, current information coding models neglect astrocytes. Using simultaneous two-photon calcium imaging of astrocytes and neurons in the hippocampus of mice navigating a virtual environment, we demonstrate that astrocytic calcium signals actively encode spatial information. Calcium events carrying spatial information occurred in topographically organized astrocytic subregions. Importantly, astrocytes encoded spatial information that was complementary and synergistic to that carried by neurons, improving spatial position decoding when astrocytic signals were considered alongside neuronal ones. These results suggest that the complementary place-dependence of localized astrocytic calcium signals regulates clusters of nearby synapses, enabling dynamic, context-dependent, variations in population coding within brain circuits.

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bioRxiv2020

Computation of the electroencephalogram (EEG) from network models of point neurons

The electroencephalogram (EEG) is one of the main tools for non-invasively studying brain function and dysfunction. To better interpret EEGs in terms of neural mechanisms, it is important to compare experimentally recorded EEGs with the output of neural network models. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neuron networks cannot directly generate an EEG, since EEGs are generated by spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of the EEG with a combination of quantities defined in point-neuron network models. We constructed several different candidate approximations (or proxies) of the EEG that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and specific combinations of synaptic currents. We then evaluated how well each proxy reconstructed a realistic ground-truth EEG obtained when the synaptic input currents of the LIF network were fed into a three-dimensional (3D) network model of multi-compartmental neurons with realistic cell morphologies. We found that a new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states of the LIF point-neuron network. The new linear proxies explained most of the variance (85-95%) of the ground-truth EEG for a wide range of cell morphologies, distributions of presynaptic inputs, and position of the recording electrode. Non-linear proxies, obtained using a convolutional neural network (CNN) to predict the EEG from synaptic currents, increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations and thereby allow a quantitative comparison between computational models and experimental EEG recordings. Author summaryNetworks of point neurons are widely used to model neural dynamics. Their output, however, cannot be directly compared to the electroencephalogram (EEG), which is one of the most used tools to non-invasively measure brain activity. To allow a direct integration between neural network theory and empirical EEG data, here we derived a new mathematical expression, termed EEG proxy, which estimates with high accuracy the EEG based simply on the variables available from simulations of point-neuron network models. To compare and validate these EEG proxies, we computed a realistic ground-truth EEG produced by a network of simulated neurons with realistic 3D morphologies that receive the same spikes of the simpler network of point neurons. The new obtained EEG proxies outperformed previous approaches and worked well under a wide range of simulated configurations of cell morphologies, distribution of presynaptic inputs, and position of the recording electrode. The new proxies approximated well both EEG spectra and EEG evoked potentials. Our work provides important mathematical tools that allow a better interpretation of experimentally measured EEGs in terms of neural models of brain function.

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bioRxiv2020

Cortical silencing results in paradoxical fMRI overconnectivity

fMRI-based measurements of functional connectivity are commonly interpreted as an index of anatomical coupling and direct interareal communication. However, causal testing of this hypothesis has been lacking. Here we combine neural silencing, resting-state fMRI and in vivo electrophysiology to causally probe how inactivation of a cortical region affects brain-wide functional coupling. We find that chronic silencing of the prefrontal cortex (PFC) via overexpression of a potassium channel paradoxically increases rsfMRI connectivity between the silenced area and its thalamo-cortical terminals. Acute chemogenetic silencing of the PFC reproduces analogous patterns of overconnectivity, an effect associated with over-synchronous fMRI coupling between polymodal thalamic regions and widespread cortical districts. Notably, multielectrode recordings revealed that chemogenetic inactivation of the PFC attenuates gamma activity and increases delta power in the silenced area, resulting in robustly increased delta band coherence between functionally overconnected regions. The observation of enhanced rsfMRI coupling between chemogenetically silenced areas challenges prevailing interpretations of functional connectivity as a monotonic index of direct axonal communication, and points at a critical contribution of slow rhythm generators to the establishment of brain-wide functional coupling.

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bioRxiv2020

Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention

Achieving behavioral goals requires integration of sensory and cognitive information, across cortical laminae and cortical regions. How this computation is performed remains unknown. Using local field potential recordings and spectrally resolved conditional Granger causality (cGC) analysis, we mapped visual information flow, and its attentional modulation, between cortical layers within and between macaque areas V1 and V4. Stimulus induced inter-laminar information flow within V1 dominated upwardly, channeling information towards supragranular cortico-cortical output layers. Within V4, information flow dominated from granular to supragranular layers, but interactions between supragranular and infragranular layers dominated downwardly. Low-frequency across-area communication was stronger from V4 to V1, with little layer specificity. Gamma-band communication was stronger in the feedforward V1 to V4 direction. Attention to the receptive field of V1 decreased communication between all V1 layers, except for granular to supragranular layers interactions. Communication within V4, and from V1 to V4, increased with attention across all frequencies. While communication from V4 to V1 was stronger in lower frequency bands (4-25 Hz), attention modulated cGCs from V4 to V1 across all investigated frequencies. Our data show that top down cognitive processes result in reduced communication within cortical areas, increased feedforward communication across all frequency bands and increased gamma band feedback communication.

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bioRxiv2020

Extended field-of-view ultrathin microendoscopes for high-resolution two-photon imaging with minimal invasiveness in awake mice

Imaging neuronal activity with high and homogeneous spatial resolution across the field-of-view (FOV) and limited invasiveness in deep brain regions is fundamental for the progress of neuroscience, yet is a major technical challenge. We achieved this goal by correcting optical aberrations in gradient index lens-based ultrathin ([≤] 500 {micro}m) microendoscopes using aspheric microlenses generated through 3D-microprinting. Corrected microendoscopes had extended FOV (eFOV) with homogeneous spatial resolution for two-photon fluorescence imaging and required no modification of the optical set-up. Synthetic calcium imaging data showed that, compared to uncorrected endoscopes, eFOV-microendoscopes led to improved signal-to-noise ratio and more precise evaluation of correlated neuronal activity. We experimentally validated these predictions in awake head-fixed mice. Moreover, using eFOV-microendoscopes we demonstrated cell-specific encoding of behavioral state-dependent information in distributed functional subnetworks in a primary somatosensory thalamic nucleus. eFOV-microendoscopes are, therefore, small-cross-section ready-to-use tools for deep two-photon functional imaging with unprecedentedly high and homogeneous spatial resolution.

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bioRxiv2020

Transient disruption of the inferior parietal lobule impairs the ability to attribute intention to action

Although it is well established that fronto-parietal regions are active during action observation, whether they play a causal role in the ability to infer others intentions from visual kinematics remains undetermined. In experiments reported here, we combined offline continuous theta-burst stimulation (cTBS) with computational modeling to reveal single-trial computations in the inferior parietal lobule (IPL) and inferior frontal gyrus (IFG). Participants received cTBS over the left anterior IPL and the left IFG pars orbitalis, in separate sessions, before completing an intention discrimination task (discriminate intention of observed reach-to-grasp acts) or a kinematic discrimination task (discriminate peak wrist height of the same acts) unrelated to intention. We targeted intentions-sensitive regions whose fMRI-measured activity accurately discriminated intention from the same action stimuli. We found that transient disruption of activity of the left IPL, but not the IFG, impaired the observers ability to judge intention from movement kinematics. Kinematic discrimination unrelated to intention, in contrast, was largely unaffected. Computational analyses revealed that IPL cTBS did not impair the ability to see changes in movement kinematics, nor did it alter the weight given to informative versus non-informative kinematic features. Rather, it selectively impaired the ability to link variations in informative features to the correct intention. These results provide the first causal evidence that left anterior IPL maps kinematics to intentions.

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bioRxiv2020

Correlations enhance the behavioral readout of neural population activity in association cortex

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on "noise" correlations - trial-to-trial covariations in neural activity for a given stimulus - as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a populations total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouses behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

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bioRxiv2020

Noradrenergic locus coeruleus ensembles evoke different states in rat prefrontal cortex

The noradrenergic locus coeruleus (LC) is a crucial controller of brain and behavioral states. Activating LC neurons synchronously en masse by electrical or optogenetic stimulation promotes a stereotypical "activated" high-frequency cortical state. However, it has been recently reported that spontaneous LC cell-pairs have sparse yet structured time-averaged cross-correlations, which is unlike the high synchrony of en masse neuronal stimulation. This suggests the untested possibility that LC population activity may be made of distinct multi-cell ensembles each with unique temporal evolution of activity. We used non-negative matrix factorization (NMF) to analyze large populations of LC single units simultaneously recorded in the rat LC. Synthetic spike train simulations showed that NMF, unlike the traditional time-averaged pairwise correlations, detects both the precise neuronal composition and the activation time courses of each ensemble. NMF identified the existence of robust ensembles of spontaneously co-active LC neurons. Since LC neurons selectively project to specific forebrain regions, we hypothesized that individual LC ensembles produce different cortical states. To test this hypothesis, we triggered local field potentials (LFP) in cortical area 24a on the activation of distinct LC ensembles. We found four cortical states, each with different spectro-temporal LFP characteristics, that were robust across sessions and animals. While some LC ensembles triggered the activated state, others were associated with a beta oscillation-specific state or a reduced high frequency oscillation state. Thus - in contrast to the stereotypical "activated" brain state evoked by en masse LC stimulation - spontaneous activation of distinct LC ensembles can control a multitude of cortical states.

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