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During wakefulness, cortical networks exhibit diverse states of activity arising from fluctuations in modulatory inputs, any of which may shape the recorded neural activity across a range of scales, from local field potential (LFP) to the spiking of individual neurons. Multiple studies have uncovered state-dependent changes in single neuron activity specific to cell type. These changes are heterogeneous across cell types, suggesting that there is a link between changes in states of wakefulness and the dynamic patterns of spiking activity across the population, yet this is currently not well understood. Here, we use an unsupervised statistical modeling framework to link fluctuations in the state of the LFP to the dynamics of spiking activity of populations of neurons in the whisker area of primary somatosensory cortex of awake male mice. By identifying latent states from population spiking activity, we show that these spiking states are more informative of states of the LFP than a direct readout of the spiking activity of single neurons or of the population. Further, we find that the latent spiking states predict trial-by-trial variability in sensory responses as well as one aspect of behavior, whisking activity. Taken together, our approach captures the latent variables that underlie the dynamics of the spiking activity of cortical neuronal populations, providing a framework to relate cortical activity across different scales.Significance statementDuring wakefulness, variability in cortical activity can arise from shifts in arousal or behavior. We analyze the dynamics of two measures of cortical activity, the spiking activity of local populations of neurons and the local field potential (LFP), typically used to track overall levels of arousal. We show that the latent states that capture variability in population spiking activity are highly predictive of LFP state changes, even more so than spiking activity directly. By linking latent states of spiking activity to changes in the LFP, our novel approach bridges fine-scale microcircuits ensemble activity and the meso-scale LFP. Our latent-state framework opens new avenues for extracting information from the LFP as well as identification of subject-level sources of variability.

Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse graining had been previously applied to experimental neural recordings, which showed over two decades of apparent scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model such data by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally observed scalings, suggesting that they do not require fine-tuning of internal parameters, but will arise in any system, biological or not, where activity variables are coupled to latent dynamic stimuli. Parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to nonplace stimuli.

To compensate for sensory processing delays, the visual system must make predictions to ensure timely and appropriate behaviors. Recent work has found predictive information about the stimulus in neural populations early in vision processing, starting in the retina. However, to utilize this information, cells downstream must in turn be able to read out the predictive information from the spiking activity of retinal ganglion cells. Here we investigate whether a downstream cell could learn efficient encoding of predictive information in its inputs in the absence of other instructive signals, from the correlations in the inputs themselves. We simulate learning driven by spiking activity recorded in salamander retina. We model a downstream cell as a binary neuron receiving a small group of weighted inputs and quantify the predictive information between activity in the binary neuron and future input. Input weights change according to spike timing-dependent learning rules during a training period. We characterize the readouts learned under spike timing-dependent learning rules, finding that although the fixed points of learning dynamics are not associated with absolute optimal readouts, they convey nearly all the information conveyed by the optimal readout. Moreover, we find that learned perceptrons transmit position and velocity information of a moving bar stimulus nearly as efficiently as optimal perceptrons. We conclude that predictive information is, in principle, readable from the perspective of downstream neurons in the absence of other inputs, and consequently suggests that bottom-up prediction may play an important role in sensory processing., 36 pages, 5 figures, 3 supplemental figures

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states., Author summary Establishing the link between neural activity and behavior is a central goal of neuroscience. One context in which to examine this link is in a sensory detection task, in which an animal is trained to report the presence of a barely perceptible sensory stimulus. In such tasks, both sensory responses in the brain and behavioral responses are highly variable. A simple hypothesis, originating in signal detection theory, is that perceived inputs generate neural activity that cross some threshold for detection. According to this hypothesis, sensory response variability would predict behavioral variability, but previous studies have not born out this prediction. Further complicating the picture, sensory response variability is partially dependent on the ongoing state of cortical activity, and we wondered whether this could resolve the mismatch between response variability and behavioral variability. Here, we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs. This observer has higher overall accuracy than the standard ideal observer. Moreover, because of the adaptation, the observer breaks the direct link between neural and behavioral variability, which could resolve discrepancies arising in past studies. We suggest new experiments to test our theory.

A behavioral response appropriate to a sensory stimulus depends on the collective activity of thousands of interconnected neurons. The majority of cortical connections arise from neighboring neurons, and thus understanding the cortical code requires characterizing information representation at the scale of the cortical microcircuit. Using two-photon calcium imaging, we densely sampled the thalamically evoked response of hundreds of neurons spanning multiple layers and columns in thalamocortical slices of mouse somatosensory cortex. We then used a biologically plausible decoder to characterize the representation of two distinct thalamic inputs, at the level of the microcircuit, to reveal those aspects of the activity pattern that are likely relevant to downstream neurons. Our data suggest a sparse code, distributed across lamina, in which a small population of cells carries stimulus-relevant information. Furthermore, we find that, within this subset of neurons, decoder performance improves when noise correlations are taken into account.

Stimulus discrimination depends on the selectivity and variability of neural responses, as well as the size and correlation structure of the responsive population. For direction discrimination in visual cortex, only the selectivity of neurons has been well characterized across development. Here we show in ferrets that at eye opening, the cortical response to visual stimulation exhibits several immaturities, including a high density of active neurons that display prominent wave-like activity, a high degree of variability and strong noise correlations. Over the next three weeks, the population response becomes increasingly sparse, wave-like activity disappears, and variability and noise correlations are markedly reduced. Similar changes were observed in identified neuronal populations imaged repeatedly over days. Furthermore, experience with a moving stimulus was capable of driving a reduction in noise correlations over a matter of hours. These changes in variability and correlation contribute significantly to a marked improvement in direction discriminability over development.

Little is known about whether information transfer at primary sensory thalamic nuclei is modified by behavioral context. Here we studied the influence of previous decisions/rewards on current choices and preceding spike responses of ventroposterior medial thalamus (VPm; the primary sensory thalamus in the rat whisker-related tactile system). We trained head-fixed rats to detect a ramp-like deflection of one whisker interspersed within ongoing white noise stimulation. Using generative modeling of behavior, we identify two task-related variables that are predictive of actual decisions. The first reflects task engagement on a local scale (“trial history”: defined as the decisions and outcomes of a small number of past trials), whereas the other captures behavioral dynamics on a global scale (“satiation”: slow dynamics of the response pattern along an entire session). Although satiation brought about a slow drift from Go to NoGo decisions during the session, trial history was related to local (trial-by-trial) patterning of Go and NoGo decisions. A second model that related the same predictors first to VPm spike responses, and from there to decisions, indicated that spiking, in contrast to behavior, is sensitive to trial history but relatively insensitive to satiation. Trial history influences VPm spike rates and regularity such that a history of Go decisions would predict fewer noise-driven spikes (but more regular ones), and more ramp-driven spikes. Neuronal activity in VPm, thus, is sensitive to local behavioral history, and may play an important role in higher-order cognitive signaling.SIGNIFICANCE STATEMENTIt is an important question for perceptual and brain functions to find out whether cognitive signals modulate the sensory signal stream and if so, where in the brain this happens. This study provides evidence that decision and reward history can already be reflected in the ascending sensory pathway, on the level of first-order sensory thalamus. Cognitive signals are relayed very selectively such that only local trial history (spanning a few trials) but not global history (spanning an entire session) are reflected.

With the recent breakthrough in genetically expressed voltage indicators (GEVIs), there has been a tremendous demand to determine the capabilities of these sensors in vivo. Novel voltage sensitive fluorescent proteins allow for direct measurement of neuron membrane potential changes through changes in fluorescence. Here, we utilized ArcLight, a recently developed GEVI, and examined the functional characteristics in the widely used mouse somatosensory whisker pathway. We measured the resulting evoked fluorescence using a wide-field microscope and a CCD camera at 200 Hz, which enabled voltage recordings over the entire cortical region with high temporal resolution. We found that ArcLight produced a fluorescent response in the S1 barrel cortex during sensory stimulation at single whisker resolution. During wide-field cortical imaging, we encountered substantial hemodynamic noise that required additional post hoc processing through noise subtraction techniques. Over a period of 28 days, we found clear and consistent ArcLight fluorescence responses to a simple sensory input. Finally, we demonstrated the use of ArcLight to resolve cortical S1 sensory responses in the awake mouse. Taken together, our results demonstrate the feasibility of ArcLight as a measurement tool for mesoscopic, chronic imaging.

In a broad class of models, direction selectivity in primary visual cortical neurons arises from the linear summation of spatially offset and temporally lagged inputs combined with a spike threshold. Here, we characterize the robustness of this class of models to input noise and background activity that is uncorrelated with the visual stimulus. When only excitatory inputs were considered, moderate levels of noise substantially degraded direction selectivity. By contrast, the inclusion of inhibition produced a direction-selective neuron even at high noise levels. Moreover, if inhibitory inputs were tuned, mirroring excitatory inputs but lagging by a fixed delay, they promoted a highly direction-selective response by suppressing all excitatory inputs in the null direction while minimally affecting excitatory inputs in the preferred direction. Additionally, tuned inhibition strongly reduced trial-by-trial variability, such that the neuron produced a consistent direction-selective response to multiple presentation of the same stimulus. This work illustrates how inhibition could be used by cortical circuits to reliably extract information on a single-trial basis from feed-forward inputs in a noisy, high-background context.

The Millimeter Bolometer Array Camera (MBAC) was commissioned in the fall of 2007 on the new 6-meterAtacama Cosmology Telescope (ACT). The MBAC on the ACT will map the temperature anisotropies of theCosmic Microwave Background (CMB) with arc-minute resolution. For this first observing season, the MBACcontained a diffraction-limited, 32 by 32 element, focal plane array of Transition Edge Sensor (TES) bolometersfor observations at 145 GHz. This array was coupled to the telescope with a series of cold, refractive, reimagingoptics. To meet the performance specifications, the MBAC employs four stages of cooling using closed-cycle3He/4He sorption fridge systems in combination with pulse tube coolers. In this paper we present the design ofthe instrument and discuss its performance during the first observing season. Finally, we report on the statusof the MBAC for the 2008 observing season, when the instrument will be upgraded to a total of three separate1024-element arrays at 145 GHz, 220 GHz and 280 GHz.

The Atacama Cosmology Telescope (ACT) aims to measure the Cosmic Microwave Background (CMB) temperature anisotropies on arcminute scales. The primary receiver for ACT is the Millimeter Bolometer Array Camera (MBAC). The MBAC is comprised of three 32×32 transition edge sensor (TES) bolometer arrays, each observing the sky with an independent set of band-defining filters. The MBAC arrays will be the largest pop-up detector arrays fielded, and among the largest TES arrays built. Prior to its assembly into an array and installation into the MBAC, a column of 32 bolometers is tested at ~ 0.4 K in a quick-turn-around dip probe. In this paper we describe the properties of the ACT bolometers as revealed by data from those tests, emphasizing a characterization that accounts for both the complex impedance and the noise as a function of frequency.

The Atacama Cosmology Telescope (ACT) is designed to measure temperature anisotropies of the cosmic microwave background (CMB) at arcminute resolution. It is the first CMB experiment to employ a 32×32 close-packed array of free-space-coupled transition-edge superconducting bolometers. We describe the organization of the telescope systems and software for autonomous, scheduled operations. When paired with real-time data streaming and display, we are able to operate the telescope at the remote site in the Chilean Altiplano via the Internet from North America. The telescope had a data rate of 70 GB/day in the 2007 season, and the 2008 upgrade to three arrays will bring this to 210 GB/day.

The Atacama Cosmology Telescope is a six meter, off-axis Gregorian telescope for measuring the cosmic microwave background at arcminute resolutions. The Millimeter Bolometer Array Camera (MBAC) is its current science instrument. Erected in the Atacama Desert of Chile in early 2007, it saw first light with the MBAC on 22 October 2007. In this paper we review its performance after one month of observing, focusing in particular on issues surrounding the alignment of the optical system that impact the sensitivity of the experiment. We discuss the telescope motion, pointing, and susceptibility to thermal distortions. We describe the mirror alignment procedure, which has yielded surface deviations of 31 μm rms on the primary and 10 μm rms on the secondary. Observations of planets show that the optical performance is consistent with the telescope design parameters. Preliminary analysis measures a solid angle of about 215 nanosteradians with a full width at half maximum of 1.44 arcminutes at 145 GHz.