The Hybrid Drive: a chronic implant device combining tetrode arrays with silicon probes for layer-resolved ensemble electrophysiology in freely moving mice.

Objective. Understanding the function of brain cortices requires simultaneous investigation at multiple spatial and temporal scales and to link neural activity to an animal's behavior. A major challenge is to measure within- and across-layer information in actively behaving animals, in particular in mice that have become a major species in neuroscience due to an extensive genetic toolkit. Here we describe the Hybrid Drive, a new chronic implant for mice that combines tetrode arrays to record within-layer information with silicon probes to simultaneously measure across-layer information.Approach. The design of our device combines up to 14 tetrodes and 2 silicon probes, that can be arranged in custom arrays to generate unique areas-specific (and multi-area) layouts.Main results. We show that large numbers of neurons and layer-resolved local field potentials can be recorded from the same brain region across weeks without loss in electrophysiological signal quality. The drive's lightweight structure (≈3.5 g) leaves animal behavior largely unchanged, compared to other tetrode drives, during a variety of experimental paradigms. We demonstrate how the data collected with the Hybrid Drive allow state-of-the-art analysis in a series of experiments linking the spiking activity of CA1 pyramidal layer neurons to the oscillatory activity across hippocampal layers.Significance. Our new device fits a gap in the existing technology and increases the range and precision of questions that can be addressed about neural computations in freely behaving mice.

Mouse visual cortex contains a region of enhanced spatial resolution.

The representation of space in mouse visual cortex was thought to be relatively uniform. Here we reveal, using population receptive-field (pRF) mapping techniques, that mouse visual cortex contains a region in which pRFs are considerably smaller. This region, the "focea," represents a location in space in front of, and slightly above, the mouse. Using two-photon imaging we show that the smaller pRFs are due to lower scatter of receptive-fields at the focea and an over-representation of binocular regions of space. We show that receptive-fields of single-neurons in areas LM and AL are smaller at the focea and that mice have improved visual resolution in this region of space. Furthermore, freely moving mice make compensatory eye-movements to hold this region in front of them. Our results indicate that mice have spatial biases in their visual processing, a finding that has important implications for the use of the mouse model of vision.

Vision: Depth perception in climbing mice.

Depth perception helps animals interact with a three-dimensional world. A new study presents a novel paradigm for studying depth perception in naturally climbing mice and links their behavior to binocular disparity signals in primary visual cortical neurons.

Two Distinct Types of Eye-Head Coupling in Freely Moving Mice.

Animals actively interact with their environment to gather sensory information. There is conflicting evidence about how mice use vision to sample their environment. During head restraint, mice make rapid eye movements coupled between the eyes, similar to conjugate saccadic eye movements in humans. However, when mice are free to move their heads, eye movements are more complex and often non-conjugate, with the eyes moving in opposite directions. We combined head and eye tracking in freely moving mice and found both observations are explained by two eye-head coupling types, associated with vestibular mechanisms. The first type comprised non-conjugate eye movements, which compensate for head tilt changes to maintain a similar visual field relative to the horizontal ground plane. The second type of eye movements was conjugate and coupled to head yaw rotation to produce a "saccade and fixate" gaze pattern. During head-initiated saccades, the eyes moved together in the head direction but during subsequent fixation moved in the opposite direction to the head to compensate for head rotation. This saccade and fixate pattern is similar to humans who use eye movements (with or without head movement) to rapidly shift gaze but in mice relies on combined head and eye movements. Both couplings were maintained during social interactions and visually guided object tracking. Even in head-restrained mice, eye movements were invariably associated with attempted head motion. Our results reveal that mice combine head and eye movements to sample their environment and highlight similarities and differences between eye movements in mice and humans.

A Head-Mounted Camera System Integrates Detailed Behavioral Monitoring with Multichannel Electrophysiology in Freely Moving Mice.

Breakthroughs in understanding the neural basis of natural behavior require neural recording and intervention to be paired with high-fidelity multimodal behavioral monitoring. An extensive genetic toolkit for neural circuit dissection, and well-developed neural recording technology, make the mouse a powerful model organism for systems neuroscience. However, most methods for high-bandwidth acquisition of behavioral data in mice rely upon fixed-position cameras and other off-animal devices, complicating the monitoring of animals freely engaged in natural behaviors. Here, we report the development of a lightweight head-mounted camera system combined with head-movement sensors to simultaneously monitor eye position, pupil dilation, whisking, and pinna movements along with head motion in unrestrained, freely behaving mice. The power of the combined technology is demonstrated by observations linking eye position to head orientation; whisking to non-tactile stimulation; and, in electrophysiological experiments, visual cortical activity to volitional head movements.

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation.

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far-ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or "priors." In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available.

Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations.

BACKGROUND: The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. NEW METHOD: Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. RESULTS AND COMPARISON WITH EXISTING METHOD: Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. CONCLUSIONS: The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves.

Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles.

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

Temporal variability of spectro-temporal receptive fields in the anesthetized auditory cortex.

Temporal variability of neuronal response characteristics during sensory stimulation is a ubiquitous phenomenon that may reflect processes such as stimulus-driven adaptation, top-down modulation or spontaneous fluctuations. It poses a challenge to functional characterization methods such as the receptive field, since these often assume stationarity. We propose a novel method for estimation of sensory neurons' receptive fields that extends the classic static linear receptive field model to the time-varying case. Here, the long-term estimate of the static receptive field serves as the mean of a probabilistic prior distribution from which the short-term temporally localized receptive field may deviate stochastically with time-varying standard deviation. The derived corresponding generalized linear model permits robust characterization of temporal variability in receptive field structure also for highly non-Gaussian stimulus ensembles. We computed and analyzed short-term auditory spectro-temporal receptive field (STRF) estimates with characteristic temporal resolution 5-30 s based on model simulations and responses from in total 60 single-unit recordings in anesthetized Mongolian gerbil auditory midbrain and cortex. Stimulation was performed with short (100 ms) overlapping frequency-modulated tones. Results demonstrate identification of time-varying STRFs, with obtained predictive model likelihoods exceeding those from baseline static STRF estimation. Quantitative characterization of STRF variability reveals a higher degree thereof in auditory cortex compared to midbrain. Cluster analysis indicates that significant deviations from the long-term static STRF are brief, but reliably estimated. We hypothesize that the observed variability more likely reflects spontaneous or state-dependent internal fluctuations that interact with stimulus-induced processing, rather than experimental or stimulus design.