Arne-Freerk Meyer

The perceptual salience of a sound depends on the acoustic context in which it appears, and can vary on a timescale of milliseconds. At the level of single neurons in the auditory cortex, spectrotemporal tuning for particular sounds is shaped by a similarly fast and systematic nonlinear sensitivity to acoustic context. Does this neuronal context sensitivity “drift” over time in awake animals, or is it a stable feature of sound representation in the auditory cortex? We used chronically implanted tetrode arrays in awake mice to measure the electrophysiological responses of auditory cortical neurons to spectrotemporally complex, rapidly varying sounds across many days. For each neuron in each recording session, we applied the nonlinear-linear “context model” to estimate both a principal (spectrotemporal) receptive field and a “contextual gain field” describing the neuron’s nonlinear sensitivity to acoustic context. We then quantified the stability of these fields within and across days, using spike waveforms to match neurons recorded in multiple sessions. Contextual gain fields of auditory cortical neurons in awake mice were remarkably stable across many days of recording, and comparable in stability to principal receptive fields. Interestingly, there were small but significant effects of changes in locomotion or pupil size on the ability of the context model to fit temporal fluctuations in the neuronal response. We conclude that both spectrotemporal tuning and nonlinear sensitivity to acoustic context are stable features of neuronal sound representation in the awake auditory cortex, which can be modulated by behavioral state.

The perceptual salience of a sound depends on the acoustic context in which it appears, and can vary on a timescale of milliseconds. At the level of single neurons in the auditory cortex, spectrotemporal tuning for particular sounds is shaped by a similarly fast and systematic nonlinear sensitivity to acoustic context. Does this neuronal context sensitivity “drift” over time in awake animals, or is it a stable feature of sound representation in the auditory cortex? We used chronically implanted tetrode arrays in awake mice to measure the electrophysiological responses of auditory cortical neurons to spectrotemporally complex, rapidly varying sounds across many days. For each neuron in each recording session, we applied the nonlinear-linear “context model” to estimate both a principal (spectrotemporal) receptive field and a “contextual gain field” describing the neuron’s nonlinear sensitivity to acoustic context. We then quantified the stability of these fields within and across days, using spike waveforms to match neurons recorded in multiple sessions. Contextual gain fields of auditory cortical neurons in awake mice were remarkably stable across many days of recording, and comparable in stability to principal receptive fields. Interestingly, there were small but significant effects of changes in locomotion or pupil size on the ability of the context model to fit temporal fluctuations in the neuronal response. We conclude that both spectrotemporal tuning and nonlinear sensitivity to acoustic context are stable features of neuronal sound representation in the awake auditory cortex, which can be modulated by behavioral state.

Summary: 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. 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. 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 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. 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.

Summary: 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 strongly coupled between the eyes, similar to conjugate saccadic eye movements in humans. However, when mice are free to move their heads, eye movement patterns are more complex and often non-conjugate, with the eyes moving in opposite directions. Here, we combined eye tracking with head motion measurements in freely moving mice and found that both observations can be explained by the existence of two distinct types of coupling between eye and head movements. The first type comprised non-conjugate eye movements which systematically compensated for changes in head tilt to maintain approximately the same visual field relative to the horizontal ground plane. The second type of eye movements were 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 same direction as the head, 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 that seen in humans who use eye movements (with or without head movement) to rapidly shift gaze but in mice relies on combined eye and head movements. Indeed, the two types of eye movements very rarely occurred in the absence of head movements. Even in head-restrained mice, eye movements were invariably associated with attempted head motion. Both types of eye-head coupling were seen in freely moving mice during social interactions and a visually-guided object tracking task. Our results reveal that mice use a combination of head and eye movements to sample their environment and highlight the similarities and differences between eye movements in mice and humans. Highlights: Tracking of eyes and head in freely moving mice reveals two types of eye-head coupling Eye/head tilt coupling aligns gaze to horizontal plane Rotational eye and head coupling produces a “saccade and fixate” gaze pattern with head leading the eye Both types of eye-head coupling are maintained during visually-guided behaviors Eye movements in head-restrained mice are related to attempted head movements

Hearing loss has been implicated as a risk factor for schizophrenia, but it is not known whether this association arises from common etiology, top-down influences (e.g., social isolation), bottom-up neurobiological mechanisms, or combinations of these factors. Patients with 22q11.2 Deletion Syndrome (22q11.2DS) have a 25-30% risk of developing schizophrenia, and also suffer frequent hearing loss. Here, we used the Df1/+ mouse model of 22q11.2DS to investigate the relationship between hearing loss and susceptibility to schizophrenia-relevant brain and behavioral abnormalities. Df1/+ mice have a multi-gene deletion analogous to the chromosomal microdeletion that causes human 22q11.2DS, and like human 22q11.2DS patients exhibit high rates of hearing loss arising primarily from susceptibility to middle ear inflammation. We found that hearing loss in Df1/+ mice affected schizophrenia-relevant endophenotypes, including electrophysiological measures of central auditory gain and behavioral measures of auditory sensorimotor gating. Moreover, PV+ inhibitory interneurons, another marker for schizophrenia pathology, were significantly reduced in density in auditory cortex but not secondary motor cortex of Df1/+ mice with hearing loss. These results reveal bottom-up neurobiological mechanisms through which peripheral hearing loss arising from the 22q11.2 deletion may promote the emergence of schizophrenia-relevant auditory brain and behavioral abnormalities, and also suggest a link between conductive hearing loss and reduced PV+ interneuron density in the auditory cortex.SIGNIFICANCE STATEMENT: Hearing loss is a known risk factor for schizophrenia. Deletion of chromosomal locus 22q11.2 is associated with both schizophrenia and hearing loss in humans. In the Df1/+ mouse model of human 22q11.2 Deletion Syndrome, we find that hearing loss shapes measures that are considered schizophrenia-relevant endophenotypes, such as central auditory gain and auditory sensorimotor gating. Moreover, we report a reduction in density of PV+ inhibitory interneurons in the auditory cortex, but not secondary motor cortex, of Df1/+ mice with hearing loss. These results suggest mechanisms through which hearing loss associated with the 22q11.2 deletion may promote emergence of schizophrenia-relevant auditory brain and behavioral abnormalities and indicate that conductive hearing loss may influence PV+ interneuron density in the auditory cortex.

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 characterise 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 normalisation 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 regularising 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.

We introduce a new class of efficient estimators based on score matching for probabilistic point process models. Unlike discretised likelihood-based estimators, score matching estimators operate on continuous-time data, with computational demands that grow with the number of events rather than with total observation time. Furthermore, estimators for many common regression models can be obtained in closed form, rather than by iteration. This new approach to estimation may thus expand the range of tractable models available for event-based data.