Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales.

The processing of sensory information, even at early stages, is influenced by the internal state of the animal. Internal states, such as arousal, are often characterized by relating neural activity to a single "level" of arousal, defined by a behavioral indicator such as pupil size. In this study, we expand the understanding of arousal-related modulations in sensory systems by uncovering multiple timescales of pupil dynamics and their relationship to neural activity. Specifically, we observed a robust coupling between spiking activity in the mouse dorsolateral geniculate nucleus (dLGN) of the thalamus and pupil dynamics across timescales spanning a few seconds to several minutes. Throughout all these timescales, 2 distinct spiking modes-individual tonic spikes and tightly clustered bursts of spikes-preferred opposite phases of pupil dynamics. This multi-scale coupling reveals modulations distinct from those captured by pupil size per se, locomotion, and eye movements. Furthermore, coupling persisted even during viewing of a naturalistic movie, where it contributed to differences in the encoding of visual information. We conclude that dLGN spiking activity is under the simultaneous influence of multiple arousal-related processes associated with pupil dynamics occurring over a broad range of timescales.

Ambient sound stimulation tunes axonal conduction velocity by regulating radial growth of myelin on an individual, axon-by-axon basis.

Adaptive myelination is the emerging concept of tuning axonal conduction velocity to the activity within specific neural circuits over time. Sound processing circuits exhibit structural and functional specifications to process signals with microsecond precision: a time scale that is amenable to adjustment in length and thickness of myelin. Increasing activity of auditory axons by introducing sound-evoked responses during postnatal development enhances myelin thickness, while sensory deprivation prevents such radial growth during development. When deprivation occurs during adulthood, myelin thickness was reduced. However, it is unclear whether sensory stimulation adjusts myelination in a global fashion (whole fiber bundles) or whether such adaptation occurs at the level of individual fibers. Using temporary monaural deprivation in mice provided an internal control for a) differentially tracing structural changes in active and deprived fibers and b) for monitoring neural activity in response to acoustic stimulation of the control and the deprived ear within the same animal. The data show that sound-evoked activity increased the number of myelin layers around individual active axons, even when located in mixed bundles of active and deprived fibers. Thicker myelination correlated with faster axonal conduction velocity and caused shorter auditory brainstem response wave VI-I delays, providing a physiologically relevant readout. The lack of global compensation emphasizes the importance of balanced sensory experience in both ears throughout the lifespan of an individual.

Connecting Connectomes to Physiology.

With the advent of volumetric EM techniques, large connectomic datasets are being created, providing neuroscience researchers with knowledge about the full connectivity of neural circuits under study. This allows for numerical simulation of detailed, biophysical models of each neuron participating in the circuit. However, these models typically include a large number of parameters, and insight into which of these are essential for circuit function is not readily obtained. Here, we review two mathematical strategies for gaining insight into connectomics data: linear dynamical systems analysis and matrix reordering techniques. Such analytical treatment can allow us to make predictions about time constants of information processing and functional subunits in large networks.SIGNIFICANCE STATEMENT This viewpoint provides a concise overview on how to extract important insights from Connectomics data by mathematical methods. First, it explains how new dynamics and new time constants can evolve, simply through connectivity between neurons. These new time-constants can be far longer than the intrinsic membrane time-constants of the individual neurons. Second, it summarizes how structural motifs in the circuit can be discovered. Specifically, there are tools to decide whether or not a circuit is strictly feed-forward or whether feed-back connections exist. Only by reordering connectivity matrices can such motifs be made visible.

Low rate hippocampal delay period activity encodes behavioral experience.

Remembering what just happened is a crucial prerequisite to form long-term memories but also for establishing and maintaining working memory. So far there is no general agreement about cortical mechanisms that support short-term memory. Using a classifier-based decoding approach, we report that hippocampal activity during few sparsely distributed brief time intervals contains information about the previous sensory motor experience of rodents. These intervals are characterized by only a small increase of firing rate of only a few neurons. These low-rate predictive patterns are present in both working memory and non-working memory tasks, in two rodent species, rats and Mongolian gerbils, are strongly reduced for rats with medial entorhinal cortex lesions, and depend on the familiarity of the sensory-motor context.

Directional Tuning of Phase Precession Properties in the Hippocampus.

Running direction in the hippocampus is encoded by rate modulations of place field activity but also by spike timing correlations known as theta sequences. Whether directional rate codes and the directionality of place field correlations are related, however, has so far not been explored, and therefore the nature of how directional information is encoded in the cornu ammonis remains unresolved. Here, using a previously published dataset that contains the spike activity of rat hippocampal place cells in the CA1, CA2, and CA3 subregions during free foraging of male Long-Evans rats in a 2D environment, we found that rate and spike timing codes are related. Opposite to a preferred firing rate direction of a place field, spikes are more likely to undergo theta phase precession and, hence, more strongly affect paired correlations. Furthermore, we identified a subset of field pairs whose theta correlations are intrinsic in that they maintain the same firing order when the running direction is reversed. Both effects are associated with differences in theta phase distributions and are more prominent in CA3 than in CA1. We thus hypothesize that intrinsic spiking is most prominent when the directionally modulated sensory-motor drive of hippocampal firing rates is minimal, suggesting that extrinsic and intrinsic sequences contribute to phase precession as two distinct mechanisms.SIGNIFICANCE STATEMENT Hippocampal theta sequences, on the one hand, are thought to reflect the running trajectory of an animal, connecting past and future locations. On the other hand, sequences have been proposed to reflect the rich, recursive hippocampal connectivity, related to memories of previous trajectories or even to experience-independent prestructure. Such intrinsic sequences are inherently one dimensional and cannot be easily reconciled with running trajectories in two dimensions as place fields can be approached on multiple one-dimensional paths. In this article, we dissect phase precession along different directions in all hippocampal subareas and find that CA3 in particular shows a high level of direction-independent correlations that are inconsistent with the notion of representing running trajectories. These intrinsic correlations are associated with later spike phases.

Synaptic Mechanisms underlying Temporally Precise Information Processing in the VNLL, an auditory brainstem nucleus.

Large glutamatergic, somatic synapses mediate temporally precise information transfer. In the ventral nucleus of the lateral lemniscus (VNLL), an auditory brainstem nucleus, the signal of an excitatory large somatic synapse is sign inverted to generate rapid feed forward inhibition with high temporal acuity at sound onsets, a mechanism involved in the suppression of spurious frequency information. The mechanisms of the synaptically driven input-output functions in the VNLL are not fully resolved. Here, we show in Mongolian gerbils of both sexes that for stimulation frequencies up to 200 Hz the EPSC kinetics together with short-term plasticity allow for faithful transmission with only a small increase in latency. Glutamatergic currents are exclusively mediated by AMPARs and NMDARs. Short-term plasticity is frequency dependent and composed of an initial facilitation followed by depression. Physiologically relevant output generation is limited by the decrease in synaptic conductance through short-term plasticity (STP). At this endbulb synapse, STP acts as a low pass filter and increases the dynamic range of the conductance dependent input-output relation, while NMDAR signaling slightly increases the sensitivity of the input-output function. Our computational model shows that STP-mediated filtering limits the intensity dependence of the spike output, thus maintaining selectivity to sound transients. Our results highlight the interaction of cellular features that together give rise to the computations in the circuit.Significant statementAuditory information processing in the brainstem is a prerequisite for generating our auditory representation of the environment. Thereby, many processing steps rely on temporally precise filtering. Precise feed forward inhibition is a key motif in auditory brainstem processing and produced through sign inversion at several large somatic excitatory synapses. A particular feature of the ventral nucleus of the lateral lemniscus is to produce temporally precise onset inhibition with little temporal variance independent of sound intensity. Our cell-physiology and modeling data explain how the synaptic characteristics of different current components and their short-term plasticity are tuned to establish sound intensity-invariant onset inhibition that is crucial for filtering out spurious frequency information.

Arrangement of Excitatory Synaptic Inputs on Dendrites of the Medial Superior Olive.

Neurons in the medial superior olive (MSO) detect 10 µs differences in the arrival times of a sound at the two ears. Such acuity requires exquisitely precise integration of binaural synaptic inputs. There is substantial understanding of how neuronal phase locking of afferent MSO structures, and MSO membrane biophysics subserve such high precision. However, we still lack insight into how the entirety of excitatory inputs is integrated along the MSO dendrite under sound stimulation. To understand how the dendrite integrates excitatory inputs as a whole, we combined anatomic quantifications of the afferent innervation in gerbils of both sexes with computational modeling of a single cell. We present anatomic data from confocal and transmission electron microscopy showing that single afferent fibers follow a single dendrite mostly up to the soma and contact it at multiple (median 4) synaptic sites, each containing multiple independent active zones (the overall density of active zones is estimated as 1.375 per µm2). Thus, any presynaptic action potential may elicit temporally highly coordinated synaptic vesicle release at tens of active zones, thereby achieving secure transmission. Computer simulations suggest that such an anatomic arrangement boosts the amplitude and sharpens the time course of excitatory postsynaptic potentials by reducing current sinks and more efficiently recruiting subthreshold potassium channels. Both effects improve binaural coincidence detection compared with single large synapses at the soma. Our anatomic data further allow for estimation of a lower bound of 7 and an upper bound of 70 excitatory fibers per dendrite.SIGNIFICANCE STATEMENT Passive dendritic propagation attenuates the amplitude of postsynaptic potentials and widens their temporal spread. Neurons in the medial superior olive, with their large bilateral dendrites, however, can detect coincidence of binaural auditory inputs with submillisecond precision, a computation that is in stark contrast to passive dendritic processing. Here, we show that dendrites can counteract amplitude attenuation and even decrease the temporal spread of postsynaptic potentials, if active subthreshold potassium conductances are triggered in temporal coordination along the whole dendrite. Our anatomic finding that axons run in parallel to the dendrites and make multiple synaptic contacts support such coordination since incoming action potentials would depolarize the dendrite at multiple sites within a brief time interval.

Neural kernels for recursive support vector regression as a model for episodic memory.

Retrieval of episodic memories requires intrinsic reactivation of neuronal activity patterns. The content of the memories is thereby assumed to be stored in synaptic connections. This paper proposes a theory in which these are the synaptic connections that specifically convey the temporal order information contained in the sequences of a neuronal reservoir to the sensory-motor cortical areas that give rise to the subjective impression of retrieval of sensory motor events. The theory is based on a novel recursive version of support vector regression that allows for efficient continuous learning that is only limited by the representational capacity of the reservoir. The paper argues that hippocampal theta sequences are a potential neural substrate underlying this reservoir. The theory is consistent with confabulations and post hoc alterations of existing memories.

A phenomenological spiking model for octopus cells in the posterior-ventral cochlear nucleus.

Octopus cells in the posteroventral cochlear nucleus exhibit characteristic onset responses to broad band transients but are little investigated in response to more complex sound stimuli. In this paper, we propose a phenomenological, but biophysically motivated, modeling approach that allows to simulate responses of large populations of octopus cells to arbitrary sound pressure waves. The model depends on only few parameters and reproduces basic physiological characteristics like onset firing and phase locking to amplitude modulations. Simulated responses to speech stimuli suggest that octopus cells are particularly sensitive to high-frequency transients in natural sounds and their sustained firing to phonemes provides a population code for sound level.

Modality-specific Subpopulations of Place Fields Coexist in the Hippocampus.

Hippocampal place cells integrate signals from multiple sensory modalities. However, it is unclear how these different inputs are combined to generate place fields. We investigated how visual spatial cues and an animal's locomotion are integrated by CA3 place cells of Mongolian gerbils. While the animals moved on a virtual linear track, we adapted the gain between the visually projected environment and the treadmill movement. Place cells responded differently to this manipulation. In a subset, place fields were kept in accord with salient visual cues in the virtual environment or reward location, whereas in another subset, place fields were strongly influenced by locomotion. Theta phase precession was present and indistinguishable between the place field types. Theta compression remained intact under gain changes and extended over both types of place field. Hippocampal place cells thus retain strong influence from distinct input streams suggesting a role of the hippocampus CA3 as a multimodal associator on the theta time scale.

The hippocampal code for space in Mongolian gerbils.

Large parts of our knowledge about the physiology of the hippocampus in the intact brain are derived from studies in rats and mice. While many of those findings fit well to the limited data available from humans and primates, there are also marked differences, for example, in hippocampal oscillation frequencies and in the persistence of theta oscillations. To test whether the distinct sensory specializations of the visual and auditory system of primates play a key role in explaining these differences, we recorded basic hippocampal physiological properties in Mongolian gerbils, a rodent species with high visual acuity, and good low-frequency hearing, similar to humans. We found that gerbils show only minor differences to rats regarding hippocampal place field activity, theta properties (frequency, persistence, phase precession, theta compression), and sharp wave ripple events. The only major difference between rats and gerbils was a considerably higher degree of head direction selectivity of gerbil place fields, which may be explained by their visual system being able to better resolve distant cues. Thus, differences in sensory specializations between rodent species only affect hippocampal circuit dynamics to a minor extent, which implies that differences to other mammalian lineages, such as bats and primates, cannot be solely explained by specialization in the auditory or visual system.

Multivariate classification of neuroimaging data with nested subclasses: Biased accuracy and implications for hypothesis testing.

Biological data sets are typically characterized by high dimensionality and low effect sizes. A powerful method for detecting systematic differences between experimental conditions in such multivariate data sets is multivariate pattern analysis (MVPA), particularly pattern classification. However, in virtually all applications, data from the classes that correspond to the conditions of interest are not homogeneous but contain subclasses. Such subclasses can for example arise from individual subjects that contribute multiple data points, or from correlations of items within classes. We show here that in multivariate data that have subclasses nested within its class structure, these subclasses introduce systematic information that improves classifiability beyond what is expected by the size of the class difference. We analytically prove that this subclass bias systematically inflates correct classification rates (CCRs) of linear classifiers depending on the number of subclasses as well as on the portion of variance induced by the subclasses. In simulations, we demonstrate that subclass bias is highest when between-class effect size is low and subclass variance high. This bias can be reduced by increasing the total number of subclasses. However, we can account for the subclass bias by using permutation tests that explicitly consider the subclass structure of the data. We illustrate our result in several experiments that recorded human EEG activity, demonstrating that parametric statistical tests as well as typical trial-wise permutation fail to determine significance of classification outcomes correctly.

Sound-Evoked Activity Influences Myelination of Brainstem Axons in the Trapezoid Body.

Plasticity of myelination represents a mechanism to tune the flow of information by balancing functional requirements with metabolic and spatial constraints. The auditory system is heavily myelinated and operates at the upper limits of action potential generation frequency and speed observed in the mammalian CNS. This study aimed to characterize the development of myelin within the trapezoid body, a central auditory fiber tract, and determine the influence sensory experience has on this process in mice of both sexes. We find that in vitro conduction speed doubles following hearing onset and the ability to support high-frequency firing increases concurrently. Also in this time, the diameter of trapezoid body axons and the thickness of myelin double, reaching mature-like thickness between 25 and 35 d of age. Earplugs were used to induce ∼50 dB elevation in auditory thresholds. If introduced at hearing onset, trapezoid body fibers developed thinner axons and myelin than age-matched controls. If plugged during adulthood, the thickest trapezoid body fibers also showed a decrease in myelin. These data demonstrate the need for sensory activity in both development and maintenance of myelin and have important implications in the study of myelin plasticity and how this could relate to sensorineural hearing loss following peripheral impairment.SIGNIFICANCE STATEMENT The auditory system has many mechanisms to maximize the dynamic range of its afferent fibers, which operate at the physiological limit of action potential generation, precision, and speed. In this study we demonstrate for the first time that changes in peripheral activity modifies the thickness of myelin in sensory neurons, not only in development but also in mature animals. The current study suggests that changes in CNS myelination occur as a downstream mechanism following peripheral deficit. Given the required submillisecond temporal precision for binaural auditory processing, reduced myelination might augment sensorineural hearing impairment.

Electrogenic N-methyl-D-aspartate receptor signaling enhances binaural responses in the adult brainstem.

In sensory systems, the neuronal representation of external stimuli is enhanced along the sensory pathway. In the auditory system, strong enhancement of binaural information takes place between the brainstem and the midbrain; however, the underlying cellular mechanisms are unknown. Here we investigated the transformation of binaural information in the dorsal nucleus of the lateral lemniscus (DNLL), a nucleus that connects the binaural nuclei in the brainstem and the inferior colliculus in the midbrain. We used in vitro and in vivo electrophysiology in adult Mongolian gerbils to show that N-methyl-D-aspartate receptor (NMDARs) play a critical role in neuronal encoding of stimulus properties in the DNLL. While NMDARs increase firing rates, the timing and the accuracy of the neuronal responses remain unchanged. NMDAR-mediated excitation increases the information about the acoustic stimulus. Taken together, our results show that NMDARs in the DNLL enhance the auditory information content in adult mammal brainstem.

Decoding cognitive concepts from neuroimaging data using multivariate pattern analysis.

Multivariate pattern analysis (MVPA) methods are now widely used in life-science research. They have great potential but their complexity also bears unexpected pitfalls. In this paper, we explore the possibilities that arise from the high sensitivity of MVPA for stimulus-related differences, which may confound estimations of class differences during decoding of cognitive concepts. We propose a method that takes advantage of concept-unrelated grouping factors, uses blocked permutation tests, and gradually manipulates the proportion of concept-related information in data while the stimulus-related, concept-irrelevant factors are held constant. This results in a concept-response curve, which shows the relative contribution of these two components, i.e. how much of the decoding performance is specific to higher-order category processing and to lower order stimulus processing. It also allows separating stimulus-related from concept-related neuronal processing, which cannot be achieved experimentally. We applied our method to three different EEG data sets with different levels of stimulus-related confound to decode concepts of digits vs. letters, faces vs. houses, and animals vs. fruits based on event-related potentials at the single trial level. We show that exemplar-specific differences between stimuli can drive classification accuracy to above chance levels even in the absence of conceptual information. By looking into time-resolved windows of brain activity, concept-response curves can help characterize the time-course of lower-level and higher-level neural information processing and detect the corresponding temporal and spatial signatures of the corresponding cognitive processes. In particular, our results show that perceptual information is decoded earlier in time than conceptual information specific to processing digits and letters. In addition, compared to the stimulus-level predictive sites, concept-related topographies are spread more widely and, at later time points, reach the frontal cortex. Thus, our proposed method yields insights into cognitive processing as well as corresponding brain responses.

Hippocampal Spike-Timing Correlations Lead to Hexagonal Grid Fields.

Space is represented in the mammalian brain by the activity of hippocampal place cells, as well as in their spike-timing correlations. Here, we propose a theory for how this temporal code is transformed to spatial firing rate patterns via spike-timing-dependent synaptic plasticity. The resulting dynamics of synaptic weights resembles well-known pattern formation models in which a lateral inhibition mechanism gives rise to a Turing instability. We identify parameter regimes in which hexagonal firing patterns develop as they have been found in medial entorhinal cortex.

Classification based hypothesis testing in neuroscience: Below-chance level classification rates and overlooked statistical properties of linear parametric classifiers.

Multivariate pattern analysis (MVPA) has recently become a popular tool for data analysis. Often, classification accuracy as quantified by correct classification rate (CCR) is used to illustrate the size of the effect under investigation. However, we show that in low sample size (LSS), low effect size (LES) data, which is typical in neuroscience, the distribution of CCRs from cross-validation of linear MVPA is asymmetric and can show classification rates considerably below what would be expected from chance classification. Conversely, the mode of the distribution in these cases is above expected chance levels, leading to a spuriously high number of above chance CCRs. This unexpected distribution has strong implications when using MVPA for hypothesis testing. Our analyses warrant the conclusion that CCRs do not well reflect the size of the effect under investigation. Moreover, the skewness of the null-distribution precludes the use of many standard parametric tests to assess significance of CCRs. We propose that MVPA results should be reported in terms of P values, which are estimated using randomization tests. Also, our results show that cross-validation procedures using a low number of folds, e.g. twofold, are generally more sensitive, even though the average CCRs are often considerably lower than those obtained using a higher number of folds. Hum Brain Mapp 37:1842-1855, 2016. © 2016 Wiley Periodicals, Inc.

Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change.

Synaptic change is a costly resource, particularly for brain structures that have a high demand of synaptic plasticity. For example, building memories of object positions requires efficient use of plasticity resources since objects can easily change their location in space and yet we can memorize object locations. But how should a neural circuit ideally be set up to integrate two input streams (object location and identity) in case the overall synaptic changes should be minimized during ongoing learning? This letter provides a theoretical framework on how the two input pathways should ideally be specified. Generally the model predicts that the information-rich pathway should be plastic and encoded sparsely, whereas the pathway conveying less information should be encoded densely and undergo learning only if a neuronal representation of a novel object has to be established. As an example, we consider hippocampal area CA1, which combines place and object information. The model thereby provides a normative account of hippocampal rate remapping, that is, modulations of place field activity by changes of local cues. It may as well be applicable to other brain areas (such as neocortical layer V) that learn combinatorial codes from multiple input streams.

Action potential generation in an anatomically constrained model of medial superior olive axons.

Neurons in the medial superior olive (MSO) encode interaural time differences (ITDs) with sustained firing rates of >100 Hz. They are able to generate such high firing rates for several hundred milliseconds despite their extremely low-input resistances of only few megaohms and high synaptic conductances in vivo. The biophysical mechanisms by which these leaky neurons maintain their excitability are not understood. Since action potentials (APs) are usually assumed to be generated in the axon initial segment (AIS), we analyzed anatomical data of proximal MSO axons in Mongolian gerbils and found that the axon diameter is <1 µm and the internode length is ∼100 µm. Using a morphologically constrained computational model of the MSO axon, we show that these thin axons facilitate the excitability of the AIS. However, for ongoing high rates of synaptic inputs the model generates a substantial fraction of APs in its nodes of Ranvier. These distally initiated APs are mediated by a spatial gradient of sodium channel inactivation and a strong somatic current sink. The model also predicts that distal AP initiation increases the dynamic range of the rate code for ITDs.

Hippocampal remapping is constrained by sparseness rather than capacity.

Grid cells in the medial entorhinal cortex encode space with firing fields that are arranged on the nodes of spatial hexagonal lattices. Potential candidates to read out the space information of this grid code and to combine it with other sensory cues are hippocampal place cells. In this paper, we investigate a population of grid cells providing feed-forward input to place cells. The capacity of the underlying synaptic transformation is determined by both spatial acuity and the number of different spatial environments that can be represented. The codes for different environments arise from phase shifts of the periodical entorhinal cortex patterns that induce a global remapping of hippocampal place fields, i.e., a new random assignment of place fields for each environment. If only a single environment is encoded, the grid code can be read out at high acuity with only few place cells. A surplus in place cells can be used to store a space code for more environments via remapping. The number of stored environments can be increased even more efficiently by stronger recurrent inhibition and by partitioning the place cell population such that learning affects only a small fraction of them in each environment. We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code. Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information. Both populations may encode different aspects of space.