Cooperative thalamocortical circuit mechanism for sensory prediction errors.

The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and the outcomes of our actions. Discrepancies between expected and actual events, referred to as prediction errors, are leveraged to update the internal model and guide our attention towards unexpected events1-10. Despite the importance of prediction-error signals for various neural computations across the brain, surprisingly little is known about the neural circuit mechanisms responsible for their implementation. Here we describe a thalamocortical disinhibitory circuit that is required for generating sensory prediction-error signals in mouse primary visual cortex (V1). We show that violating animals' predictions by an unexpected visual stimulus preferentially boosts responses of the layer 2/3 V1 neurons that are most selective for that stimulus. Prediction errors specifically amplify the unexpected visual input, rather than representing non-specific surprise or difference signals about how the visual input deviates from the animal's predictions. This selective amplification is implemented by a cooperative mechanism requiring thalamic input from the pulvinar and cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response to prediction errors, VIP neurons inhibit a specific subpopulation of somatostatin-expressing inhibitory interneurons that gate excitatory pulvinar input to V1, resulting in specific pulvinar-driven response amplification of the most stimulus-selective neurons in V1. Therefore, the brain prioritizes unpredicted sensory information by selectively increasing the salience of unpredicted sensory features through the synergistic interaction of thalamic input and neocortical disinhibitory circuits.

Functional specificity of recurrent inhibition in visual cortex.

In the neocortex, neural activity is shaped by the interaction of excitatory and inhibitory neurons, defined by the organization of their synaptic connections. Although connections among excitatory pyramidal neurons are sparse and functionally tuned, inhibitory connectivity is thought to be dense and largely unstructured. By measuring in vivo visual responses and synaptic connectivity of parvalbumin-expressing (PV+) inhibitory cells in mouse primary visual cortex, we show that the synaptic weights of their connections to nearby pyramidal neurons are specifically tuned according to the similarity of the cells' responses. Individual PV+ cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This structured organization of inhibitory synaptic weights provides a circuit mechanism for tuned inhibition onto pyramidal cells despite dense connectivity, stabilizing activity within feature-specific excitatory ensembles while supporting competition between them.

Learning shapes cortical dynamics to enhance integration of relevant sensory input.

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

The caudal prethalamus: Inhibitory switchboard for behavioral control?

Prethalamic nuclei in the mammalian brain include the zona incerta, the ventral lateral geniculate nucleus, and the intergeniculate leaflet, which provide long-range inhibition to many targets in the midbrain, hindbrain, and thalamus. These nuclei in the caudal prethalamus can integrate sensory and non-sensory information, and together they exert powerful inhibitory control over a wide range of brain functions and behaviors that encompass most aspects of the behavioral repertoire of mammals, including sleep, circadian rhythms, feeding, drinking, predator avoidance, and exploration. In this perspective, we highlight the evidence for this wide-ranging control and lay out the hypothesis that one role of caudal prethalamic nuclei may be that of a behavioral switchboard that-depending on the sensory input, the behavioral context, and the state of the animal-can promote a behavioral strategy and suppress alternative, competing behaviors by modulating inhibitory drive onto diverse target areas.

Dynamic causal communication channels between neocortical areas.

Processing of sensory information depends on the interactions between hierarchically connected neocortical regions, but it remains unclear how the activity in one area causally influences the activity dynamics in another and how rapidly such interactions change with time. Here, we show that the communication between the primary visual cortex (V1) and high-order visual area LM is context-dependent and surprisingly dynamic over time. By momentarily silencing one area while recording activity in the other, we find that both areas reliably affected changing subpopulations of target neurons within one hundred milliseconds while mice observed a visual stimulus. The influence of LM feedback on V1 responses became even more dynamic when the visual stimuli predicted a reward, causing fast changes in the geometry of V1 population activity and affecting stimulus coding in a context-dependent manner. Therefore, the functional interactions between cortical areas are not static but unfold through rapidly shifting communication subspaces whose dynamics depend on context when processing sensory information.

Learning and attention increase visual response selectivity through distinct mechanisms.

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance and across seconds when animals switch attention. While both phenomena occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales.

Flexible inhibitory control of visually evoked defensive behavior by the ventral lateral geniculate nucleus.

Animals can choose to act upon, or to ignore, sensory stimuli, depending on circumstance and prior knowledge. This flexibility is thought to depend on neural inhibition, through suppression of inappropriate and disinhibition of appropriate actions. Here, we identified the ventral lateral geniculate nucleus (vLGN), an inhibitory prethalamic area, as a critical node for control of visually evoked defensive responses in mice. The activity of vLGN projections to the medial superior colliculus (mSC) is modulated by previous experience of threatening stimuli, tracks the perceived threat level in the environment, and is low prior to escape from a visual threat. Optogenetic stimulation of the vLGN abolishes escape responses, and suppressing its activity lowers the threshold for escape and increases risk-avoidance behavior. The vLGN most strongly affects visual threat responses, potentially via modality-specific inhibition of mSC circuits. Thus, inhibitory vLGN circuits control defensive behavior, depending on an animal's prior experience and its anticipation of danger in the environment.

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.

Visual intracortical and transthalamic pathways carry distinct information to cortical areas.

Sensory processing involves information flow between neocortical areas, assumed to rely on direct intracortical projections. However, cortical areas may also communicate indirectly via higher-order nuclei in the thalamus, such as the pulvinar or lateral posterior nucleus (LP) in the visual system of rodents. The fine-scale organization and function of these cortico-thalamo-cortical pathways remains unclear. We find that responses of mouse LP neurons projecting to higher visual areas likely derive from feedforward input from primary visual cortex (V1) combined with information from many cortical and subcortical areas, including superior colliculus. Signals from LP projections to different higher visual areas are tuned to specific features of visual stimuli and their locomotor context, distinct from the signals carried by direct intracortical projections from V1. Thus, visual transthalamic pathways are functionally specific to their cortical target, different from feedforward cortical pathways, and combine information from multiple brain regions, linking sensory signals with behavioral context.

Standardized and reproducible measurement of decision-making in mice.

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

In science, it is of vital importance that multiple studies corroborate the same result. Researchers therefore need to know all the details of previous experiments in order to implement the procedures as exactly as possible. However, this is becoming a major problem in neuroscience, as animal studies of behavior have proven to be hard to reproduce, and most experiments are never replicated by other laboratories. Mice are increasingly being used to study the neural mechanisms of decision making, taking advantage of the genetic, imaging and physiological tools that are available for mouse brains. Yet, the lack of standardized behavioral assays is leading to inconsistent results between laboratories. This makes it challenging to carry out large-scale collaborations which have led to massive breakthroughs in other fields such as physics and genetics. To help make these studies more reproducible, the International Brain Laboratory (a collaborative research group) et al. developed a standardized approach for investigating decision making in mice that incorporates every step of the process; from the training protocol to the software used to analyze the data. In the experiment, mice were shown images with different contrast and had to indicate, using a steering wheel, whether it appeared on their right or left. The mice then received a drop of sugar water for every correction decision. When the image contrast was high, mice could rely on their vision. However, when the image contrast was very low or zero, they needed to consider the information of previous trials and choose the side that had recently appeared more frequently. This method was used to train 140 mice in seven laboratories from three different countries. The results showed that learning speed was different across mice and laboratories, but once training was complete the mice behaved consistently, relying on visual stimuli or experiences to guide their choices in a similar way. These results show that complex behaviors in mice can be reproduced across multiple laboratories, providing an unprecedented dataset and open-access tools for studying decision making. This work could serve as a foundation for other groups, paving the way to a more collaborative approach in the field of neuroscience that could help to tackle complex research challenges.

Dispersion analysis of the orthorhombic peptide single crystal Z-(Aib)3-L-Ala-OtBu.

We present a dispersion analysis of the orthorhombic peptide single crystal Z-Aib-Aib-Aib-L-Ala-OtBu (N-benzyloxycarbonyl-α-aminoisobutyryl-α-aminoisobutyryl-α-aminoisobutyryl-L-alanine tert-butyl ester, C27H42N4O7), where Z is benzyloxycarbonyl and OtBu is the tert-butylester) in the MIR spectral region by means of adapted generalized dispersion analysis, employing the naturally grown crystal faces. Based on the results we identify the orientation of the crystal axes a, b, c within the sample, and supported by a stereographic projection of the crystal, we assign the individual axes. The gained dielectric tensor function and the oscillator parameters were confirmed by forward calculation of reflection spectra of different orientations. The orientation of the crystal axes was verified by a second stereographic projection with another crystal face in the center.

Deviations from Beer's law on the microscale - nonadditivity of absorption cross sections.

Beer's law assumes a linear dependence of absorbance on concentration, accordingly the index of absorption and the molar attenuation coefficient are material properties and the absorption cross section, including absorbance itself, must be additive if chemical interactions are excluded. Under the "no interaction" condition, a linear dependence should also exist between macroscopic polarization and the number of induced dipole moments per unit volume. The latter linear dependence is the basis for dispersion theory. Invoking Maxwell's wave equation, Beer's law has been derived recently from dispersion theory. As a result, Beer's law is a limiting law. Accordingly, indices of absorption and molar attenuation coefficients including absorption cross sections are no material properties and the latter can per se not be additive. Indeed, as we show in this contribution, not even for particles very small compared to wavelength, where the scattering cross sections can be neglected, is this additivity a given, except for comparably large distances between the particles. We investigate the magnitude of these critical distances with the help of finite difference time domain calculations for amorphous SiO2 spheres in the infrared spectral range. Based on electric field maps, we conclude that the deviations scale with oscillator strengths and, correspondingly, with local electric fields and nearfield effects.

Complete dispersion analysis of single crystal EDDt.

We present the first complete dispersion analysis of the organic ethylenediamine-d-tartrate (EDDt) in single crystal form in the spectral range of 5000-100 cm-1. The obtained oscillator parameters were used to generate the polarized and the cross-polarized spectra of randomly oriented polycrystalline EDDt. The comparison of the generated with the experimental spectra confirms the value of obtained oscillator parameters and dielectric tensor function.

Dispersion analysis of sodium dichromate dihydrate Na2Cr2O7·2H2O single crystal.

A single crystal of monoclinic sodium dichromate dihydrate was investigated by FTIR-spectroscopy in the spectral region of 4000-80 cm-1. On the measured reflection spectra dispersion analysis was performed to gain the oscillator parameters of the transition dipole moments of Na2Cr2O7·2H2O and by that the dielectric tensor function. The gained oscillator parameters were verified by forward-calculation of spectra using different crystal faces.

Generalized dispersion analysis of crystals with unknown symmetry and orientation.

With generalized dispersion analysis it is possible to gain the dispersion parameters and dielectric tensor function of crystals with not only unknown orientation of the symmetry elements in within the sample but with also unknown crystal symmetry. The developed procedure is applied to two spectra sets of each crystal symmetry type to cover various levels of difficulty in the evaluation with generalized dispersion analysis.

Contextual signals in visual cortex.

Vision is an active process. What we perceive strongly depends on our actions, intentions and expectations. During visual processing, these internal signals therefore need to be integrated with the visual information from the retina. The mechanisms of how this is achieved by the visual system are still poorly understood. Advances in recording and manipulating neuronal activity in specific cell types and axonal projections together with tools for circuit tracing are beginning to shed light on the neuronal circuit mechanisms of how internal, contextual signals shape sensory representations. Here we review recent work, primarily in mice, that has advanced our understanding of these processes, focusing on contextual signals related to locomotion, behavioural relevance and predictions.

Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex.

How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR-PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR-SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.

Synaptic organization of visual space in primary visual cortex.

How a sensory stimulus is processed and perceived depends on the surrounding sensory scene. In the visual cortex, contextual signals can be conveyed by an extensive network of intra- and inter-areal excitatory connections that link neurons representing stimulus features separated in visual space. However, the connectional logic of visual contextual inputs remains unknown; it is not clear what information individual neurons receive from different parts of the visual field, nor how this input relates to the visual features that a neuron encodes, defined by its spatial receptive field. Here we determine the organization of excitatory synaptic inputs responding to different locations in the visual scene by mapping spatial receptive fields in dendritic spines of mouse visual cortex neurons using two-photon calcium imaging. We find that neurons receive functionally diverse inputs from extended regions of visual space. Inputs representing similar visual features from the same location in visual space are more likely to cluster on neighbouring spines. Inputs from visual field regions beyond the receptive field of the postsynaptic neuron often synapse on higher-order dendritic branches. These putative long-range inputs are more frequent and more likely to share the preference for oriented edges with the postsynaptic neuron when the receptive field of the input is spatially displaced along the axis of the receptive field orientation of the postsynaptic neuron. Therefore, the connectivity between neurons with displaced receptive fields obeys a specific rule, whereby they connect preferentially when their receptive fields are co-oriented and co-axially aligned. This organization of synaptic connectivity is ideally suited for the amplification of elongated edges, which are enriched in the visual environment, and thus provides a potential substrate for contour integration and object grouping.

Generalized dispersion analysis of arbitrarily cut monoclinic crystals.

Dispersion analysis is applicable to arbitrarily cut monoclinic crystals of unknown orientation in order to find the symmetry axis. By this it is possible to differentiate between the transition moments oriented parallel and normal to the b-axis and to determine the dielectric tensor functions of those two principal directions. Dispersion analysis of arbitrarily cut monoclinic crystals is based on an extension of the evaluation scheme developed for arbitrarily cut orthorhombic crystals. We present dispersion analysis of monoclinic crystals exemplarily on spodumene (LiAl(SiO3)2) and yttrium orthosilicate (Y2SiO5).