A whole-cell recording database of neuromodulatory action in the adult neocortex

Background: The recent release of two large intracellular electrophysiological databases now allows high-dimensional systematic analysis of mechanisms of information processing in the neocortex. Here, to complement these efforts, we introduce a freely and publicly available database that provides a comparative insight into the role of various neuromodulatory transmitters in controlling neural information processing. Findings: A database of in vitro whole-cell patch-clamp recordings from primary somatosensory and motor cortices (layers 2/3) of the adult mice (2-15 months old) from both sexes is introduced. A total of 464 current-clamp experiments from identified excitatory and inhibitory neurons are provided. Experiments include recordings with (i) Step-and-Hold protocol during which the current was transiently held at 10 steps, gradually increasing in amplitude, (ii) 'Frozen Noise' injections that model the amplitude and time-varying nature of synaptic inputs to a neuron in biological networks. All experiments follow a within neuron across drug design which includes a vehicle control and a modulation of one of the following targets in the same neuron: dopamine and its receptors D1R, D2R, serotonin 5HT1f receptor, norepinephrine Alpha1, and acetylcholine M1 receptors. Conclusions: This dataset is the first to provide a systematic and comparative insight into the role of the selected neuromodulators in controlling cellular excitability. The data will help to mechanistically address how bottom-up information processing can be modulated, providing a reference for studying neural coding characteristics and revealing the contribution of neuromodulation to information processing.

Magnetic Stimulation Allows Focal Activation of the Mouse Cochlea

Cochlear implants (CIs) strive to restore hearing to those with severe to profound hearing loss by artificially stimulating the auditory nerve. While most CI users can understand speech in a quiet environment, hearing that utilizes complex neural coding (e.g., appreciating music) has proved elusive, probably because of the inability of CIs to create narrow regions of spectral activation. Several novel approaches have recently shown promise for improving spatial selectivity, but substantial design differences from conventional CIs will necessitate much additional safety testing before clinical viability is established. Outside the cochlea, magnetic stimulation from small coils (micro-coils) has been shown to confine activation more narrowly than that from conventional micro-electrodes, raising the possibility that coil-based stimulation of the cochlea could improve the spectral resolution of CIs. To explore this, we delivered magnetic stimulation from micro-coils to multiple locations of the cochlea and measured the spread of activation utilizing a multi-electrode array inserted into the inferior colliculus; responses to magnetic stimulation were compared to analogous experiments with conventional micro-electrodes as well as to the responses to auditory monotones. Encouragingly, the extent of activation with micro-coils was ~60% narrower than that from electric stimulation and largely similar to the spread arising from acoustic stimulation. The dynamic range of coils was more than three times larger than that of electrodes, further supporting a smaller spread of activation. While much additional testing is required, these results support the notion that coil-based CIs can produce a larger number of independent spectral channels and may therefore improve functional performance. Further, because coil-based devices are structurally similar to existing CIs, fewer impediments to clinical translational are likely to arise.

Multiplexed and flexible neural coding in sensory, parietal, and frontal cortices during goal-directed virtual navigation

We do not understand how neural nodes operate within the recurrent action-perception loops that characterize naturalistic self-environment interactions, nor how brain networks reconfigure during changing computational demands. Here, we record local field potentials (LFPs) and spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and dorsolateral prefrontal cortex (dlPFC) as monkeys navigate in virtual reality to “catch fireflies”. This task requires animals to actively sample from a closed-loop visual environment while concurrently computing latent variables: the evolving distance and angle to a memorized firefly. We observed mixed selectivity in all areas, with even a traditionally sensory area (MSTd) tracking latent variables. Strikingly, global encoding profiles and unit-to-unit coupling suggested a functional subnetwork between MSTd and dlPFC, and not between these areas and 7a, as anatomy would suggest. When sensory evidence was rendered scarce, lateral connectivity through neuron-to-neuron coupling within MSTd strengthened but its pattern remained fixed, while neuronal coupling adaptively remapped within 7a and dlPFC. The larger the remapping in 7a/dlPFC and the greater the stability within MSTd, the less was behavior impacted by loss of sensory evidence. These results highlight the distributed nature of neural coding during closed-loop action-perception naturalistic behaviors and suggest internal models may be housed in the pattern of fine-grain lateral connectivity within parietal and frontal cortices.

Ramp-shaped neural tuning supports graded population-level representation of the object-to-scene continuum

We can easily perceive the spatial scale depicted in a picture, regardless of whether it is a small space (e.g., a close-up view of a chair) or a much larger space (e.g., an entire class room). How does the human visual system encode this continuous dimension? Here, we investigated the underlying neural coding of depicted spatial scale, by examining the voxel tuning and topographic organization of brain responses. We created naturalistic yet carefully-controlled stimuli by constructing virtual indoor environments, and rendered a series of snapshots to smoothly sample between a close-up view of the central object and far-scale view of the full environment (object-to-scene continuum). Human brain responses were measured to each position using functional magnetic resonance imaging. We did not find evidence for a smooth topographic mapping for the object-to-scene continuum on the cortex. Instead, we observed large swaths of cortex with opposing ramp-shaped profiles, with highest responses to one end of the object-to-scene continuum or the other, and a small region showing a weak tuning to intermediate scale views. Importantly, when we considered the multi-voxel patterns of the entire ventral occipito-temporal cortex, we found smooth and linear representation of the object-to-scene continuum. Thus, our results together suggest that depicted spatial scale is coded parametrically in large-scale population codes across the entire ventral occipito-temporal cortex.

Contribution of behavioural variability to representational drift

Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. While the drift of these representations is mostly characterized in relation to external stimuli or tasks, behavioural or internal state of the animal is also known to modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational drift. By analysing publicly available datasets from the Allen Brain Observatory, we found that behavioural variability significantly contributes to changes in stimulus-induced neuronal responses across various cortical areas in the mouse. This effect could not be explained by a gain model in which change in the behavioural state scaled the signal or the noise. A better explanation was provided by a model in which behaviour contributed independently to neuronal tuning. Our results are consistent with a view in which behaviour modulates the low-dimensional, slowly-changing setpoints of neurons, upon which faster operations like sensory processing are performed. Importantly, our analysis suggests that reliable but variable behavioural signals might be misinterpreted as representational drift, if neuronal representations are only characterized in the stimulus space and marginalised over behavioural parameters.

Neural correlates of individual odor preference in Drosophila

Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical underpinnings of this individuality. Drosophila olfaction is an ideal system for discovering the origins of behavioral individuality among genetically identical individuals. The fly olfactory circuit is well-characterized and stereotyped, yet stable idiosyncrasies in odor preference, neural coding, and neural wiring are present and may be relevant to behavior. Using paired behavior and two-photon imaging measurements, we show that individual odor preferences in odor-vs-air and odor-vs-odor assays are predicted by idiosyncratic calcium dynamics in Olfactory Receptor Neurons (ORNs) and Projection Neurons (PNs), respectively. This suggests that circuit variation at the sensory periphery determines individual odor preferences. Furthermore, paired behavior and immunohistochemistry measurements reveal that variation in ORN presynaptic density also predicts odor-vs-odor preference. This point in the olfactory circuit appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. To unify these results, we constructed a leaky-integrate-and-fire model of 3,062 neurons in the antennal lobe. In these simulations, stochastic fluctuations at the glomerular level, like those observed in our ORN immunohistochemistry, produce variation in PN calcium responses with the same structure as we observed experimentally, the very structure that predicts idiosyncratic behavior. Thus, our results demonstrate how minute physiological and structural variations in a neural circuit may produce individual behavior, even when genetics and environment are held constant.

A neuronal prospect theory model in the brain reward circuitry

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, reflects prospect theory remains unknown. Here, we show with theoretical accuracy equivalent to that of human neuroimaging studies that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards, parameterized as a multiplicative combination of a utility and probability weighting functions in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reliably reconstructed risk preferences and subjective probability perceptions revealed by the animals' choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders

Proprioception is one of the least understood senses yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a variational autoencoder with topographic lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry PDs. 2. Few neurons will encode just a single joint. Topo-VAE provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with application the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.

Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks

Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants’ memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.