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Individuals with autism and individuals with dyslexia both show reduced use of previous sensory information (stimuli statistics) in perceptual tasks, even though these are very different neurodevelopmental disorders. To better understand how past sensory information influences the perceptual experience in these disorders, we first investigated the trial-by-trial performance of neurotypical participants in a serial discrimination task. Neurotypical participants overweighted recent stimuli, revealing fast updating of internal sensory models, which is adaptive in changing environments. They also weighted the detailed stimuli distribution inferred by longer-term accumulation of stimuli statistics, which is adaptive in stable environments. Compared to neurotypical participants, individuals with dyslexia weighted earlier stimuli less heavily, whereas individuals with autism spectrum disorder weighted recent stimuli less heavily. Investigating the dynamics of perceptual inference reveals that individuals with dyslexia rely more on information about the immediate past, whereas perception in individuals with autism is dominated by longer-term statistics.Lieder et al show that individuals with dyslexia and individuals with ASD rely mostly on recent and earlier perceptual information, respectively, during perceptual tasks. This may explain the unique difficulties associated with the two conditions.

Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and (M1) provide direct input to spinal circuitry and are critical for skilled movement control. Contrary to initial hypotheses, they can also be active during action observation, in the absence of any movement. A population-level understanding of this phenomenon is currently lacking. We recorded from single neurons, including identified PTNs, in (M1) (n = 187), and F5 (n = 115) as two adult male macaques executed, observed, or withheld (NoGo) reach-to-grasp actions. F5 maintained a similar representation of grasping actions during both execution and observation. In contrast, although many individual M1 neurons were active during observation, M1 population activity was distinct from execution, and more closely aligned to NoGo activity, suggesting this activity contributes to withholding of self-movement. M1 and its outputs may dissociate initiation of movement from representation of grasp in order to flexibly guide behaviour.

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.

Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging. Surface two-photon imaging of the brain cannot access somatic calcium signals of neurons from deep layers of the macaque cortex. Here, the authors present an implant and imaging system for chronic motion-stabilized two-photon imaging of dendritic calcium signals to drive an optical brain-computer interface in macaques.

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations. Our brains contain billions of neurons, which are continually producing electrical signals to relay information around the brain. Yet most of our knowledge of how the brain works comes from studying the activity of one neuron at a time. Recently, studies of multiple neurons have shown that they tend to be active together in short bursts called “up” states, which are followed by periods in which they are less active called “down” states. When we are sleeping or under a general anesthetic, the neurons may be completely silent during down states, but when we are awake the difference in activity between the two states is usually less extreme. However, it is still not clear how the neurons generate these patterns of activity. To address this question, Stringer et al. studied the activity of neurons in the brains of awake and anesthetized rats, mice and gerbils. The experiments recorded electrical activity from many neurons at the same time and found a wide range of different activity patterns. A computational model based on these data suggests that differences in the degree to which some neurons suppress the activity of other neurons may account for this variety. Increasing the strength of these inhibitory signals in the model decreased the fluctuations in electrical activity across entire areas of the brain. Further analysis of the experimental data supported the model’s predictions by showing that inhibitory neurons – which act to reduce electrical activity in other neurons – were more active when there were fewer fluctuations in activity across the brain. The next step following on from this work would be to develop ways to build computer models that can mimic the activity of many more neurons at the same time. The models could then be used to interpret the electrical activity produced by many different kinds of neuron. This will enable researchers to test more sophisticated hypotheses about how the brain works.

We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world.

A perceptual phenomenon is reported, whereby prior acoustic context has a large, rapid and long-lasting effect on a basic auditory judgement. Pairs of tones were devised to include ambiguous transitions between frequency components, such that listeners were equally likely to report an upward or downward ‘pitch’ shift between tones. We show that presenting context tones before the ambiguous pair almost fully determines the perceived direction of shift. The context effect generalizes to a wide range of temporal and spectral scales, encompassing the characteristics of most realistic auditory scenes. Magnetoencephalographic recordings show that a relative reduction in neural responsivity is correlated to the behavioural effect. Finally, a computational model reproduces behavioural results, by implementing a simple constraint of continuity for binding successive sounds in a probabilistic manner. Contextual processing, mediated by ubiquitous neural mechanisms such as adaptation, may be crucial to track complex sound sources over time. Perception can be swayed by prior context. Here the authors report an auditory illusion in which sounds with ambiguous pitch shifts are perceived as shifting upward or downward based on the preceding contextual sounds, explore the neural correlates, and propose a probabilistic model based on temporal binding.

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in domains ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.

This paper discusses the location bias and the spatial resolution in the reconstruction of a single dipole source by various spatial filtering techniques used for neuromagnetic imaging. We first analyze the location bias for several representative adaptive and non-adaptive spatial filters using their resolution kernels. This analysis theoretically validates previously reported empirical findings that standardized low-resolution electromagnetic tomography (sLORETA) has no location bias. We also find that the minimum-variance spatial filter does exhibit bias in the reconstructed location of a single source, but that this bias is eliminated by using the normalized lead field. We then focus on the comparison of sLORETA and the lead-field normalized minimum-variance spatial filter, and analyze the effect of noise on source location bias. We find that the signal-to-noise ratio (SNR) in the measurements determines whether the sLORETA reconstruction has source location bias, while the lead-field normalized minimum-variance spatial filter has no location bias even in the presence of noise. Finally, we compare the spatial resolution for sLORETA and the minimum-variance filter, and show that the minimum-variance filter attains much higher resolution than sLORETA does. The results of these analyses are validated by numerical experiments as well as by reconstructions based on two sets of evoked magnetic responses.

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as \"single-spike information\" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex.