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Our sense of touch emerges from an array of mechanosensory structures residing within the fabric of our skin. These tactile end organ structures convert innocuous forces acting on the skin into electrical signals that propagate to the CNS via the axons of low-threshold mechanoreceptors (LTMRs). Our rich capacity for tactile discrimination arises from the dissimilar intrinsic properties of the LTMR subtypes that innervate different regions of the skin and the structurally distinct end organ complexes with which they associate. These end organ structures comprise a range of non-neuronal cell types, which may themselves actively contribute to the transformation of tactile forces into neural impulses within the LTMR afferents. Although the mechanism and the site of transduction across end organs remain unclear, PIEZO2 has emerged as the principal mechanosensitive channel involved in light touch of the skin. Here we review the physiological properties of LTMR subtypes and discuss how features of their cutaneous end organ complexes shape subtype-specific tuning.Mammalian skin contains an array of specialized structures that transform mechanical forces into electrical signals. Handler and Ginty provide a comprehensive overview of the features of the skin’s mechanosensory end organs and the neurons with which they associate and consider how their diverse properties contribute to the sense of touch.

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain. Neuroscientists can measure activity from more neurons than ever before, garnering new insights and posing challenges to traditional theoretical frameworks. New frameworks may help researchers use these observations to shed light on brain function.

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits. The authors propose a synaptic plasticity rule for pyramidal neurons based on postsynaptic bursting that captures experimental data and solves the credit assignment problem for deep networks.

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically 1 . However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset—part of the Allen Brain Observatory 2 —that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas 3 . We find that four classical hierarchical measures—response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale—are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas. A large, open dataset containing parallel recordings from six visual cortical and two thalamic areas of the mouse brain is presented, from which the relative timing of activity in response to visual stimuli and behaviour is used to construct a hierarchy scheme that corresponds to anatomical connectivity data.

The brain is increasingly thought to predict sensory inputs, based on previous experience. In this Opinion article, Barrett and Simmons integrate this active inference account with an anatomical model of corticocortical connections, and describe how such a system may unify allostatic control and interoception within an integrated neural architecture. Intuition suggests that perception follows sensation and therefore bodily feelings originate in the body. However, recent evidence goes against this logic: interoceptive experience may largely reflect limbic predictions about the expected state of the body that are constrained by ascending visceral sensations. In this Opinion article, we introduce the Embodied Predictive Interoception Coding model, which integrates an anatomical model of corticocortical connections with Bayesian active inference principles, to propose that agranular visceromotor cortices contribute to interoception by issuing interoceptive predictions. We then discuss how disruptions in interoceptive predictions could function as a common vulnerability for mental and physical illness.

In the vertebrate visual system, all output of the retina is carried by retinal ganglion cells. Each type encodes distinct visual features in parallel for transmission to the brain. How many such ‘output channels’ exist and what each encodes are areas of intense debate. In the mouse, anatomical estimates range from 15 to 20 channels, and only a handful are functionally understood. By combining two-photon calcium imaging to obtain dense retinal recordings and unsupervised clustering of the resulting sample of more than 11,000 cells, here we show that the mouse retina harbours substantially more than 30 functional output channels. These include all known and several new ganglion cell types, as verified by genetic and anatomical criteria. Therefore, information channels from the mouse eye to the mouse brain are considerably more diverse than shown thus far by anatomical studies, suggesting an encoding strategy resembling that used in state-of-the-art artificial vision systems. Two-photon calcium imaging reveals that the mouse retina contains more than 30 functionally distinct retinal ganglion cells, including some that have not been described before, exceeding current estimates and suggesting that the functional diversity of retinal ganglion cells may be much larger than previously thought. Multiple retinal ganglion cell types Retinal ganglion cells (RGCs) convey visual information from the retina to the brain. How many types of RGC exist and how they should be classified have been long-standing questions. Thomas Euler and colleagues used two-photon calcium imaging to record responses to stimuli in more than 11,000 cells in a patch of the mouse ganglion cell layer, and applied unsupervised clustering of the resulting data. This revealed that the mouse retina harbours more than 30 distinct functional RGC types, including several that have not been described before. This number substantially exceeds current estimates and indicates that the functional diversity of RGCs is greater than previously thought.

The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure–function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.In this Review, Panzeri, Moroni, Safaai and Harvey explain how the levels and structures of correlations among the activity of neurons in a population shape information encoding, transmission and readout, and describe how future research could determine how the structures of correlations are optimized.

This paper describes an integrated approach for neuroimaging data acquisition, analysis and sharing. Building on methodological advances from the Human Connectome Project (HCP) and elsewhere, the HCP-style paradigm applies to new and existing data sets that meet core requirements and may accelerate progress in understanding the brain in health and disease. Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

Perceptual constancy requires the brain to maintain a stable representation of sensory input. In the olfactory system, activity in primary olfactory cortex (piriform cortex) is thought to determine odour identity 1 – 5 . Here we present the results of electrophysiological recordings of single units maintained over weeks to examine the stability of odour-evoked responses in mouse piriform cortex. Although activity in piriform cortex could be used to discriminate between odorants at any moment in time, odour-evoked responses drifted over periods of days to weeks. The performance of a linear classifier trained on the first recording day approached chance levels after 32 days. Fear conditioning did not stabilize odour-evoked responses. Daily exposure to the same odorant slowed the rate of drift, but when exposure was halted the rate increased again. This demonstration of continuous drift poses the question of the role of piriform cortex in odour perception. This instability might reflect the unstructured connectivity of piriform cortex 6 – 12 , and may be a property of other unstructured cortices. All odours elicit a unique pattern of neuronal activity in primary olfactory cortex but these patterns drift over time, posing a problem for the perceptual constancy of odours.

We have come to recognize the brain as a predictive organ, anticipating attributes of the incoming sensory stimulation to guide perception and action in the service of adaptive behaviour. In the quest to understand the neural bases of the modulatory prospective signals that prioritize and select relevant events during perception, one fundamental dimension has until recently been largely overlooked: time. In this Review, we introduce the burgeoning field of temporal attention and illustrate how the brain makes use of various forms of temporal regularities in the environment to guide adaptive behaviour and influence neural processing.