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Predictable sensory stimuli do not evoke significant responses in a subset of cortical excitatory neurons. Some of those neurons, however, change their activity upon mismatches between actual and predicted stimuli. Different variants of these prediction-error neurons exist, and they differ in their responses to unexpected sensory stimuli. However, it is unclear how these variants can develop and coexist in the same recurrent network and how they are simultaneously shaped by the astonishing diversity of inhibitory interneurons. Here, we study these questions in a computational network model with three types of inhibitory interneurons. We find that balancing excitation and inhibition in multiple pathways gives rise to heterogeneous prediction-error circuits. Dependent on the network’s initial connectivity and distribution of actual and predicted sensory inputs, these circuits can form different variants of prediction-error neurons that are robust to network perturbations and generalize to stimuli not seen during learning. These variants can be learned simultaneously via homeostatic inhibitory plasticity with low baseline firing rates. Finally, we demonstrate that prediction-error neurons can support biased perception, we illustrate a number of functional implications, and we discuss testable predictions.

Animals with compact sensory systems face an encoding problem where a small number of sensory neurons are required to encode information about its surrounding complex environment. Using Caenorhabditis elegans worms as a model, we ask how chemical stimuli are encoded by a small and highly connected sensory system. We first generated a comprehensive library of transgenic worms where each animal expresses a genetically encoded calcium indicator in individual sensory neurons. This library includes the vast majority of the sensory system in C. elegans . Imaging from individual sensory neurons while subjecting the worms to various stimuli allowed us to compile a comprehensive functional map of the sensory system at single neuron resolution. The functional map reveals that despite the dense wiring, chemosensory neurons represent the environment using sparse codes. Moreover, although anatomically closely connected, chemo- and mechano-sensory neurons are functionally segregated. In addition, the code is hierarchical, where few neurons participate in encoding multiple cues, whereas other sensory neurons are stimulus specific. This encoding strategy may have evolved to mitigate the constraints of a compact sensory system. Significance We investigated how a numerically and spatially compact nematode nervous system encodes information about the world. A library of transgenic worms expressing a genetically encoded calcium indicator in each type of sensory neuron was constructed and used to assay neural activity in response to various chemical stimuli to compile a functional map of a sensory system. We find that the sensory system uses hierarchical sparse coding, a strategy that mitigates the limited size and the shallow structure of the neural network. Also, this is a timely study that significantly adds to the communal effort and enthusiasm in obtaining functional maps of the connectome.

Animals must encode fundamental physical relationships in their brains. A heron plunging its head underwater to skewer a fish must correct for light refraction, an archerfish shooting down an insect must “consider” gravity, and an echolocating bat that is attacking prey must account for the speed of sound in order to assess its distance. Do animals learn these relations or are they encoded innately and can they adjust them as adults are all open questions. We addressed this question by shifting the speed of sound and assessing the sensory behavior of a bat species that naturally experiences different speeds of sound. We found that both newborn pups and adults are unable to adjust to this shift, suggesting that the speed of sound is innately encoded in the bat brain. Moreover, our results suggest that bats encode the world in terms of time and do not translate time into distance. Our results shed light on the evolution of innate and flexible sensory perception.

To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory–inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images, and this was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding and paves the road to map the perturbome of neuronal networks in future studies.

In color vision, the quantitative rules for mixing lights to make a target color are well understood. By contrast, the rules for mixing odorants to make a target odor remain elusive. A solution to this problem in vision relied on characterizing receptor responses to different wavelengths of light and subsequently relating these responses to perception. In olfaction, experimentally measuring receptor responses to a representative set of complex mixtures is intractable due to the vast number of possibilities. To meet this challenge, we develop a biophysical model that predicts mammalian receptor responses to complex mixtures using responses to single odorants. The dominant nonlinearity in our model is competitive binding (CB): Only one odorant molecule can attach to a receptor binding site at a time. This simple framework predicts receptor responses to mixtures of up to 12 monomolecular odorants to within 15% of experimental observations and provides a powerful method for leveraging limited experimental data. Simple extensions of our model describe phenomena such as synergy, overshadowing, and inhibition. We demonstrate that the presence of such interactions can be identified via systematic deviations from the competitive-binding model.

Despite decades of research, we lack a mechanistic framework capable of predicting how movement-related signals are transformed into the diversity of muscle spindle afferent firing patterns observed experimentally, particularly in naturalistic behaviors. Here, a biophysical model demonstrates that well-known firing characteristics of mammalian muscle spindle Ia afferents – including movement history dependence, and nonlinear scaling with muscle stretch velocity – emerge from first principles of muscle contractile mechanics. Further, mechanical interactions of the muscle spindle with muscle-tendon dynamics reveal how motor commands to the muscle (alpha drive) versus muscle spindle (gamma drive) can cause highly variable and complex activity during active muscle contraction and muscle stretch that defy simple explanation. Depending on the neuromechanical conditions, the muscle spindle model output appears to ‘encode’ aspects of muscle force, yank, length, stiffness, velocity, and/or acceleration, providing an extendable, multiscale, biophysical framework for understanding and predicting proprioceptive sensory signals in health and disease.

Predictive processing, a leading theoretical framework for sensory processing, suggests that the brain constantly generates predictions on the sensory world and that perception emerges from the comparison between these predictions and the actual sensory input. This requires two distinct neural elements: generative units, which encode the model of the sensory world; and prediction error units, which compare these predictions against the sensory input. Although predictive processing is generally portrayed as a theory of cerebral cortex function, animal and human studies over the last decade have robustly shown the ubiquitous presence of prediction error responses in several nuclei of the auditory, somatosensory, and visual subcortical pathways. In the auditory modality, prediction error is typically elicited using so-called oddball paradigms, where sequences of repeated pure tones with the same pitch are at unpredictable intervals substituted by a tone of deviant frequency. Repeated sounds become predictable promptly and elicit decreasing prediction error; deviant tones break these predictions and elicit large prediction errors. The simplicity of the rules inducing predictability make oddball paradigms agnostic about the origin of the predictions. Here, we introduce two possible models of the organizational topology of the predictive processing auditory network: (1) the global view, that assumes that predictions on the sensory input are generated at high-order levels of the cerebral cortex and transmitted in a cascade of generative models to the subcortical sensory pathways; and (2) the local view, that assumes that independent local models, computed using local information, are used to perform predictions at each processing stage. In the global view information encoding is optimized globally but biases sensory representations along the entire brain according to the subjective views of the observer. The local view results in a diminished coding efficiency, but guarantees in return a robust encoding of the features of sensory input at each processing stage. Although most experimental results to-date are ambiguous in this respect, recent evidence favors the global model.

Significance A six-layered neocortex is a hallmark feature of the mammalian brain. Connections among layers and progressive changes in the neural coding properties of each layer define a microcircuit thought to perform the computations underlying complex behavior. Birds lack a six-layered cortex. Yet, they demonstrate complex cognition and behavior. Recent anatomical studies propose that adjacent regions of the avian pallium are homologs of neocortical layers. Here, we show that the avian auditory pallium exhibits the same information-processing principles that define the mammalian neocortical microcircuit. Results suggest that the cortical microcircuit evolved in a common ancestor of mammals and birds and provide a physiological explanation for neural processes that give rise to complex behavior in the absence of cortical lamination. Mammalian neocortex is characterized by a layered architecture and a common or “canonical” microcircuit governing information flow among layers. This microcircuit is thought to underlie the computations required for complex behavior. Despite the absence of a six-layered cortex, birds are capable of complex cognition and behavior. In addition, the avian auditory pallium is composed of adjacent information-processing regions with genetically identified neuron types and projections among regions comparable with those found in the neocortex. Here, we show that the avian auditory pallium exhibits the same information-processing principles that define the canonical cortical microcircuit, long thought to have evolved only in mammals. These results suggest that the canonical cortical microcircuit evolved in a common ancestor of mammals and birds and provide a physiological explanation for the evolution of neural processes that give rise to complex behavior in the absence of cortical lamination.

To perform adaptive behaviours, animals have to establish a representation of the physical “outside” world. How these representations are created by sensory systems is a central issue in sensory physiology. This review addresses the history of experimental approaches toward ideas about sensory coding, using the relatively simple auditory system of acoustic insects. I will discuss the empirical evidence in support of Barlow’s “efficient coding hypothesis”, which argues that the coding properties of neurons undergo specific adaptations that allow insects to detect biologically important acoustic stimuli. This hypothesis opposes the view that the sensory systems of receivers are biased as a result of their phylogeny, which finally determine whether a sound stimulus elicits a behavioural response. Acoustic signals are often transmitted over considerable distances in complex physical environments with high noise levels, resulting in degradation of the temporal pattern of stimuli, unpredictable attenuation, reduced signal-to-noise levels, and degradation of cues used for sound localisation. Thus, a more naturalistic view of sensory coding must be taken, since the signals as broadcast by signallers are rarely equivalent to the effective stimuli encoded by the sensory system of receivers. The consequences of the environmental conditions for sensory coding are discussed.

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.