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Insects and vertebrates harbor specific neurons that encode the animal’s head direction (HD) and provide an internal compass for spatial navigation. Each HD cell fires most strongly in one preferred direction. As the animal turns its head, however, HD cells in rat anterodorsal thalamic nucleus (ADN) and other brain areas fire already before their preferred direction is reached, as if the neurons anticipated the future head direction. This phenomenon has been explained at a mechanistic level but a functional interpretation is still missing. To close this gap, we use a computational approach based on the animal’s movement statistics and a simple model for the behavior of the ADN head-direction network. Network activity is read out using population vectors in a biologically plausible manner, so that only past spikes are taken into account. We find that anticipatory firing improves the representation of the present HD by reducing the motion-induced temporal bias inherent in causal decoding. The amount of anticipation observed in ADN enhances the precision of the HD compass read-out by up to 40%. In addition, our framework predicts that neural integration times not only reflect biophysical constraints, but also the statistics of natural stimuli; anticipatory tuning should be found whenever neurons encode sensory signals that change gradually in time.Significance statementAcross different brain regions, populations of noisy neurons encode dynamically changing stimuli. Decoding a time-varying stimulus from the population response involves a trade-off: For short read-out times, stimulus estimates are unreliable as the number of stochastic spikes will be small; for long read-out times, estimates are biased because they lag behind the true stimulus. We show that optimal decoding relies not only on finding the right read-out time window, but requires neurons to anticipate future stimulus values. We apply this framework to the rodent head-direction system and show that the experimentally observed anticipation of future head directions can be explained at a quantitative level from the neuronal tuning properties, the network size, and the animal’s head-movement statistics.

Neurons in the auditory cortex are tuned to specific ranges of sound frequencies. Although the cellular and network mechanisms underlying neuronal sound frequency selectivity are well studied and reflect the interplay of thalamocortical and intracortical excitatory inputs and further refinement by cortical inhibition, the precise synaptic signaling mechanisms remain less understood. To gain further understanding on these mechanisms and their effects on sound-driven behavior, we usedin vivoimaging as well as behavioral approaches in awake and behaving female and male mice. We discovered that synaptic zinc, a modulator of neurotransmission and responsiveness to sound, sharpened the sound frequency tuning of principal and parvalbumin-expressing neurons and widened the sound frequency tuning of somatostatin-expressing inhibitory neurons in layer 2/3 of the primary auditory cortex. In the absence of cortical synaptic zinc, mice exhibited reduced acuity for detecting changes in sound frequencies. Together, our results reveal that cell-type-specific effects of zinc contribute to cortical sound frequency tuning and enhance acuity for sound frequency discrimination.SIGNIFICANCE STATEMENTNeuronal tuning to specific features of sensory stimuli is a fundamental property of cortical sensory processing that advantageously supports behavior. Despite the established roles of synaptic thalamocortical and intracortical excitation and inhibition in cortical tuning, the precise synaptic signaling mechanisms remain unknown. Here, we investigated these mechanisms in the mouse auditory cortex. We discovered a previously unknown signaling mechanism linking synaptic zinc signaling with cell-specific cortical tuning and enhancement in sound frequency discrimination acuity. Given the abundance of synaptic zinc in all sensory cortices, this newly discovered interaction between synaptic zinc and cortical tuning can provide a general mechanism for modulating neuronal stimulus specificity and sensory-driven behavior.

Channel-encoding models offer the ability to bridge different scales of neuronal measurement by interpreting population responses, typically measured with BOLD imaging in humans, as linear sums of groups of neurons (channels) tuned for visual stimulus properties. Inverting these models to form predicted channel responses from population measurements in humans seemingly offers the potential to infer neuronal tuning properties. Here, we test the ability to make inferences about neural tuning width from inverted encoding models. We examined contrast invariance of orientation selectivity in human V1 (both sexes) and found that inverting the encoding model resulted in channel response functions that became broader with lower contrast, thus apparently violating contrast invariance. Simulations showed that this broadening could be explained by contrast-invariant single-unit tuning with the measured decrease in response amplitude at lower contrast. The decrease in response lowers the signal-to-noise ratio of population responses that results in poorer population representation of orientation. Simulations further showed that increasing signal to noise makes channel response functions less sensitive to underlying neural tuning width, and in the limit of zero noise will reconstruct the channel function assumed by the model regardless of the bandwidth of single units. We conclude that our data are consistent with contrast-invariant orientation tuning in human V1. More generally, our results demonstrate that population selectivity measures obtained by encoding models can deviate substantially from the behavior of single units because they conflate neural tuning width and noise and are therefore better used to estimate the uncertainty of decoded stimulus properties.SIGNIFICANCE STATEMENTIt is widely recognized that perceptual experience arises from large populations of neurons, rather than a few single units. Yet, much theory and experiment have examined links between single units and perception. Encoding models offer a way to bridge this gap by explicitly interpreting population activity as the aggregate response of many single neurons with known tuning properties. Here we use this approach to examine contrast-invariant orientation tuning of human V1. We show with experiment and modeling that due to lower signal to noise, contrast-invariant orientation tuning of single units manifests in population response functions that broaden at lower contrast, rather than remain contrast-invariant. These results highlight the need for explicit quantitative modeling when making a reverse inference from population response profiles to single-unit responses.

Neuronal tuning, defined by the degree of selectivity to a specific stimulus, is a hallmark of cortical computation. Understanding the role of GABAergic interneurons in shaping cortical tuning is now possible with the ability to manipulate interneuron classes selectively. Here, we show that interneurons expressing vasoactive intestinal polypeptide (VIP+) regulate the spatial frequency (SF) tuning of pyramidal neurons in mouse visual cortex. Using two-photon calcium imaging and optogenetic manipulations of VIP+cell activity, we found that activating VIP+cells elicited a stronger network response to stimuli of higher SFs, whereas suppressing VIP+cells resulted in a network response shift toward lower SFs. These results establish that cortical inhibition modulates the spatial resolution of visual processing and add further evidence demonstrating that feature selectivity depends, not only on the feedforward excitatory projections into the cortex, but also on dynamic intracortical modulations by specific forms of inhibition.SIGNIFICANCE STATEMENTWe demonstrate that interneurons expressing vasoactive intestinal polypeptide (VIP+) play a causal role in regulating the spatial frequency (SF) tuning of neurons in mouse visual cortex. We show that optogenetic activation of VIP+cells results in a shift in network preference toward higher SFs, whereas suppressing them shifts the network toward lower SFs. Several studies have shown that VIP+cells are sensitive to neuromodulation and increase their firing during locomotion, whisking, and pupil dilation and are involved in spatially specific top-down modulation, reminiscent of the effects of top-down attention, and also that attention enhances spatial resolution. Our findings provide a bridge between these studies by establishing the inhibitory circuitry that regulates these fundamental modulations of SF in the cortex.

In rodents, the progression of extrastriate areas located laterally to primary visual cortex (V1) has been assigned to a putative object-processing pathway (homologous to the primate ventral stream), based on anatomical considerations. Recently, we found functional support for such attribution (Tafazoli et al., 2017), by showing that this cortical progression is specialized for coding object identity despite view changes – the hallmark property of a ventral-like pathway. Here, we sought to clarify what computations are at the base of such specialization. To this aim, we performed multi-electrode recordings from V1 and laterolateral area LL (at the apex of the putative ventral-like hierarchy) of male adult rats, during the presentation of drifting gratings and noise movies. We found that the extent to which neuronal responses were entrained to the phase of the gratings sharply dropped from V1 to LL, along with the quality of the receptive fields inferred through reverse correlation. Concomitantly, the tendency of neurons to respond to different oriented gratings increased, while the sharpness of orientation tuning declined. Critically, these trends are consistent with the nonlinear summation of visual inputs that is expected to take place along the ventral stream, according to the predictions of hierarchical models of ventral computations and a meta-analysis of the monkey literature. This suggests an intriguing homology between the mechanisms responsible for building up shape selectivity and transformation tolerance in the visual cortex of primates and rodents, reasserting the potential of the latter as models to investigate ventral stream functions at the circuitry level.Significance StatementDespite the growing popularity of rodents as models of visual functions, it remains unclear whether their visual cortex contains specialized modules for processing shape information. To addresses this question, we compared how neuronal tuning evolves from rat primary visual cortex (V1) to a downstream visual cortical region (area LL) that previous work has implicated in shape processing. In our experiments, LL neurons displayed a stronger tendency to respond to drifting gratings with different orientations, while maintaining a sustained response across the whole duration of the drift cycle. These trends match the increased complexity of pattern selectivity and the augmented tolerance to stimulus translation found in monkey visual temporal cortex, thus revealing a homology between shape processing in rodents and primates.

Repetition of a stimulus results in decreased responses in many cortical areas. This so-called adaptation or repetition suppression has been used in several human functional magnetic resonance imaging studies to deduce the stimulus selectivity of neuronal populations. We tested in macaque monkeys whether the degree of neural adaptation depends on the similarity between the adapter and test stimulus. To manipulate similarity, we varied stimulus size. We recorded the responses of single neurons to different-sized shapes in inferior temporal (IT) and prefrontal cortical (PFC) areas while the animals were engaged in a size or shape discrimination task. The degree of response adaptation in IT decreased with increasing size differences between the adapter and the test stimuli in both tasks, but the dependence of adaptation on the degree of similarity between the adapter and test stimuli was limited mainly to the early phase of the neural response in IT. PFC neurons showed only weak size-contingent repetition effects, despite strong size selectivity observed with the same stimuli. Thus, based on the repetition effects in PFC, one would have erroneously concluded that PFC shows weak or no size selectivity in such tasks. These findings are relevant for the interpretation of functional magnetic resonance adaptation data: they support the conjecture that the degree of adaptation scales with the similarity between adapter and test stimuli. However, they also show that the temporal evolution of adaptation during the course of the response, and differences in the way individual regions react to stimulus repetition, may complicate the inference of neuronal tuning from functional magnetic resonance adaptation.

How single neurons represent information about the magnitude of a stimulus remains controversial. Neurons encoding purely sensory magnitude typically show monotonic response functions (“summation coding”), and summation units are usually implemented in models of numerosity representation. In contrast, cells representing numerical quantity exhibit nonmonotonic tuning functions that peak at their preferred numerosity (“labeled-line code”), but the restricted range of tested quantities in these studies did not permit a definite answer. Here, we analyzed both behavioral and neuronal representations of a broad range of numerosities from 1 to 30 in the prefrontal cortex of monkeys. Numerosity-selective neurons showed a clear and behaviorally relevant labeled-line code for all numerosities. Moreover, both the behavioral and neuronal tuning functions obeyed the Weber–Fechner Law and were best represented on a nonlinearly compressed scale. Our single-cell study is in good agreement with functional imaging data reporting peaked tuning functions in humans, demonstrating neuronal precursors for human number competence in a nonhuman primate. Our findings also emphasize that the manner in which neurons encode and maintain magnitude information may depend on the precise task at hand as well as the type of magnitude to represent and memorize.

People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.

Neurons in the inferior temporal cortex (IT) of the macaque fire more strongly to some shapes than others, but little is known about how to characterize this shape tuning more generally, because most previous studies have used somewhat arbitrary variations in the stimuli with unspecified magnitudes of the changes. The present investigation studied the modulation of IT cells to nonaccidental property (NAP, i.e., invariant to orientations in depth) and metric property (MP, i.e., depth dependent) variations of dimensions of generalized cones (a general formalism for characterizing shapes hypothesized to mediate object recognition). Changes in an NAP resulted in greater neuronal modulation than equally large pixel-wise changes in an MP (including those consisting of a rotation in depth). There was also precise and highly systematic neuronal tuning to the quantitative variations of MPs along specific dimensions to which a neuron was sensitive. The NAP advantage was independent of whether the object was composed of only a single part or had two parts. These findings indicate that qualitative shape changes such as NAPs help explain the surplus amount of IT shape sensitivity that cannot be accounted for on the basis of metric or pixel-based changes alone. This NAP advantage may provide the neural basis for the greater detectability of NAP compared with MP changes in human psychophysics.