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Scientists have long conjectured that the neocortex learns the structure of the environment in a predictive, hierarchical manner. According to this conjecture, expected, predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of information. It is theorized that the neocortex then changes the representation of incoming stimuli, guided by differences in the responses to expected and unexpected events. In line with this conjecture, different responses to expected and unexpected sensory features have been observed in spiking and somatic calcium events. However, it remains unknown whether these unexpected event signals occur in the distal apical dendrites where many top-down signals are received, and whether these signals govern subsequent changes in the brain’s stimulus representations. Here, we show that both somata and distal apical dendrites of cortical pyramidal neurons exhibit distinct unexpected event signals that systematically change over days. These findings were obtained by tracking the responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons over multiple days in primary visual cortex of awake, behaving mice using two-photon calcium imaging. Many neurons in both layers 2/3 and 5 showed large differences between their responses to expected and unexpected events. Interestingly, these responses evolved in opposite directions in the somata and distal apical dendrites. These differences between the somata and distal apical dendrites may be important for hierarchical computation, given that these two compartments tend to receive bottom-up and top-down information, respectively.

An open question in systems neuroscience is which objective function (or computational “goal”) best describes the computations performed by the ventral stream (VS) of primate visual cortex. Substantial past research has suggested that object categorization could be such a goal. Recent experiments, however, showed that information about object positions, sizes, etc. is encoded with increasing explicitness along this pathway. Because that information is not necessarily needed for object categorization, this motivated us to ask whether primate VS may do more than “just” object recognition. To address that question, we trained deep neural networks, all with the same architecture, with three different objectives: a supervised object categorization objective; an unsupervised autoencoder objective; and a semi-supervised objective that combined autoencoding with categorization. We then compared the image representations learned by these models to those observed in areas V4 and IT of macaque monkeys using canonical correlation analysis (CCA). We found that the semi-supervised model provided the best match the monkey data, followed closely by the unsupervised model, and more distantly by the supervised one. These results suggest that multiple objectives – including, critically, unsupervised ones – might be essential for explaining the computations performed by primate VS.

The retina encodes visual stimuli across light intensities spanning 10-12 orders of magnitude from starlight to sunlight. To accommodate this enormous range, adaptation alters retinal output, changing both the signal and noise among populations of retinal ganglion cells (RGCs). Here we determine how these light level-dependent changes in signal and noise impact decoding of retinal output. In particular, we consider the importance of accounting for noise correlations among RGCs to optimally read out retinal activity. We find that at moonlight conditions, correlated noise is greater and assuming independent noise severely diminished decoding performance. In fact, assuming independence among a local population of RGCs produced worse decoding than using a single RGC, demonstrating a failure of population codes when correlated noise is substantial and ignored. We generalize these results with a simple model to determine the signal and noise conditions under which this failure of population processing can occur. This work elucidates the circumstances in which accounting for noise correlations is necessary to take advantage of population-level codes and shows that sensory adaptation can strongly impact decoding requirements on downstream brain areas.

SummaryHow neural populations represent sensory information, and how that representation is transformed from one brain area to another, are fundamental questions of neuroscience. The dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1) represent two distinct stages of early visual processing. Classic sparse coding theories propose that V1 neurons represent local features of images. More recent theories have argued that the visual pathway transforms visual representations to become increasingly linearly separable. To test these ideas, we simultaneously recorded the spiking activity of mouse dLGN and V1 in vivo. We find strong evidence for both sparse coding and linear separability theories. Surprisingly, the correlations between neurons in V1 (but not dLGN) were shaped as to be irrelevant for stimulus decoding, a feature which we show enables linear separability. Therefore, our results suggest that the dLGN-V1 transformation reshapes correlated variability in a manner that facilitates linear decoding while producing a sparse code.

Simple stimuli have been critical to understanding neural population codes in sensory systems. Yet it remains necessary to determine the extent to which this understanding generalizes to more complex conditions. To examine this problem, we measured how populations of direction-selective ganglion cells (DSGCs) from the retinas of male and female mice respond to a global motion stimulus with its direction and speed changing dynamically. We then examined the encoding and decoding of motion direction in both individual and populations of DSGCs. Individual cells integrated global motion over ∼200 ms, and responses were tuned to direction. However, responses were sparse and broadly tuned, which severely limited decoding performance from small DSGC populations. In contrast, larger populations compensated for response sparsity, enabling decoding with high temporal precision (