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Cortical neurons show distinct firing patterns across multiple task epochs characterized by different computations. Recent studies suggest that such distinct patterns underlie dynamic population code achieving computational flexibility, whereas neurons in some cortical areas often show coherent firing patterns across epochs. To understand how coherent single-neuron code contributes to dynamic population code, we analyzed neural responses in the rat perirhinal cortex (PRC) during cue and reward epochs of a two-alternative forced-choice task. We found that the PRC neurons often encoded the opposite choice directions between those epochs. By using principal component analysis as a population-level analysis, we identified neural subspaces associated with each epoch, which reflected coordination across the neurons. The cue and reward epochs shared neural dimensions where the choice directions were consistently discriminated. Interestingly, those dimensions were supported by dynamically changing contributions of the individual neurons. These results demonstrated heterogeneity of coherent single-neuron representations in their contributions to population code., Tomoya Ohnuki et al. show that individual neurons in the rat perirhinal cortex coherently encoded choice directions across cue and reward epochs. Individual neurons also displayed dynamically changing contributions to population encodings between these epochs, suggesting that they flexibly coordinate to support computations associated with different epochs while holding temporally coherent representations.

Object-vector (OV) cells are cells in the medial entorhinal cortex (MEC) that track an animal’s distance and direction to objects in the environment. Their firing fields are defined by vectorial relationships to free-standing 3-dimensional (3D) objects of a variety of identities and shapes. However, the natural world contains a panorama of objects, ranging from discrete 3D items to flat two-dimensional (2D) surfaces, and it remains unclear what are the most fundamental features of objects that drive vectorial responses. Here we address this question by systematically changing features of experimental objects. Using an algorithm that robustly identifies OV firing fields, we show that the cells respond to a variety of 2D surfaces, with visual contrast as the most basic visual feature to elicit neural responses. The findings suggest that OV cells use plain visual features as vectorial anchoring points, allowing vector-guided navigation to proceed in environments with few free-standing landmarks., In order to understand more about which object features are the most influential when driving vectorial responses, Andersson et al. use an algorithm to reliably examine object-vector (OV) cells in mice. Their findings suggest that OV cells use visual features as anchoring points, allowing vector-guided navigation to proceed with reference to a wide range of visual landmarks.

Episodic memory, an essential element of orderly thinking, requires the organization of serial events into narratives about the identity of cues along with their locations and temporal order (what, where, and when). The hippocampus plays a central role in the acquisition and retrieval of episodes with two of its subsystems being separately linked to what and where information. The substrates for the third element are poorly understood. Here we report that in hippocampal slices field CA3 maintains self-sustained activity for remarkable periods following a brief input and that this effect is extremely sensitive to minor network perturbations. Using behavioral tests, that do not involve training or explicit rewards, we show that partial silencing of the CA3 commissural/associational network in mice blocks acquisition of temporal order, but not the identity or location, of odors. These results suggest a solution to the question of how hippocampus adds time to episodic memories., Brittney Cox, Conor Cox, Ben Gunn et al. show that brief stimulation of the commissural/associational fibers elicits extended periods of reverberating activity in the CA3 hippocampal region ex vivo. Partial silencing of the CA3 network in vivo inhibits the acquisition of temporal, but not identity or location, information in mice.

When measuring sparseness in neural populations as an indicator of efficient coding, an implicit assumption is that each stimulus activates a different random set of neurons. In other words, population responses to different stimuli are, on average, uncorrelated. Here we examine neurophysiological data from four lobes of macaque monkey cortex, including V1, V2, MT, anterior inferotemporal cortex, lateral intraparietal cortex, the frontal eye fields, and perirhinal cortex, to determine how correlated population responses are. We call the mean correlation the pseudosparseness index, because high pseudosparseness can mimic statistical properties of sparseness without being authentically sparse. In every data set we find high levels of pseudosparseness ranging from 0.59–0.98, substantially greater than the value of 0.00 for authentic sparseness. This was true for synthetic and natural stimuli, as well as for single-electrode and multielectrode data. A model indicates that a key variable producing high pseudosparseness is the standard deviation of spontaneous activity across the population. Consistently high values of pseudosparseness in the data demand reconsideration of the sparse coding literature as well as consideration of the degree to which authentic sparseness provides a useful framework for understanding neural coding in the cortex., Sidney R. Lehky et al. examined neurophysiological data from a wide variety of macaque cortices and find highly correlated population responses to both synthetic and natural stimuli. This high correlation, termed the pseudosparseness index, mimics statistical properties of sparseness without being authentically sparse, highlighting need for more in-depth assessment of the cortical sparse coding literature.