Sustained MK-801 induced deficit in a novel probabilistic reversal learning task.

Cognitive flexibility, the ability to adapt to unexpected changes, is critical for healthy environmental and social interactions, and thus to everyday functioning. In neuropsychiatric diseases, cognitive flexibility is often impaired and treatment options are lacking. Probabilistic reversal learning (PRL) is commonly used to measure cognitive flexibility in rodents and humans. In PRL tasks, subjects must sample choice options and, from probabilistic feedback, find the current best choice which then changes without warning. However, in rodents, pharmacological models of human cognitive impairment tend to disrupt only the first (or few) of several contingency reversals, making quantitative assessment of behavioral effects difficult. To address this limitation, we developed a novel rat PRL where reversals occur at relatively long intervals in time that demonstrates increased sensitivity to the non-competitive NMDA receptor antagonist MK-801. Here, we quantitively compare behavior in time-based PRL with a widely used task where reversals occur based on choice behavior. In time-based PRL, MK-801 induced sustained reversal learning deficits both in time and across reversal blocks but, at the same dose, only transient weak effects in performance-based PRL. Moreover, time-based PRL yielded better estimates of behavior and reinforcement learning model parameters, which opens meaningful pharmacological windows to efficiently test and develop novel drugs preclinically with the goal of improving cognitive impairment in human patients.

Somatostatin interneurons in the prefrontal cortex control affective state discrimination in mice.

The prefrontal cortex (PFC) is implicated in processing of the affective state of others through non-verbal communication. This social cognitive function is thought to rely on an intact cortical neuronal excitatory and inhibitory balance. Here combining in vivo electrophysiology with a behavioral task for affective state discrimination in mice, we show a differential activation of medial PFC (mPFC) neurons during social exploration that depends on the affective state of the conspecific. Optogenetic manipulations revealed a double dissociation between the role of interneurons in social cognition. Specifically, inhibition of mPFC somatostatin (SOM+), but not of parvalbumin (PV+) interneurons, abolishes affective state discrimination. Accordingly, synchronized activation of mPFC SOM+ interneurons selectively induces social discrimination. As visualized by in vivo single-cell microendoscopic Ca2+ imaging, an increased synchronous activity of mPFC SOM+ interneurons, guiding inhibition of pyramidal neurons, is associated with affective state discrimination. Our findings provide new insights into the neurobiological mechanisms of affective state discrimination.

Non-rhythmic head-direction cells in the parahippocampal region are not constrained by attractor network dynamics.

Computational models postulate that head-direction (HD) cells are part of an attractor network integrating head turns. This network requires inputs from visual landmarks to anchor the HD signal to the external world. We investigated whether information about HD and visual landmarks is integrated in the medial entorhinal cortex and parasubiculum, resulting in neurons expressing a conjunctive code for HD and visual landmarks. We found that parahippocampal HD cells could be divided into two classes based on their theta-rhythmic activity: non-rhythmic and theta-rhythmic HD cells. Manipulations of the visual landmarks caused tuning curve alterations in most HD cells, with the largest visually driven changes observed in non-rhythmic HD cells. Importantly, the tuning modifications of non-rhythmic HD cells were often non-coherent across cells, refuting the notion that attractor-like dynamics control non-rhythmic HD cells. These findings reveal a new population of non-rhythmic HD cells whose malleable organization is controlled by visual landmarks.

Hippocampal Remapping and Its Entorhinal Origin.

The activity of hippocampal cell ensembles is an accurate predictor of the position of an animal in its surrounding space. One key property of hippocampal cell ensembles is their ability to change in response to alterations in the surrounding environment, a phenomenon called remapping. In this review article, we present evidence for the distinct types of hippocampal remapping. The progressive divergence over time of cell ensembles active in different environments and the transition dynamics between pre-established maps are discussed. Finally, we review recent work demonstrating that hippocampal remapping can be triggered by neurons located in the entorhinal cortex.

Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex.

Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.

Interspike Intervals Reveal Functionally Distinct Cell Populations in the Medial Entorhinal Cortex.

The superficial layers of the medial entorhinal cortex (MEC) contain spatially selective neurons that are crucial for spatial navigation and memory. These highly specialized neurons include grid cells, border cells, head-direction cells, and irregular spatially selective cells. In addition, MEC neurons display a large variability in their spike patterns at a millisecond time scale. In this study, we analyzed spike trains of neurons in the MEC superficial layers of mice and found that these neurons can be classified into two groups based on their propensity to fire spike doublets at 125-250 Hz. The two groups, labeled "bursty" and "non-bursty" neurons, differed in their spike waveforms and interspike interval adaptation but displayed a similar mean firing rate. Grid cell spatial periodicity was more commonly observed in bursty than in non-bursty neurons. In contrast, most neurons with head-direction selectivity or those that fired at the border of the environment were non-bursty neurons. During theta oscillations, both bursty and non-bursty neurons fired preferentially near the end of the descending phase of the cycle, but the spikes of bursty neurons occurred at an earlier phase than those of non-bursty neurons. Finally, analysis of spike-time crosscorrelations between simultaneously recorded neurons suggested that the two cell classes are differentially coupled to fast-spiking interneurons: bursty neurons were twice as likely to have excitatory interactions with putative interneurons as non-bursty neurons. These results demonstrate that bursty and non-bursty neurons are differentially integrated in the MEC network and preferentially encode distinct spatial signals. SIGNIFICANCE STATEMENT: We report that neurons in the superficial layers of the medial entorhinal cortex can be classified based on their tendency to fire bursts of action potentials at 125-250 Hz. The relevance of this classification is demonstrated by the types of spatial information preferentially encoded by bursty and non-bursty neurons. Grid-like spatial periodicity is more commonly observed in bursty neurons, whereas most cells with head-direction selectivity or those that are firing at the border of the environment are non-bursty neurons. This work indicates that the spatial firing patterns of neurons in the medial entorhinal cortex can be predicted by electrophysiological features reflecting the synaptic inputs and/or integrating properties of the neurons.