Grid cells with their rigid hexagonal firing fields are thought to provide an invariant metric to the hippocampal cognitive map, yet environmental geometrical features have recently been shown to distort the grid structure. Given that the hippocampal role goes beyond space, we tested the influence of nonspatial information on the grid organization. We trained rats to daily learn three new reward locations on a cheeseboard maze while recording from the medial entorhinal cortex and the hippocampal CA1 region. Many grid fields moved toward goal location, leading to long-lasting deformations of the entorhinal map. Therefore, distortions in the grid structure contribute to goal representation during both learning and recall, which demonstrates that grid cells participate in mnemonic coding and do not merely provide a simple metric of space.

Temporally organized reactivation of experiences during awake immobility periods is thought to underlie cognitive processes like planning and evaluation. While replay of trajectories is well established for the hippocampus, it is unclear whether the medial prefrontal cortex (mPFC) can reactivate sequential behavioral experiences in the awake state to support task execution. We simultaneously recorded from hippocampal and mPFC principal neurons in rats performing a mPFC-dependent rule-switching task on a plus maze. We found that mPFC neuronal activity encoded relative positions between the start and goal. During awake immobility periods, the mPFC replayed temporally organized sequences of these generalized positions, resembling entire spatial trajectories. The occurrence of mPFC trajectory replay positively correlated with rule-switching performance. However, hippocampal and mPFC trajectory replay occurred independently, indicating different functions. These results demonstrate that the mPFC can replay ordered activity patterns representing generalized locations and suggest that mPFC replay might have a role in flexible behavior.

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Background: Until recently, anti-SSA/Ro antibodies were not considered pathogenic for severe heart disease in adults. Prolongation of the mean QTc interval in electrocardiograms of adult patients with anti-SSA/Ro-positive connective tissue disease has been reported and could contribute to complex arrhythmias in such patients. Furthermore, complete heart block may also be related to these autoantibodies.

Hippocampal and neocortical neural activity is modulated by the position of the individual in space. While hippocampal neurons provide the basis for a spatial map, prefrontal cortical neurons generalize over environmental features. Whether these generalized representations result from a bidirectional interaction with, or are mainly derived from hippocampal spatial representations is not known. By examining simultaneously recorded hippocampal and medial prefrontal neurons, we observed that prefrontal spatial representations show a delayed coherence with hippocampal ones. We also identified subpopulations of cells in the hippocampus and medial prefrontal cortex that formed functional cross-area couplings; these resembled the optimal connections predicted by a probabilistic model of spatial information transfer and generalization. Moreover, cross-area couplings were strongest and had the shortest delay preceding spatial decision-making. Our results suggest that generalized spatial coding in the medial prefrontal cortex is inherited from spatial representations in the hippocampus, and that the routing of information can change dynamically with behavioral demands.

Although much is known about how single neurons in the hippocampus represent an animal's position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs.\ novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.

The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While nonlinear computations can be implemented successfully in spiking neural networks, this requires supervised training and the resulting connectivity can be hard to interpret. In contrast, the required connectivity for any computation in the form of a linear dynamical system can be directly derived and understood with the spike coding network (SCN) framework. These networks also have biologically realistic activity patterns and are highly robust to cell death. Here we extend the SCN framework to directly implement any polynomial dynamical system, without the need for training. This results in networks requiring a mix of synapse types (fast, slow, and multiplicative), which we term multiplicative spike coding networks (mSCNs). Using mSCNs, we demonstrate how to directly derive the required connectivity for several nonlinear dynamical systems. We also show how to carry out higher-order polynomials with coupled networks that use only pair-wise multiplicative synapses, and provide expected numbers of connections for each synapse type. Overall, our work demonstrates a novel method for implementing nonlinear computations in spiking neural networks, while keeping the attractive features of standard SCNs (robustness, realistic activity patterns, and interpretable connectivity). Finally, we discuss the biological plausibility of our approach, and how the high accuracy and robustness of the approach may be of interest for neuromorphic computing.