Simultaneous localisation and mapping (SLAM) algorithms are commonly used in robotic systems for learning maps of novel environments. Brains also appear to learn maps, but the mechanisms are not known and it is unclear how to infer these maps from neural activity data. We present BrainSLAM; a method for performing SLAM using only population activity (local field potential, LFP) data simultaneously recorded from three brain regions in rats: hippocampus, prefrontal cortex, and parietal cortex. This system uses a convolutional neural network (CNN) to decode velocity and familiarity information from wavelet scalograms of neural local field potential data recorded from rats as they navigate a 2D maze. The CNN's output drives a RatSLAM-inspired architecture, powering an attractor network which performs path integration plus a separate system which performs `loop closure' (detecting previously visited locations and correcting map aliasing errors). Together, these three components can construct faithful representations of the environment while simultaneously tracking the animal's location. This is the first demonstration of inference of a spatial map from brain recordings. Our findings expand SLAM to a new modality, enabling a new method of mapping environments and facilitating a better understanding of the role of cognitive maps in navigation and decision making.

Neural representations of space in the hippocampus and related brain areas change over timescales of days-weeks, even in familiar contexts and when behavior appears stable. It is unclear whether this ‘representational drift’ is primarily driven by the passage of time or by behavioral experience. Here we present a novel deep-learning approach for measuring network-level representational drift, quantifying drift as the rate of change in decoder error of deep neural networks as a function of train-test lag. Using this method, we analyse a longitudinal dataset of 0.5–475 Hz broadband local field potential (LFP) data recorded from dorsal hippocampal CA1, medial prefrontal cortex and parietal cortex of six rats over ∼ 30 days, during learning of a spatial navigation task in an unfamiliar environment. All three brain regions contained clear spatial representations which improve and drift over training sessions. We find that the rate of drift slows for later training sessions. Finally, we find that drift is statistically better explained by task-relevant rewarded experiences within the maze, rather than the passage of time or number of sessions the animal spent on the maze. Our use of deep neural networks to quantify drift in broadband neural time series unlocks new possibilities for testing which aspects of behavior drive representational drift.### Competing Interest StatementThe authors have declared no competing interest.

Spatial information is encoded by location-dependent hippocampal place cell firing rates and sub-second, rhythmic entrainment of spike times. These rate and temporal codes have primarily been characterized in low-dimensional environments under limited cognitive demands; but how is coding configured in complex environments when individual place cells signal several locations, individual locations contribute to multiple routes and functional demands vary? Quantifying CA1 population dynamics of male rats during a decision-making task, here we show that the phase of individual place cells' spikes relative to the local theta rhythm shifts to differentiate activity in different place fields. Theta phase coding also disambiguates repeated visits to the same location during different routes, particularly preceding spatial decisions. Using unsupervised detection of cell assemblies alongside theoretical simulation, we show that integrating rate and phase coding mechanisms dynamically recruits units to different assemblies, generating spiking sequences that disambiguate episodes of experience and multiplexing spatial information with cognitive context. Russo et al. show that context-specific place cell assemblies support hippocampal integration of past experiences into future plans during goal-directed behavior and propose a biophysical mechanism behind the formation of goal dependent theta sequences.

Neural representations of space in the hippocampus and related brain areas change over timescales of daysweeks, even when there are no apparent behavioural changes.This 'representational drift' occurs even after animals are fully familiar with a given context.Many qualities of this phenomenon are unknown, yet few tools exist to aid analysis.Here we present a novel deeplearning approach for robust quantification and analysis of ensemble level representational drift.Using this method, we analyse a longitudinal dataset of 0.5-475Hz broadband local field potential (LFP) data taken from Hippocampal, Prefrontal-Cortex and Parietal-Cortex of rats collected over multiple days, before and after a contextual rule change in a spatial navigation learning task.First, we observed clear spatial representations in all considered brain regions, despite the low frequency LFP data used.Second, we show statistically significant drift in these representations in all brain regions.Lastly, we show a statistically significant increase in the stability of representations for all considered brain regions as time and experience increases.Our general strategy for using deep neural networks to quantify drift in broadband LFP data opens up new possibilities for flexibly dissecting the features of drift in large-scale neural recordings, and how they relate to animal behaviour.

Ultrasonic phased arrays are used to generate mid-air haptic feedback, allowing users to feel sensations in mid-air. In this work, we present a method for testing mid-air haptics with a biomimetic tactile sensor that is inspired by the human fingertip. Our experiments with point, line, and circular test stimuli provide insights on how the acoustic radiation pressure produced by the ultrasonic array deforms the skin-like material of the sensor. This allows us to produce detailed visualizations of the sensations in two-dimensional and three-dimensional space. This approach provides a detailed quantification of mid-air haptic stimuli of use as an investigative tool for improving the performance of haptic displays and for understanding the transduction of mid-air haptics by the human sense of touch.