Abstract The retrosplenial cortex (RSC) integrates sensory and mnemonic information to support spatial orientation and navigation, yet how visuospatial processing differs across its subregions remains unclear. Here, we combined cellular imaging in navigating mice with brain-wide anatomical input tracing to characterize how multimodal sensory and positional signals are integrated along the anterior–posterior axis of dorsal RSC. We identified consistent differences between anterior and posterior subregions in both functional response properties and long-range connectivity. Anterior RSC neurons displayed sharper and more reliable position tuning during tactile-cued navigation and preferential sensitivity to fast, low–spatial-frequency visual motion. In contrast, posterior RSC neurons showed broader position selectivity, stronger responses to slow, high–spatial-frequency visual patterns, and enhanced tuning in visually immersive virtual environments. Consistent with these differences, anterior RSC received denser projections from motor, somatosensory, and parietal areas, whereas posterior RSC received stronger input from primary and posteromedial visual cortices. Together, these findings identify an anterior–posterior functional gradient in RSC, with subregions differing in how they integrate sensory and positional signals during navigation.

The hippocampus is known to encode spatial information and reactivate experienced trajectories during sharp-wave ripple events. These events are thought to be key time-points at which information about learned trajectories is transferred to the neocortex for long-term storage. It is unclear, however, how this information may be transferred and integrated in downstream cortical regions. In this study, we performed high-density probe recordings across the full depth of the medial prefrontal cortex and in the hippocampus simultaneously in rats while they were performing a task of spatial navigation. We find that neurons in the medial prefrontal cortex encode spatial information and reliably predict upcoming choice on a maze, and we find that a subset of neurons in the mPFC is modulated by hippocampal sharp-wave ripples. However, sharp-wave ripple modulation does not appear to be the main driving factor in predicting upcoming choice. This indicates that the integration of spatial information requires the collaboration of different specialized populations of neurons.

The hippocampus is known to encode spatial information and reactivate experienced trajectories during sharp-wave ripple events. These events are thought to be key time-points at which information about learned trajectories is transferred to the neocortex for long-term storage. It is unclear, however, how this information may be transferred and integrated in downstream cortical regions. In this study, we performed high-density probe recordings across the full depth of the medial prefrontal cortex and in the hippocampus simultaneously in rats while they were performing a task of spatial navigation. We find that neurons in the medial prefrontal cortex encode spatial information and reliably predict upcoming choice on a maze, and we find that a subset of neurons in the mPFC is modulated by hippocampal sharp-wave ripples. However, the neurons that are involved in predicting upcoming choice are not the neurons that are modulated by hippocampal sharp-wave ripples. This indicates that the integration of spatial information requires the collaboration of different specialized populations of neurons.

Neuropixels probes contain thousands of electrodes across one or more shanks and are sufficiently small to allow chronic recording of neural activity in freely behaving small animals. However, the joint increase in the number of electrodes and miniaturization of the probe package has led to a compromise in which groups of electrodes share a single read-out channel and only a fraction of the electrodes can be read out at any given time. Experimenters then face the challenge of selecting a subset of electrodes (i.e., channel map) that both covers the brain regions of interest and adheres to the restrictions of the underlying hardware. Here, we present NeuroCarto, a Python toolkit and GUI to simplify the construction of a custom channel map for Neuropixels probes. We describe a general iterative approach to select electrodes and provide a specific implementation that allows experimenters to specify a blueprint of regions of interest along the probe shanks and the desired local electrode density. NeuroCarto assists in generating a channel map from the blueprint and visualizes potential read-out channel conflicts. We showcase the utility of NeuroCarto in an experimental workflow to simultaneously record from the dorsal and ventral hippocampus with 4-shank Neuropixels 2.0 probes in freely moving mice.

The retrosplenial cortex (RSC) is a key integrative hub involved in spatial orientation, navigation, and cognitive processes. In rodents, RSC neurons carry rich sensory and navigational signals and are interconnected with sensory, motor, thalamic, and hippocampal circuits—supporting multimodal integration. However, the circuitry that supports this integration remain unclear. Here, we combined 2-photon calcium imaging in navigating mice with brain-wide retrograde tracing to investigate how visual and positional information are represented and distributed across RSC subregions. We found a clear anterior–posterior gradient: anterior RSC neurons exhibited sharper, more reliable position tuning and preferred fast-moving visual stimuli, while posterior RSC neurons showed broader tuning and preferential responses to slower motion. These functional differences were paralleled by distinct patterns of long-range input: anterior RSC received denser projections from motor, parietal, and hippocampal-associated areas—regions implicated in position encoding—whereas posterior RSC was more strongly innervated by visual cortices. Our findings reveal that the RSC contains functionally and anatomically distinct subregions specialized for processing different visuospatial features, suggesting a modular organization that supports integration of contextual and sensory information during navigation. ### Competing Interest Statement The authors have declared no competing interest. FWO, G0C1220N, G077321N, 1226320N

Temporally and spatially selective closed-loop perturbation of brain activity can be used to reveal the contribution of spontaneous neural events to behavior. Here, we present a protocol for optogenetic inhibition of the medial prefrontal cortex following hippocampal sharp-wave ripple events in freely behaving rats. We describe procedures for injecting a viral vector bilaterally, constructing and implanting an assembly of optical fibers and tetrodes, and performing closed-loop perturbations in rats. For complete details on the use and execution of this protocol, please refer to den Bakker et al.1.

An internal representation of the environment – or map – allows animals to evaluate multiple routes and adapt their navigation strategy to current needs and future goals. The hippocampal formation plays a crucial role in learning a spatial map and using the map for goal-directed navigation. The lateral septum forms a major node for connections between the hippocampus and subcortical brain regions that could link the spatial map to motivation and reward processing centers such as the ventral tegmental area. It is not known, however, how the lateral septum contributes to processing of spatial information and route planning. In this study we investigated the temporal dynamics of spatial representations in the lateral septum. Neuropixels probes were used to record cellular activity along the dorsal-ventral extent of the lateral septum while rats performed one of two spatial navigation tasks in a Y-maze. The activity of a large fraction of cells was theta rhythmic and a subset of cells showed evidence of being active on alternate theta cycles (theta cycle skipping). Both theta rhythmicity and cycle skipping were strongest in the dorsal lateral septum. Similarly, spatially selective firing was most prominent in the dorsal later septum. Using neural decoding, we show that the lateral septum cell population encodes both the current location and alternatingly the possible future paths within single theta cycles when rats approached the choice point in the maze. Our data further shows that the alternating expression of spatial representations in the lateral septum is task dependent, such that it is strongest when the task also requires the animals to alternate between rewarded goal arms. These data suggests that task demands and experience shape which representations are activated near a choice point. The lateral septum receives strong input from hippocampal place cells, and while there may be integration and transformation of incoming spatial signals, our findings support the idea that hippocampal spatial representations and their temporal dynamics are conveyed to subcortical projection areas through the lateral septum.

Storing and accessing memories is required to successfully perform day-to-day tasks, for example for engaging in a meaningful conversation. Previous studies in both rodents and primates have correlated hippocampal cellular activity with behavioral expression of memory. A key role has been attributed to awake hippocampal replay – a sequential reactivation of neurons representing a trajectory through space. However, it is unclear if awake replay impacts immediate future behavior, gradually creates and stabilizes long-term memories over a long period of time (hours and longer), or enables the temporary memorization of relevant events at an intermediate time scale (seconds to minutes). In this study, we aimed to address the uncertainty around the timeframe of impact of awake replay by collecting causal evidence from behaving rats. We detected and disrupted sharp wave ripples (SWRs) - signatures of putative replay events - using electrical stimulation of the ventral hippocampal commissure in rats that were trained on three different spatial memory tasks. In each task, rats were required to memorize a new set of locations in each trial or each daily session. Interestingly, the rats performed equally well with or without SWR disruptions. These data suggest that awake SWRs - and potentially replay - does not affect the immediate behavior nor the temporary memorization of relevant events at a short timescale that are required to successfully perform the spatial tasks. Based on these results, we hypothesize that the impact of awake replay on memory and behavior is long-term and cumulative over time.

Previous studies have highlighted an important role for hippocampal sharp-wave ripples in spatial alternation learning, as well as in modulating activity in the medial prefrontal cortex (mPFC). However, the direct influence of hippocampal sharp-wave ripples on mPFC activity during spatial alternation learning has not been investigated. Here, we train Long Evans rats on a three-arm radial maze to perform a sequence of alternations. Three alternation sequences needed to be learned, and while learning a sequence, the activity in the mPFC was inhibited either directly following sharp-wave ripples in the hippocampus (on-time condition) or with a randomized delay (delayed condition). In the on-time condition, the behavioral performance is significantly worse compared to the same animals in the delayed inhibition condition, as measured by a lower correct alternation performance and more perseverative behavior. This indicates that the activity in the mPFC directly following hippocampal sharp-wave ripples is necessary for spatial rule switching.

Short-term memory (STM) on a time scale of seconds to minutes is required to successfully perform day-to-day tasks, for example when engaging in a meaningful conversation. Previous studies in both rodents and primates have correlated hippocampal cellular activity and behavioural expression of STM. This research has led to models describing the putative neural mechanism in the hippocampus that mediate STM. In these models, a key role has been given to hippocampal replay – reactivation of neurons representing a trajectory through space– but definitive causal evidence that can challenge or confirm the model is missing. In this study, we aimed to address the uncertainty around the role of awake replay in STM by collecting direct causal evidence from behaving rats. Signatures of replay events were detected in the hippocampus and disrupted using electrical stimulation of the ventral hippocampal commissure in rats that were trained on three different spatial memory tasks in a multi-arm radial maze. All tasks required memory of the recent past, but varied in the time scale over which information needed to be retained: (1) a multiple trial match-to sample task, (2) a single trial non-match to sample task and (3) a spatial sequence memory paradigm. Rats readily learned the task rules, but disruption of awake replay did not affect task performance or other behavioral measures in any of the task. Altogether, our results show for the first time with definitive causal evidence that awake replay is not required for STM of events or of their temporal order.

Hippocampal sharp-wave ripple activity (SWRs) and the associated replay of neural activity patterns are well-known for their role in memory consolidation. This activity has been studied using electrophysiological approaches, as high temporal resolution is required to recognize SWRs in the neuronal signals. However, it has been difficult to analyze the individual contribution of neurons to task-specific SWRs, because it is hard to track neurons across a long time with electrophysiological recording. In this study, we recorded local field potential (LFP) signals in the hippocampal CA1 of freely behaving mice and simultaneously imaged calcium signals in contralateral CA1 to leverage the advantages of both electrophysiological and imaging approaches. We manufactured a custom-designed microdrive array and targeted tetrodes to the left hippocampus CA1 for LFP recording and applied electrical stimulation in the ventral hippocampal commissure (VHC) for closed-loop disruption of SWRs. Neuronal population imaging in the right hippocampal CA1 was performed using a miniature fluorescent microscope (Miniscope) and a genetically encoded calcium indicator. As SWRs show highly synchronized bilateral occurrence, calcium signals of SWR-participating neurons could be identified and tracked in spontaneous or SWR-disrupted conditions. Using this approach, we identified a subpopulation of CA1 neurons showing synchronous calcium elevation to SWRs. Our results showed that SWR-related calcium transients are more disrupted by electrical stimulation than non-SWR-related calcium transients, validating the capability of the system to detect and disrupt SWRs. Our dual recording method can be used to uncover the dynamic participation of individual neurons in SWRs and replay over extended time windows.

Reward value is known to modulate learning speed in spatial memory tasks, but little is known about its influence on the dynamical changes in hippocampal spatial representations. Here, we monitored the trial-to-trial changes in hippocampal place cell activity during the acquisition of place-reward associations with varying reward size. We show a faster reorganization and stabilization of the hippocampal place map when a goal location is associated with a large reward. The reorganization is driven by both rate changes and the appearance and disappearance of place fields. The occurrence of hippocampal replay activity largely followed the dynamics of changes in spatial representations. Replay patterns became more selectively tuned toward behaviorally relevant experiences over the course of learning via the refined contributions of specific cell subpopulations. These results suggest that high reward value enhances memory retention by accelerating the formation and stabilization of the hippocampal cognitive map and selectively enhancing its reactivation during learning.

Objective.Spike sorting is the process of extracting neuronal action potentials, or spikes, from an extracellular brain recording, and assigning each spike to its putative source neuron. Spike sorting is usually treated as a clustering problem. However, this clustering process is known to be affected by overlapping spikes. Existing methods for resolving spike overlap typically require an expensive post-processing of the clustering results. In this paper, we propose the design of a domain-specific feature map, which enables the resolution of spike overlap directly in the feature space.Approach.The proposed domain-specific feature map is based on a neural network architecture that is trained to simultaneously perform spike sorting and spike overlap resolution. Overlapping spikes clusters can be identified in the feature space through a linear relation with the single-neuron clusters for which the neurons contribute to the overlapping spikes. To aid the feature map training, a data augmentation procedure is presented that is based on biophysical simulations.Main results.We demonstrate the potential of our method on independent and realistic test data. We show that our novel approach for resolving spike overlap generalizes to unseen and realistic test data. Furthermore, the sorting performance of our method is shown to be similar to the state-of-the-art, but our method does not assume the availability of spike templates for resolving spike overlap.Significance.Resolving spike overlap directly in the feature space, results in an overall simplified spike sorting pipeline compared to the state-of-the-art. For the state-of-the-art, the overlapping spike snippets exhibit a large spread in the feature space and do not appear as concentrated clusters. This can lead to biased spike template estimates which affect the sorting performance of the state-of-the-art. In our proposed approach, overlapping spikes form concentrated clusters and spike overlap resolution does not depend on the availability of spike templates.

How dynamic activity in neural circuits gives rise to behavior is a major area of interest in neuroscience. A key experimental approach for addressing this question involves measuring extracellular neuronal activity in awake, behaving animals. Recently developed Neuropixels probes have provided a step change in recording neural activity in large tissue volumes with high spatiotemporal resolution. This protocol describes the chronic implantation of Neuropixels probes in mice and rats using compact and reusable 3D-printed fixtures. The fixtures facilitate stable chronic in vivo recordings in freely behaving rats and mice. They consist of two parts: a covered main body and a skull connector. Single-, dual- and movable-probe fixture variants are available. After completing an experiment, probes are safely recovered for reimplantation by a dedicated retrieval mechanism. Fixture assembly and surgical implantation typically take 4–5 h, and probe retrieval takes ~30 min, followed by 12 h of incubation in probe cleaning agent. The duration of data acquisition depends on the type of behavioral experiment. Since our protocol enables stable, chronic recordings over weeks, it enables longitudinal large-scale single-unit data to be routinely obtained in a cost-efficient manner, which will facilitate many studies in systems neuroscience. This protocol describes the implantation of Neuropixels probes for chronic recording of neural activity in rats and mice using 3D-printed fixtures. The fixtures enable routine probe reuse, and single-, dual- and movable-probe variants are described.

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

Memories of past events and common knowledge are critical to flexibly adjust one’s future behavior based on prior experiences. The formation and the transformation of these memories into a long-lasting form are supported by a dialog between the coordinated activity of population of neurons in the cortex and the hippocampus. Not all experiences are remembered equally well nor for equally long. It has been demonstrated experimentally in humans that memory strength positively depends on the behavioral relevance of the associated experience. Behavioral paradigms testing the selective retention of memory in rodents would enable to further investigate the neuronal mechanisms at play. We developed a novel paradigm to follow the repeated acquisition and retrieval of two contextually distinct, yet concurrently occurring, food-place associations in rats. We demonstrated the use of this paradigm by varying the amount of reward associated with the two locations. After delays of 2h or 20h, rats showed better memory performance for experiences associated with larger amount of reward. This effect depends on the level of spatial integration required to retrieve the associated location. Thus, this paradigm is suited to study the preferential retention of relevant experiences in rats.

In this paper, we propose three novel linear filter design methods for use in a multi-pattern recognition task with overlapping patterns and strong peak interferers. The recognition is based on a linear filter-and-threshold approach, which is particularly interesting when the task has to be performed in a computationally constrained environment. The first method optimizes the signal-to-peak-interference ratio (SPIR) of the filter output, where the focus is on minimization of the post-filtering peak interference instead of the pre-filtering peak interference as in existing methods. The second and third method are convex approximations of the first method and are shown to be closely related to support vector machines, which establishes a natural link between SPIR-optimal filtering and the maximum margin matched filter. The proposed methods only require a template of the target patterns as prior knowledge and do not require the training data to be labelled. An extensive case study is presented in the context of neural spike sorting, in which the proposed approaches are shown to significantly outperform existing filter-and-threshold approaches for spike sorting.

When inserting an electrode array in the brain, its electrodes will record so-called 'spikes' which are generated by the neurons in the neighbourhood of the array. Spike sorting is the process of detecting and assigning these recorded spikes to their putative neurons. Many spike sorting pipelines rely on a clustering algorithm that groups the spikes coming from the same neuron in a pre-defined feature space. However, classical spike sorting algorithms fail when spike overlap, i.e., the near-simultaneous occurrence of two or more spikes from different neurons, is present in the recording. In such cases, the overlapping spikes segment ends up in a seemingly random position in the feature space and is not assigned to the correct cluster. This problem has been addressed before by extending the sorting algorithm with a template matching postprocessor. In this work, a novel approach is presented to resolve spike overlap directly in the feature space. To this end, a neural network feature map is presented, that generates spike embeddings (feature vectors) that behave as a linear superposition in the feature space in the case of spike overlap. Its performance is quantified on semi-synthetic data obtained through a data augmentation procedure applied to real neural recordings.

Objective. Long-term electrophysiological recordings of neural activity in freely behaving animals are indispensable to advance the understanding of complex brain function. It is a technical challenge to chronically monitor the detailed activity across multiple distributed brain regions in freely behaving animals over a period of months. Here we present a new implant for inserting multiple flexible polyimide probes into freely behaving rats for monitoring the brain activity over a long time period. Approach. This brain implant integrates multiple flexible probes in small micromanipulator devices that ensure free behaviour of the animal. The probes are micromachined and the positioning mechanism is 3D-printed using stereolithography. Each probe is lowered by a screw-driven shuttle and guided through an exit tip before penetrating the rat’s brain. Main results. The brain implant consists of 16 individually lowerable flexible polyimide probes that contain 16 embedded electrodes adding up to a total of 256 recording channels. The total travel distance is 8 mm. The assembly time of the device was only one day. The electrode impedance values had a mean of 335 kΩ and sample standard deviation of 107 kΩ after gold plating, excluding outliers. Significance. For the first time, hyperdrive-assisted insertion of flexible multichannel probes was demonstrated. Local field potentials and neuronal spiking activity from freely behaving rats were recorded over months.