Disruption of awake sharp-wave ripples does not affect memorization of locations in repeated-acquisition spatial memory tasks.

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.

Sharp-wave-ripple-associated activity in the medial prefrontal cortex supports spatial rule switching.

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.

Simultaneous Cellular Imaging, Electrical Recording and Stimulation of Hippocampal Activity in Freely Behaving Mice.

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-SWRrelated 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.

Single-trial dynamics of hippocampal spatial representations are modulated by reward value.

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.

Implantation of Neuropixels probes for chronic recording of neuronal activity in freely behaving mice and rats.

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.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings.

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.

SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance.

Spike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.

A Dual Reward-Place Association Task to Study the Preferential Retention of Relevant Memories in Rats.

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 dialogue between populations of neurons in the cortex and the hippocampus. Not all experiences are remembered equally well or equally long. It has been demonstrated experimentally in humans that memory strength positively relates to the behavioral relevance of the associated experience. Behavioral paradigms that test the selective retention of memory in rodents would enable further investigation of the neuronal mechanisms at play. We developed a novel paradigm to follow the repeated acquisition and retrieval of two contextually distinct, yet concurrently learned, 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 2 h or 20 h, rats showed better memory performance for experience associated with large 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.

System for recording from multiple flexible polyimide neural probes in freely behaving animals.

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.

Post-learning Hippocampal Replay Selectively Reinforces Spatial Memory for Highly Rewarded Locations.

Offline replay of hippocampal neural patterns supports the acquisition of new tasks in novel contexts, but its contribution to consolidation of salient experiences in a familiar context is unknown. Here, we show that in a highly familiar spatial memory task, large rewards selectively enhanced performance for demanding task configurations. The reward-related enhancement was sensitive to ripple-specific disruption, and the proportion of replay events positively correlated with reward size and task demands. Hippocampal replay thus selectively enhances memory of highly rewarded locations in a familiar context.

A data-driven regularization approach for template matching in spike sorting with high-density neural probes.

Spike sorting is the process of assigning neural spikes in an extracellular brain recording to their putative neurons. Optimal pre-whitened template matching filters that are used in spike sorting typically suffer from ill-conditioning. In this paper, we investigate the origin of this ill-conditioning and the way in which it influences the resulting filters. Two data-driven subspace regularization approaches are proposed, and those are shown to outperform a regularization approach used in recent literature. The comparison of the methods is based on ground truth data that are recorded in-vivo.

Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes.

Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents' unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an ∼20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time <15 ms), our approach enables assessment of the statistical significance of online-decoded "memory replay" candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments.

Real-time classification of experience-related ensemble spiking patterns for closed-loop applications.

Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 -200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain-computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.

Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes.

OBJECTIVE: The process of grouping neuronal spikes in an extracellular recording according to their neuronal sources, is generally referred to as spike sorting. Currently, the use of spike sorting is mainly limited to an offline usage, where spikes are sorted after the data acquisition has been completed. In this paper, we propose a discriminative template matching algorithm for threshold-based spike sorting on high-density extracellular data. Such threshold-based spike sorting has a low and deterministic algorithmic delay, allowing for fast online spike sorting. APPROACH: At its core, threshold-based spike sorting is driven by linear filters. The proposed discriminative template matching filter design algorithm optimizes the output signal-to-peak-interference ratio in a data-driven fashion, assuming the template of the target spike is available. The latter allows the filter to suppress the spikes of interfering neurons and to resolve spike overlap. The data-driven filter design algorithm requires only templates of the target neurons of interest, which can be retrieved, e.g. through a prior clustering on an initial recording. MAIN RESULTS: The proposed discriminative template matching filters are validated on in vivo ground truth data and are shown to provide single-unit activity with good accuracy using a simple thresholding operation on the filter outputs. SIGNIFICANCE: The low algorithmic complexity allows for computationally cheap and fast spike sorting. Also the proposed filters are guaranteed to be stable and have a deterministic delay. These characteristics make the proposed filter design method a valuable building block for online spike sorting, thereby enabling unit activity-based real-time and closed-loop experiments for high-density neural recordings.

Time Multiplexed Active Neural Probe with 1356 Parallel Recording Sites.

We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor) active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm) and 12 reference pixels (20 µm × 80 µm), densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications. For enhanced performance, further noise reduction can be achieved while using half of the electrodes (678). Both of these numbers considerably surpass the state-of-the art active neural probes in both electrode count and number of recording channels. The measured input referred noise in the action potential band is 12.4 µVrms, while using 678 electrodes, with just 3 µW power dissipation per pixel and 45 µW per read-out channel (including data transmission).

Involvement of fast-spiking cells in ictal sequences during spontaneous seizures in rats with chronic temporal lobe epilepsy.

See Lenck-Santini (doi:10.1093/awx205) for a scientific commentary on this article. Epileptic seizures represent altered neuronal network dynamics, but the temporal evolution and cellular substrates of the neuronal activity patterns associated with spontaneous seizures are not fully understood. We used simultaneous recordings from multiple neurons in the hippocampus and neocortex of rats with chronic temporal lobe epilepsy to demonstrate that subsets of cells discharge in a highly stereotypical sequential pattern during ictal events, and that these stereotypical patterns were reproducible across consecutive seizures. In contrast to the canonical view that principal cell discharges dominate ictal events, the ictal sequences were predominantly composed of fast-spiking, putative inhibitory neurons, which displayed unusually strong coupling to local field potential even before seizures. The temporal evolution of activity was characterized by unique dynamics where the most correlated neuronal pairs before seizure onset displayed the largest increases in correlation strength during the seizures. These results demonstrate the selective involvement of fast spiking interneurons in structured temporal sequences during spontaneous ictal events in hippocampal and neocortical circuits in experimental models of chronic temporal lobe epilepsy.

Falcon: a highly flexible open-source software for closed-loop neuroscience.

OBJECTIVE: Closed-loop experiments provide unique insights into brain dynamics and function. To facilitate a wide range of closed-loop experiments, we created an open-source software platform that enables high-performance real-time processing of streaming experimental data. APPROACH: We wrote Falcon, a C++ multi-threaded software in which the user can load and execute an arbitrary processing graph. Each node of a Falcon graph is mapped to a single thread and nodes communicate with each other through thread-safe buffers. The framework allows for easy implementation of new processing nodes and data types. Falcon was tested both on a 32-core and a 4-core workstation. Streaming data was read from either a commercial acquisition system (Neuralynx) or the open-source Open Ephys hardware, while closed-loop TTL pulses were generated with a USB module for digital output. We characterized the round-trip latency of our Falcon-based closed-loop system, as well as the specific latency contribution of the software architecture, by testing processing graphs with up to 32 parallel pipelines and eight serial stages. We finally deployed Falcon in a task of real-time detection of population bursts recorded live from the hippocampus of a freely moving rat. MAIN RESULTS: On Neuralynx hardware, round-trip latency was well below 1 ms and stable for at least 1 h, while on Open Ephys hardware latencies were below 15 ms. The latency contribution of the software was below 0.5 ms. Round-trip and software latencies were similar on both 32- and 4-core workstations. Falcon was used successfully to detect population bursts online with ~40 ms average latency. SIGNIFICANCE: Falcon is a novel open-source software for closed-loop neuroscience. It has sub-millisecond intrinsic latency and gives the experimenter direct control of CPU resources. We envisage Falcon to be a useful tool to the neuroscientific community for implementing a wide variety of closed-loop experiments, including those requiring use of complex data structures and real-time execution of computationally intensive algorithms, such as population neural decoding/encoding from large cell assemblies.

Integration of silicon-based neural probes and micro-drive arrays for chronic recording of large populations of neurons in behaving animals.

OBJECTIVE: Understanding how neuronal assemblies underlie cognitive function is a fundamental question in system neuroscience. It poses the technical challenge to monitor the activity of populations of neurons, potentially widely separated, in relation to behaviour. In this paper, we present a new system which aims at simultaneously recording from a large population of neurons from multiple separated brain regions in freely behaving animals. APPROACH: The concept of the new device is to combine the benefits of two existing electrophysiological techniques, i.e. the flexibility and modularity of micro-drive arrays and the high sampling ability of electrode-dense silicon probes. MAIN RESULTS: Newly engineered long bendable silicon probes were integrated into a micro-drive array. The resulting device can carry up to 16 independently movable silicon probes, each carrying 16 recording sites. Populations of neurons were recorded simultaneously in multiple cortical and/or hippocampal sites in two freely behaving implanted rats. SIGNIFICANCE: Current approaches to monitor neuronal activity either allow to flexibly record from multiple widely separated brain regions (micro-drive arrays) but with a limited sampling density or to provide denser sampling at the expense of a flexible placement in multiple brain regions (neural probes). By combining these two approaches and their benefits, we present an alternative solution for flexible and simultaneous recordings from widely distributed populations of neurons in freely behaving rats.

A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields.

Pyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescales, a neuron's spike activity also depends significantly on its own spike history. However, due to limitations of current parametric modeling approaches, it remains a challenge to estimate complex, multimodal neuronal receptive fields while incorporating spike history dependence. Furthermore, efforts to decode the rat's trajectory in one- or two-dimensional space from hippocampal ensemble spiking activity have mainly focused on spike history-independent neuronal encoding models. In this letter, we address these two important issues by extending a recently introduced nonparametric neural encoding framework that allows modeling both complex spatial receptive fields and spike history dependencies. Using this extended nonparametric approach, we develop novel algorithms for decoding a rat's trajectory based on recordings of hippocampal place cells and entorhinal grid cells. Results show that both encoding and decoding models derived from our new method performed significantly better than state-of-the-art encoding and decoding models on 6 minutes of test data. In addition, our model's performance remains invariant to the apparent modality of the neuron's receptive field.

VTA neurons coordinate with the hippocampal reactivation of spatial experience.

Spatial learning requires the hippocampus, and the replay of spatial sequences during hippocampal sharp wave-ripple (SPW-R) events of quiet wakefulness and sleep is believed to play a crucial role. To test whether the coordination of VTA reward prediction error signals with these replayed spatial sequences could contribute to this process, we recorded from neuronal ensembles of the hippocampus and VTA as rats performed appetitive spatial tasks and subsequently slept. We found that many reward responsive (RR) VTA neurons coordinated with quiet wakefulness-associated hippocampal SPW-R events that replayed recent experience. In contrast, coordination between RR neurons and SPW-R events in subsequent slow wave sleep was diminished. Together, these results indicate distinct contributions of VTA reinforcement activity associated with hippocampal spatial replay to the processing of wake and SWS-associated spatial memory.