Uncovering representations of sleep-associated hippocampal ensemble spike activity

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.Collaborative Research in Computational Neuroscience (Award IIS-1307645)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-10-1-0936)National Institutes of Health (U.S.) (Grant TR01-GM10498

Spike sorting for large, dense electrode arrays

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%

Identifying priority sites for whale shark ship collision management globally

The expansion of the world's merchant fleet poses a great threat to the ocean's biodiversity. Collisions between ships and marine megafauna can have population-level consequences for vulnerable species. The Endangered whale shark (Rhincodon typus) shares a circumglobal distribution with this expanding fleet and tracking of movement pathways has shown that large vessel collisions pose a major threat to the species. However, it is not yet known whether they are also at risk within aggregation sites, where up to 400 individuals can gather to feed on seasonal bursts of planktonic productivity. These "constellation" sites are of significant ecological, socio-economic and cultural value. Here, through expert elicitation, we gathered information from most known constellation sites for this species across the world (>50 constellations and >13,000 individual whale sharks). We defined the spatial boundaries of these sites and their overlap with shipping traffic. Sites were then ranked based on relative levels of potential collision danger posed to whale sharks in the area. Our results showed that researchers and resource managers may underestimate the threat posed by large ship collisions due to a lack of direct evidence, such as injuries or witness accounts, which are available for other, sub-lethal threat categories. We found that constellations in the Arabian Sea and adjacent waters, the Gulf of Mexico, the Gulf of California, and Southeast and East Asia, had the greatest level of collision threat. We also identified 39 sites where peaks in shipping activity coincided with peak seasonal occurrences of whale sharks, sometimes across several months. Simulated collision mitigation options estimated potentially minimal impact to industry, as most whale shark core habitat areas were small. Given the threat posed by vessel collisions, a coordinated, multi-national approach to mitigation is needed within priority whale shark habitats to ensure collision protection for the species

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. The hippocampal and neocortical neuronal ensembles encode rich spatial information in navigation. Hu et al. develop computational techniques that accommodate real-time decoding and assessment of large-scale unsorted neural ensemble place codes during running behavior and sleep. Keywords: neural decoding; population decoding; place codes; GPU; memory replay; spatiotemporal patternsNational Science Foundation (U.S.) (Grant IIS-130764)National Institutes of Health (U.S.) (Grant R01-MH118928)National Institutes of Health (U.S.) (Grant R01-MH092638)National Institutes of Health (U.S.) (Grant TR01-GM104948)National Institutes of Health (U.S.) (Grant R21-EY028381)National Science Foundation (U.S.) (Grant CCF-1231216

Data from: Impaired hippocampal place cell dynamics in a mouse model of the 22q11.2 deletion

Hippocampal place cells represent the cellular substrate of episodic memory. Place cell ensembles reorganize to support learning but must also maintain stable representations to facilitate memory recall. Despite extensive research, the learning-related role of place cell dynamics in health and disease remains elusive. Using chronic two-photon Ca2+ imaging in hippocampal area CA1 of wild-type and Df(16)A+/− mice, an animal model of 22q11.2 deletion syndrome, one of the most common genetic risk factors for cognitive dysfunction and schizophrenia, we found that goal-oriented learning in wild-type mice was supported by stable spatial maps and robust remapping of place fields toward the goal location. Df(16)A+/− mice showed a significant learning deficit accompanied by reduced spatial map stability and the absence of goal-directed place cell reorganization. These results expand our understanding of the hippocampal ensemble dynamics supporting cognitive flexibility and demonstrate their importance in a model of 22q11.2-associated cognitive dysfunction