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%

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network.

Grid cells fire in sequences that represent rapid trajectories in space. During locomotion, theta sequences encode sweeps in position starting slightly behind the animal and ending ahead of it. During quiescence and slow wave sleep, bouts of synchronized activity represent long trajectories called replays, which are well-established in place cells and have been recently reported in grid cells. Theta sequences and replay are hypothesized to facilitate many cognitive functions, but their underlying mechanisms are unknown. One mechanism proposed for grid cell formation is the continuous attractor network. We demonstrate that this established architecture naturally produces theta sequences and replay as distinct consequences of modulating external input. Driving inhibitory interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gives rise to theta sequences and phase precession, respectively. Decreasing input drive to all neurons produces traveling wavefronts of activity that are decoded as replays

Gamma band directional interactions between basal forebrain and visual cortex during wake and sleep states

The basal forebrain (BF) is an important regulator of cortical excitability and responsivity to sensory stimuli, and plays a major role in wake-sleep regulation. While the impact of BF on cortical EEG or LFP signals has been extensively documented, surprisingly little is known about LFP activity within BF. Based on bilateral recordings from rats in their home cage, we describe endogenous LFP oscillations in the BF during quiet wakefulness, rapid eye movement (REM) and slow wave sleep (SWS) states. Using coherence and Granger causality methods, we characterize directional influences between BF and visual cortex (VC) during each of these states. We observed pronounced BF gamma activity particularly during wakefulness, as well as to a lesser extent during SWS and REM. During wakefulness, this BF gamma activity exerted a directional influence on VC that was associated with cortical excitation. During SWS but not REM, there was also a robust directional gamma band influence of BF on VC. In all three states, directional influence in the gamma band was only present in BF to VC direction and tended to be regulated specifically within each brain hemisphere. Locality of gamma band LFPs to the BF was confirmed by demonstration of phase locking of local spiking activity to the gamma cycle. We report novel aspects of endogenous BF LFP oscillations and their relationship to cortical LFP signals during sleep and wakefulness. We link our findings to known aspects of GABAergic BF networks that likely underlie gamma band LFP activations, and show that the Granger causality analyses can faithfully recapitulate many known attributes of these networks

The wisdom of networks: A general adaptation and learning mechanism of complex systems: The network core triggers fast responses to known stimuli; innovations require the slow network periphery and are encoded by core-remodeling

I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This "core-periphery learning" theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation and a number of recent reports on the adaptation of protein, neuronal and social networks. The coreperiphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related 'wisdom of crowds' inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution.Comment: The 2015 preliminary version can be downloaded as an earlier version of the final paper here. Please find illustrative videos here: http://networkdecisions.linkgroup.hu and a video abstract here: https://youtu.be/IIjP7zWGjV