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1.  Movement Dependence and Layer Specificity of Entorhinal Phase Precession in Two-Dimensional Environments 
PLoS ONE  2014;9(6):e100638.
As a rat moves, grid cells in its entorhinal cortex (EC) discharge at multiple locations of the external world, and the firing fields of each grid cell span a hexagonal lattice. For movements on linear tracks, spikes tend to occur at successively earlier phases of the theta-band filtered local field potential during the traversal of a firing field – a phenomenon termed phase precession. The complex movement patterns observed in two-dimensional (2D) open-field environments may fundamentally alter phase precession. To study this question at the behaviorally relevant single-run level, we analyzed EC spike patterns as a function of the distance traveled by the rat along each trajectory. This analysis revealed that cells across all EC layers fire spikes that phase-precess; indeed, the rate and extent of phase precession were the same, only the correlation between spike phase and path length was weaker in EC layer III. Both slope and correlation of phase precession were surprisingly similar on linear tracks and in 2D open-field environments despite strong differences in the movement statistics, including running speed. While the phase-precession slope did not correlate with the average running speed, it did depend on specific properties of the animal's path. The longer a curving path through a grid-field in a 2D environment, the shallower was the rate of phase precession, while runs that grazed a grid field tangentially led to a steeper phase-precession slope than runs through the field center. Oscillatory interference models for grid cells do not reproduce the observed phenomena.
doi:10.1371/journal.pone.0100638
PMCID: PMC4069107  PMID: 24959748
3.  Modeling ripple oscillations in the hippocampus 
BMC Neuroscience  2013;14(Suppl 1):P208.
doi:10.1186/1471-2202-14-S1-P208
PMCID: PMC3704508
4.  Phase sequences in balanced recurrent networks 
BMC Neuroscience  2013;14(Suppl 1):P207.
doi:10.1186/1471-2202-14-S1-P207
PMCID: PMC3704515
5.  Cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus 
The Journal of Neuroscience  2012;32(2):423-435.
Summary
Neuronal oscillations allow for temporal segmentation of neuronal spikes. Interdependent oscillators can integrate multiple layers of information. We examined phase-phase coupling of theta and gamma oscillators in the CA1 region of rat hippocampus during maze exploration and REM sleep. Hippocampal theta waves were asymmetric, and estimation of the spatial position of the animal was improved by identifying the waveform-based phase of spiking, compared to traditional methods used for phase estimation. Using the waveform-based theta phase, 3 distinct gamma bands were identified: slow gammaS (30-50 Hz), mid-frequency gammaM (50-90 Hz) and fast gammaF (90-150 Hz or epsilon band). The amplitude of each sub-band was modulated by the theta phase. In addition, we found reliable phase-phase coupling between theta and both gammaS and gammaM but not gammaF oscillators. We suggest that cross-frequency phase coupling can support multiple time-scale control of neuronal spikes within and across structures.
doi:10.1523/JNEUROSCI.4122-11.2012
PMCID: PMC3293373  PMID: 22238079
6.  Microsecond Precision of Phase Delay in the Auditory System of the Barn Owl 
Journal of Neurophysiology  2005;94(2):1655-1658.
The auditory system encodes time with sub-millisecond accuracy. To shed new light on the basic mechanism underlying this precise temporal neuronal coding, we analyzed the neurophonic potential, a characteristic multiunit response, in the barn owl’s nucleus laminaris. We report here that the relative time measure of phase delay is robust against changes in sound level, with a precision sharper than 20 µs. Absolute measures of delay, such as group delay or signal-front delay, had much greater temporal jitter, for example due to their strong dependence on sound level. Our findings support the hypothesis that phase delay underlies the sub-millisecond precision of the representation of interaural time difference needed for sound localization.
doi:10.1152/jn.01226.2004
PMCID: PMC3268176  PMID: 15843477
7.  Computational models of neurophysiological correlates of tinnitus 
The understanding of tinnitus has progressed considerably in the past decade, but the details of the mechanisms that give rise to this phantom perception of sound without a corresponding acoustic stimulus have not yet been pinpointed. It is now clear that tinnitus is generated in the brain, not in the ear, and that it is correlated with pathologically altered spontaneous activity of neurons in the central auditory system. Both increased spontaneous firing rates and increased neuronal synchrony have been identified as putative neuronal correlates of phantom sounds in animal models, and both phenomena can be triggered by damage to the cochlea. Various mechanisms could underlie the generation of such aberrant activity. At the cellular level, decreased synaptic inhibition and increased neuronal excitability, which may be related to homeostatic plasticity, could lead to an over-amplification of natural spontaneous activity. At the network level, lateral inhibition could amplify differences in spontaneous activity, and structural changes such as reorganization of tonotopic maps could lead to self-sustained activity in recurrently connected neurons. However, it is difficult to disentangle the contributions of different mechanisms in experiments, especially since not all changes observed in animal models of tinnitus are necessarily related to tinnitus. Computational modeling presents an opportunity of evaluating these mechanisms and their relation to tinnitus. Here we review the computational models for the generation of neurophysiological correlates of tinnitus that have been proposed so far, and evaluate predictions and compare them to available data. We also assess the limits of their explanatory power, thus demonstrating where an understanding is still lacking and where further research may be needed. Identifying appropriate models is important for finding therapies, and we therefore, also summarize the implications of the models for approaches to treat tinnitus.
doi:10.3389/fnsys.2012.00034
PMCID: PMC3347476  PMID: 22586377
tinnitus; computational model; hearing loss; homeostatic plasticity; lateral inhibition; gain adaptation
8.  Single-trial phase precession in the hippocampus 
During the crossing of the place field of a pyramidal cell in the rat hippocampus, the firing phase of the cell decreases with respect to the local theta rhythm. This phase precession is usually studied on the basis of data in which many place field traversals are pooled together. Here we study properties of phase precession in single trials. We found that single-trial and pooled-trial phase precession were different with respect to phase-position correlation, phase-time correlation, and phase range. While pooled-trial phase precession may span 360°, the most frequent single-trial phase range was only around 180°. In pooled trials, the correlation between phase and position (r = −0.58) was stronger than the correlation between phase and time (r = −0.27), whereas in single trials these correlations (r = −0.61 for both) were not significantly different. Next, we demonstrated that phase precession exhibited a large trial-to-trial variability. Overall, only a small fraction of the trial-to-trial variability in measures of phase precession (e.g. slope or offset) could be explained by other single-trial properties (such as running speed or firing rate), while the larger part of the variability remains to be explained. Finally, we found that surrogate single trials, created by randomly drawing spikes from the pooled data, are not equivalent to experimental single trials: pooling over trials therefore changes basic measures of phase precession. These findings indicate that single trials may be better suited for encoding temporally structured events than is suggested by the pooled data.
doi:10.1523/JNEUROSCI.2270-09.2009
PMCID: PMC2830422  PMID: 19846711
Place cells; Hippocampus; CA1; Phase shift; Theta rhythm; Temporal coding; Spatial; Memory
9.  Experience-Dependent Plasticity in S1 Caused by Noncoincident Inputs 
Journal of neurophysiology  2005;94(3):2239-2250.
Prior work has shown that coincident inputs became corepresented in somatic sensory cortex. In this study, the hypothesis that the corepresentation of digits required synchronous inputs was tested, and the daily development of two-digit receptive fields was observed with cortical implants. Two adult primates detected temporal differences in tap pairs delivered to two adjacent digits. With stimulus onset asynchronies of ≥100 ms, representations changed to include two-digit receptive fields across the first 4 wk of training. In addition, receptive fields at sites responsive to the taps enlarged more than twofold, and receptive fields at sites not responsive to the taps had no significant areal change. Further training did not increase the expression of two-digit receptive fields. Cortical responses to the taps were not dependent on the interval length. Stimuli preceding a hit, miss, false positives, and true negatives differed in the ongoing cortical rate from 50 to 100 ms after the stimulus but did not differ in the initial, principal, response to the taps. Response latencies to the emergent responses averaged 4.3 ms longer than old responses, which occurs if plasticity is cortical in origin. New response correlations developed in parallel with the new receptive fields. These data show corepresentation can be caused by presentation of stimuli across a longer time window than predicted by spike-timing– dependent plasticity and suggest that increased cortical excitability accompanies new task learning.
doi:10.1152/jn.00172.2005
PMCID: PMC2826984  PMID: 16105958

Results 1-9 (9)