Decoding Rich Spatial Information with High Temporal Resolution

New research suggests that magnetoencephalography (MEG) contains rich spatial information for decoding neural states. Even small differences in the angle of neighbouring dipoles generate subtle, but statistically separable field patterns. This implies MEG (and electroencephalography: EEG) is ideal for decoding neural states with high-temporal resolution in the human brain

Discriminating Valid from Spurious Indices of Phase-Amplitude Coupling

Recently there has been a strong interest in cross-frequency coupling, the interaction between neuronal oscillations in different frequency bands. In particular, measures quantifying the coupling between the phase of slow oscillations and the amplitude of fast oscillations have been applied to a wide range of data recorded from animals and humans. Some of the measures applied to detect phase-amplitude coupling have been criticized for being sensitive to non-sinusoidal properties of the oscillations and thus spuriously indicate the presence of coupling. While such instances of spurious identification of coupling have been observed, in this commentary we give concrete examples illustrating cases when the identification of cross-frequency coupling can be trusted. These examples are based on control analyses and empirical observations rather than signal processing tools. Finally, we provide concrete advice on how to determine when measures of phase-amplitude coupling can be considered trustworthy

The hippocampus and implicit memory (by any other name)

Is the hippocampus involved in implicit memory? I argue that contemporary views on hippocampal function, going beyond the classic dichotomy of explicit versus implicit, predict involvement of the hippocampus whenever flexible, predictive associations are rapidly encoded. This involvement is independent of conscious awareness. A paradigm case is statistical learning: the unconscious extraction of statistical regularities from the environment. In line with this, a substantial body of literature on contextual cueing in visual search has established hippocampal involvement in this form of implicit learning. To conclude, implicit memory (as such or by any other name) is associated with the hippocampus

Hippocampal theta modulation of neocortical spike times and gamma rhythm: A biophysical model study

Contains fulltext : 103419.pdf (publisher's version ) (Open Access

A patch of neocortex modelled by a network of interneurons coupled with a network of pyramidal cells.

<p>(A) The macroarchitecture of the model. Shown are the fast-spiking inhibitory interneuron network (circle, left), the pyramidal cell network (triangle, right), the cortical long-range afferent spike trains (top), and the theta-modulated subcortical afferent spike trains (bottom). (B) The ring-like structure of the interneuron model. Shown are some of the inhibitory synaptic connections (solid circles) and gap junctions (‘conduits’ between adjacent cells) for cell 1. (C) The two-dimensional structure of the pyramidal cell network. Shown are some of the excitatory synaptic projections from cell to its neighbours. Note the projections to and are possible because the effective distance from to those cells equals (see Methods for details).</p

Effect of theta-modulated input on LFP gamma amplitude.

<p>(A) The distribution of a composite theta phase/gamma amplitude signal in the complex plane. Color code represents the number of observations; angle corresponds to theta phase (divided into equally-sized bins); radius corresponds to gamma amplitude (divided into equally-sized bins). (B) Histograms of gamma amplitudes occurring in wide phase bins centered at the peak of the theta rhythm (; black) and at the trough (; red). Dashed lines correspond to the observed data histograms; solid lines represent the best-fit gamma distribution for this data. When FS cells receive theta-modulated input, the best-fit distribution for the theta trough is shifted to the right, compared to the distribution for the theta peak (parameter differences are significant, ). (C) Mean gamma amplitude as a function of theta phase. For the two rightmost plots, the best-fit sine functions are shown by a dashed red line. Gamma amplitudes are higher at the trough than at the peak of the theta rhythm, and the lowest and highest gamma amplitudes (indicated by dotted vertical lines) occur somewhat after the theta peak and trough, respectively.</p

Frequency/input curves for the two model subnetworks: the fast-spiking cells (top) and the pyramidal cells (bottom).

<p>The dotted lines perpendicular to the x-axis represent the total range of spike train input experienced by the subnetworks due to the theta-modulated input fibres. Dotted lines perpendicular to the y-axis represent the resulting subnetwork spike activity. While the theta-modulation of input spikes is stronger for the FS cells than for the P cells, the resulting difference in subnetwork spike activity is greater for the P cells, as can be observed from the intersection of the horizontal lines with the y-axis.</p

Shunting inhibition increases robustness in a network of fast-spiking inhibitory interneurons.

<p>(A–B) Rasterplots (top) and spike histograms (bottom) for simulations GABA -synaptic reversal potentials of (A) and (B). For these plots, mean drive , drive variation . Synapses were activated at ; plots are truncated at . (C) Amplitude spectra for spike histograms. Spectral analyses were performed on complete histograms, ending at . (D) Two measures of network synchronization, network coherence (top row) and average spike volley peak height (bottom row), as a function of drive variation over cells (x-axis), synaptic reversal potential (y-axis), and mean drive (separate columns). Shunting values of result in stronger synchronization with increasing drive heterogeneity.</p