Isolation of currents containing contributions from an oscillatory LFP of interest.

<p>Application of the procedure to experimental and model LFPs are presented side by side to facilitate the biophysical interpretation of signal transformations (left and right columns, respectively). A shows sample traces of raw LFPs at different domains along the pyramidal cell axis: colored stars mark recordings in the st. pyr, rad. and l-m, respectively. Model LFPs were high-pass filtered (>0.1 Hz) to reproduce AC-coupling of experimental recordings, and the distribution of the inputs was simpler than in the real CA1 to better visualize the changes in oscillatory input. CSD analysis of LFPs produced a complex spatiotemporal mixture of current sinks and sources. Few or no domains of active synaptic sites (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075499#pone-0075499-g001" target="_blank">Figure 1</a>) were detected. The amplified segments below show that individual gamma waves in the st. rad. may be matched by either sources or sinks at the active Schaffer domain. B shows the separation of the pathway-specific contributions by ICA applied to raw LFP profiles. Three different generators (G1-G3) were obtained, each of which defined by the characteristic spatial distribution (weight at each electrode) and temporal activation specific to the period analyzed. The respective spatial distributions are shown in the middle. Those obtained for model LFPs tightly reproduced the distribution of synaptically activated compartments, and the temporal sequence of inputs was accurate. Following reconstruction of pathway-specific LFP profiles, the application of CSD analysis rendered a spatiotemporal map of sources and sinks in which stable reversal sites were observed. The model confirmed that these corresponded to the macroscopic boundaries of active and passive domains of the synaptic input. However, note that both sinks and sources still appeared at the synaptic site. These temporal patterns do not allow us to determine whether sinks or sources at each domain are real or spurious.</p

Experimental localization of the active synaptic domain and determination of its chemical nature.

<p>Excitatory and inhibitory neurotransmitter blockers were injected locally, one at a time, near the linear probe at the sites located in the spatial domains found after CSD analysis of virtual Schaffer LFPs (A). DNQX and bicuculline (BIC) were injected in the st. pyr./st. or (Experiments 1 and 3). or the st. rad. (Experiments 2 and 4). The plots illustrate the temporal envelopes of the activity of the Schaffer generator obtained following ICA of the LFP profiles before and after drug injection (inj). The insets show sample epochs of virtual Schaffer LFPs. Note that only the Glu-receptor blocker DNQX abolished the activity of this generator when applied in the st. rad. (Expt. 2). All experiments belong to the same animal. B shows the population data (mean ± s.e.m.; <i>n</i> is the number of animals): ***p<0.001, Student’s t-test.</p

Summary of the procedure and signal transformations.

<p>Block 1 illustrates the relationship between the single cell currents and the macroscopic field potentials recorded in ideal DC-coupled mode. A rhythmic excitatory drive into a discrete dendritic domain establishes local sinks of current, surrounded by sources (CSD). These spatially-aligned sources and sinks produce laminar field potentials of uneven strength and polarity, whose CSD analysis renders a correct estimation of sources and sinks amplitudes and locations. Block 2 illustrates the effect of AC-coupling on recordings. Note that channels are individually filtered (<i>zeroing</i>) and thus each is offset by a different amount (red dotted lines). Consequently, each gamma wave is transformed into a biphasic sequence at any location. CSD analysis of the AC-coupled profile results in spurious sequences of sources and sinks at all sites. Each LFP wave (dashed ovals) returns a source/sink pair, while the lack of a true reference baseline confounds their initiation time and does not allow us to ascertain which of sources or sinks are expected in cell domains. Block 3 illustrates the rectification procedure. First the ICA decomposes the original signals into pathway-specific generators (pairs s₁t₁. s_nt_n; only one is used for simplification), each with a spatial distribution and a temporal activation (time course). Note that the curve of spatial distribution is proportional to the collection of offsets introduced by AC-coupling (<i>zeroing</i> in Block 2), whilst the time evolution is unique. The experimental determination of the active synaptic sites and the excitatory/inhibitory chemical nature allows offsetting the time envelope as required in order to achieve homogeneous polarity (red vertical arrow). The subsequent reconstruction using the rectified time envelope (green curved arrows) regenerates LFPs with the correct baseline at each recording site. Consequently, the application of CSD analysis generates spatiotemporal maps of sources and sinks in which individual waves have the correct amplitude and duration.</p

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories