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.2007 Jan 2;104(1):347-52.
doi: 10.1073/pnas.0605643104. Epub 2006 Dec 21.

Sequential structure of neocortical spontaneous activity in vivo

Affiliations

Sequential structure of neocortical spontaneous activity in vivo

Artur Luczak et al. Proc Natl Acad Sci U S A..

Abstract

Even in the absence of sensory stimulation, the neocortex shows complex spontaneous activity patterns, often consisting of alternating "DOWN" states of generalized neural silence and "UP" states of massive, persistent network activity. To investigate how this spontaneous activity propagates through neuronal assemblies in vivo, we simultaneously recorded populations of 50-200 cortical neurons in layer V of anesthetized and awake rats. Each neuron displayed a virtually unique spike pattern during UP states, with diversity seen amongst both putative pyramidal cells and interneurons, reflecting a complex but stereotypically organized sequential spread of activation through local cortical networks. Spike timing was most precise during the first approximately 100 ms after UP state onset, and decayed as UP states progressed. A subset of UP states propagated as traveling waves, but waves passing a given point in either direction initiated similar local sequences, suggesting local networks as the substrate of sequential firing patterns. A search for repeating motifs indicated that their occurrence and structure was predictable from neurons' individual latencies to UP state onset. We suggest that these stereotyped patterns arise from the interplay of intrinsic cellular conductances and local circuit properties.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Sequential activity at UP state onset. (a) Spontaneous activity of neurons in S1 of a urethane anesthetized rat. DOWN states of complete silence alternate with UP states of generalized activity (underlined area is expanded in Fig. 3a). Neurons are arranged vertically by physical location of recording shank. (b) Schematic of silicon microelectrode used in these studies. (c) Raster plots for 10 example neurons triggered by UP state onset for 100 UP states, showing a diversity of temporal profiles. (d) Neural latency is defined as the center of mass of the PETH. (e) Pseudocolor plot showing normalized activity of a simultaneously recorded population triggered by UP state onset, vertically arranged by latency. The dots indicate at which shank neurons were recorded. (f) Neural latencies were stable, as illustrated by comparison of latencies calculated separately for the first and the second halves of data set (>30 min). Black and green dots represent putative pyramidal cells and interneurons, respectively.
Fig. 2.
Fig. 2.
Sequential activity is not anesthetic-dependent. (a andc) UP state triggered PETH sorted by latency for ketamine-xylazine and unanesthetized animals, respectively (cf. Fig. 1e). (b andd) Consistency of neural latencies across the two halves of the experiment for ketamine-anesthetized and -unanesthetized rats, respectively. Green dots represent putative interneurons. Note that unanesthetized latencies are approximately half those observed under urethane or ketamine.
Fig. 3.
Fig. 3.
Interaction of traveling waves and local sequences. (a) Example of a traveling wave spreading from shank 8 to shank 1 under urethane anesthesia (expansion of underlined area in Fig. 1a). Neurons arranged vertically by recording shank location. Dashed line indicates propagation front fit by linear regression (seeMaterials and Methods). (b) Another traveling wave, propagating in the opposite direction, recorded during the same experiment. (c) Distribution of propagation front slopes. Red curve denotes distribution of slopes after shuffling shank order. (d Upper) PETHs computed separately for traveling waves moving to left (blue) and right (red), for two example neurons, aligned to global UP state onset defined by first spike time on any recording shank. (d Lower) PETHs realigned to shank-specific UP state onset times computed from propagation front slope, demonstrating increased stereotypy after realignment. (e) Scatter plot showing each neuron's latency for traveling waves moving to the left and right, computed from realigned PETHs. The strong correlation indicates that regardless of wave direction, neurons at a local site follow the same activation sequence.
Fig. 4.
Fig. 4.
Uniqueness and precision of temporal profiles. (a) Normalized UP state triggered PETHs for three representative neurons estimated during the first (solid lines) and second halves (dashed lines) of a urethane experiment. Each neuron has a unique PETH shape that is consistent across the two halves of the experiment. (b) The difference between each neuron's PETH and the overall population was quantified with a “PETH uniqueness measure” (seeMaterials and Methods). PETHs are ≈90% unique for anesthetic and drug-free conditions. The distributions of this measure for the original data (thick lines) and after time-shifting by each neuron's mean latency (thin lines) were similar, indicating that uniqueness reflects PETH shapes, not just latency shifts. (c) Precision of neuronal firing decays as UP state progresses. PETH half-widthw and peak positionp were estimated from a gamma function fit (Inset); the graph shows the half-width and peak position for all neurons. Black or green symbols identify putative pyramidal cells or interneurons, respectively; symbol shape identifies anesthetic condition. Note that neurons with late onset latencies do not exhibit narrow half-width. (d) Spike timing reliability measure (Rcorr; seeResults andSI Fig. 8) decays as a function of time after UP state onset. Line width indicates the size of smoothing kernel.
Fig. 5.
Fig. 5.
The structure of precisely repeating triplets is predicted by individual neural latencies. (a) For every trio of neurons, a spike triplet is described by two interspike intervals (t2 −t1 andt3 −t1). (b) Count matrix for a representative triplet of neurons, indicating the probability of different interspike interval combinations. Black square denotes triplets occurring within ± 10 ms of mode. (c) Triplet structure reflects individual neural latencies. Each triplet is represented by two points: (x1 =t2 −t1,y1 = latency2 − latency1) and (x2 =t3 −t1,y2 = latency3 − latency1). The strong correlation indicates that the structure of the triplets is predicted by the neurons' mean latencies to UP state onset. (d) Occurrence of precisely repeating triplets peaks shortly after the start of UP states. Blue and red curves denote shuffled data for independent Poisson and common excitability models, respectively (dashed lines indicate SD).
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