Detecting synfire chain activity using massively parallel ... - CiteSeerX

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Articles in PresS. J Neurophysiol (July 16, 2008). doi:10.1152/jn.01245.2007

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Detecting synfire chain activity using massively parallel spike train recording Sven Schrader1, Sonja Grün2 , Markus Diesmann1,2, and George L. Gerstein3 1

Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg, 71904, GERMANY

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Theoretical Neuroscience Group, RIKEN Brain Science Institute, Wako City, Saitama, 3510198 JAPAN 3

Department of Neuroscience, University of Pennsylvania, Philadelphia PA, 19104, USA

ABSTRACT The synfire chain model has been proposed as the substrate that underlies computational processes in the brain and has received extensive theoretical study. In this model cortical tissue is composed of a superposition of feed-forward sub-networks (chains) each capable of transmitting packets of synchronized spikes with high reliability. Computations are then carried out by interactions of these chains. Experimental evidence for synfire chains has so far been limited to inference from detection of a few repeating spatio-temporal neuronal firing patterns in multiple single-unit recordings. Demonstration that such patterns actually come from synfire activity would require finding a meta organization among many detected patterns, as yet an untried approach. In contrast we present here a new method that directly visualizes the repetitive occurrence of synfire activity even in very large data sets of multiple single-unit recordings. We achieve reliability and sensitivity by appropriately averaging over neuron space (identities) and time. We test the method with data from a large-scale balanced recurrent network simulation containing fifty randomly activated synfire chains. The sensitivity is high enough to detect synfire chain activity in simultaneous single-unit recordings of 100 to 200 neurons from such data, enabling application to experimental data in the near future.

Corresponding Author: Sven Schrader Present Address HONDA Research Institute Europe GmbH Carl-Legien-Str. 30 63073 Offenbach/Main, GERMANY tel.: +49-(0)69-89011-786 fax.: +49-(0)69-89011-749 e-mail: [email protected]

Copyright © 2008 by the American Physiological Society.

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2 INTRODUCTION

Although a tremendous amount of detail exists about properties and connectivity of cortical neurons (Braitenberg and Schüz 1991, Petersen and Sakmann 2000, Douglas and Martin 2004, Yoshimura et al. 2005, Song et al. 2005) the neuronal organization that underlies computational processes is poorly understood. Neurons usually do not act alone, and since Hebb (1949) the idea of a neuronal assembly has become widely accepted as a functional unit. There is, however, a continuing debate in the literature over whether representation of information in and transmission of information between such assemblies is in the form of firing rates or in the form of spatio-temporal firing patterns. One particularly intriguing suggestion for assembly organization is the synfire chain first proposed by Abeles (1991). The original version was a chain of length l composed of groups of w neurons each (the synfire chain width), with full unidirectional connectivity between successive groups. A group together with its output connections can interchangeably be called a link in the synfire chain. Such a chain structure propagates nearsynchronous activity in each group along successive groups like a row of dominoes. Detailed properties of various versions of such systems have received considerable theoretical attention; stability properties (Diesmann et al. 1999) have been studied as well as structural variations like partial group to group connectivity or like feedback when a given neuron occurs in more than one group. There also have been studies of partially interconnected synfire chains to define the conditions in which activity can jump between chains (Hayon et al. 2004; Hayon et al. 2005). Recent theoretical studies suggest that coupled synfire chain structures can demonstrate compositionality, the hierarchical representation of complex entities in terms of parts and their relations (Bienenstock 1995; Hayon et al. 2004; Schrader et al. 2007).

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Intriguing though the synfire concept may be there has up to now been no direct way to examine whether such structures occur in real brains. One approach might be to look for the repeating multi-neuron firing patterns that would be the signature of synfire activity. There are good methods (Abeles and Gerstein 1988, Gerstein 2004) to detect and test for significance any repeating spatio-temporal firing patterns which exceed the number expected by chance. Such patterns have been found in repeating behavioral task situations (Prut et al. 1998; Baker and Lemon 2000; Baker et al. 2001; Kohn and Smith 2005) and, in several papers, claimed as evidence for synfire chain activity (Abeles et al 1993; Ikegaya et al 2004). With parallel recording of the order of ten neurons the likelihood that the small sample includes several neurons either from the same or successive links of a particular chain is vanishingly small, so that such patterns are more likely to directly represent information coding and flow. However as massively parallel recordings become available (electrode methods: Warren et al. 2001, Nicolelis and Ribiera 2002, Donoghue 2002, Csicsvari et al. 2003, Rolston et al. 2007, Euston et al. 2007; optical methods: Ohki et al. 2005; Ohki et al. 2006, Sasaki et al. 2007) the sampling problem is eased. The pattern program (Abeles and Gerstein 1988) can easily deal with the larger number of neurons and will detect millions of candidate repeating patterns some of which will be significant by comparison to surrogate data (proper choice of surrogate is a non trivial problem in its own right). At this stage synfire chains can only be inferred (Abeles et al 1993; Ikegaya et al 2004), since they are a possible but not unique source for patterns. It is then necessary to sort, collate and variously compare the large number of significant detected patterns to gradually build up a meta organization like several synfire chains. The process would be like assembling the pieces of a jigsaw puzzle, in this case making appropriate matches and interleavings of pattern occurrence time, neuron identities and spike time interval structure. Such

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4 programs have not yet been tried, and will have unknown and probably data dependent computing time requirements. In this paper we develop a new method that can rapidly and clearly detect synfire chain activity and identify the participating neurons in a massive recording of even thousands of neurons, but that also is sensitive enough so that it continues to detect and identify even when 200 or fewer neurons are available. We demonstrate the method’s comparative run time requirements and its effectiveness for detection and for eliciting some aspects of chain connectivity using data from a large-scale cortical network model with and without embedded synfire chains.

METHODS

Simulation Development and evaluation of a new tool for spike train analysis can only be done using data with well defined properties. The data need to resemble experimental recordings as closely as possible and contain the effects for which the analysis method searches. In our case this requires the simulation of a local cortical network representing roughly a cubic millimeter of cortex on which we can perform virtual experiments. This simulation is only briefly described here since this paper is primarily about a new analysis tool for synfire chain activity. The basic network architecture (Figure 1) is the well studied balanced random network model (Vreeswijk and Sompolinsky 1996; Amit and Brunel 1997; Brunel 2000). The dynamics of the network is adjusted to the asynchronous irregular (AI) regime to reproduce relevant aspects of cortical dynamics: low rate firing of the individual neurons with Poisson process like interval statistics and large fluctuations of the membrane potential (see Figure 1 for the full set of

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5 parameters). The model consists of 40,000 excitatory and 10,000 inhibitory integrate-and-fire point model neurons in the network.

The neurons establish a random number (drawn from a binomial distribution) of dendritic synapses with the excitatory population of neurons. The mean of the distribution is a fraction =0.1 of the size of the excitatory population. This connection scheme permits multiple synapses between pairs of neurons. The scheme is also applied for establishing the dendritic synapses with the inhibitory population of neurons. The post-synaptic currents are described by alpha-functions with a rise time of 0.2 milliseconds for the excitatory synapses and 0.6 milliseconds for the inhibitory synapses. The excitatory post-synaptic potentials (EPSPs) have an amplitude of 0.1mV, the inhibitory post-synaptic potentials (IPSPs) are g=6 times larger. The synaptic delays are randomly chosen from a uniform distribution between 0.5 and 3 milliseconds. In addition to the explicitly modeled synaptic input all neurons receive a constant excitatory current of 350pA describing the excitatory input from outside the local cortical volume. Thus, the irregular activity is exclusively generated by the dynamics of the network and not imposed by an external source.

In contrast to the standard balanced random network architecture, in our network the excitatory-to-excitatory connections are excluded from the construction scheme described in the last paragraph. This leaves the AI state largely unaffected because it is essentially generated by the interplay of the external excitatory supra-threshold drive and the local inhibition. The excitatory-to-excitatory subnetwork is, instead, purely composed of a superposition of m=50 synfire chains. These chains are consecutively constructed. First, we randomly select w*l neurons (here: w=100, l=20) from the population of excitatory neurons without repetitions. In the next step, these neurons are connected into a feed-forward subnetwork of l successive links of w

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6 neurons. Each neuron, except the neurons in the first link, establishes a dendritic synapse with all the neurons in the preceding group. This arrangement is called a complete divergent and convergent connectivity (Abeles 1982; see also Griffith and Horn 1963). The connection procedure is repeated m times. Thus, an excitatory neuron may participate in several chains but occurs at most once in any given chain. The synaptic delays are drawn from the distribution specified for the other synapses above but is kept fixed for all synapses connecting two subsequent groups (links) of neurons. The amplitudes of post-synaptic potentials of synapses in the chains are 5 times larger than those of other excitatory connections.

In the absence of specific stimuli the synfire chains are not active and the excitatory neurons spike irregularly at a low rate. For the data presented in this manuscript we apply independent 1 Hz Poisson trains of stimuli to the synfire chains. A stimulus consists of a volley of 100 spikes. The individual spike times are drawn from a Gaussian distribution centered at the time of stimulation with a standard deviation of 1 millisecond. This volley is sent to all the neurons of the first group in a chain using synapses as strong as the intra-chain synapses.

The dynamics are integrated on a discrete time-grid of 0.1 millisecond using the Exact Integration method (Rotter and Diesmann 1999; see Morrison et al. 2007 for a detailed discussion of the application of the method to integrate-and-fire dynamics). We verified our results using a newly developed simulation technique capable of treating spikes times and delays in continuous time in the framework of a globally time-driven simulation scheme (Morrison et al. 2007). All simulations were carried out using the parallel computing capabilities (Plesser et al. 2007) of the NEST simulation tool (Gewaltig and Diesmann 2008) on an Infiniband compute cluster with 24 nodes with 4 compute cores and 8 GB of RAM per node.

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The average spike rate in this network is 2.2 Hz for the excitatory neurons and 1 Hz for the inhibitory. The probability of synfire activity to survive until the end of the chain (more than 80% active neurons within 6 milliseconds around the expected firing time) is 0.7-0.8. Previous studies (Aviel et al. 2003; Mehring et al. 2003; see also the discussion in Vogels et al. 2005) have reported considerable difficulties in embedding even a single feed-forward subnetwork capable of stable propagation of synchronous activity into the balanced random network. Our network differs from previous approaches in two essential properties. First, there is heterogeneity in the number of dendritic synapses. Second, the excitatory-to-excitatory subnetwork is purely composed of synfire chains. In fact, Tetzlaff et al. (2005) showed that the former condition is sufficient for stable propagation of a single synfire chain in an otherwise random balanced network.

However, in the present manuscript the network model is only employed as the generator of cortical spike data mimicking synfire activity. The details of the network dynamics and a parametric analysis of performance will be presented elsewhere.

Data Overview Neuronal activity is commonly visualized as a raster display where each tick represents the spike activity of a particular neuron. In the neural net simulations we deliberately randomize the neuron number (identity) with respect to the embedded synfire structures in order to mimic the random sampling of real recordings. It is instructive, however, to remap some data so that adjacent lines in the raster are associated with particular links and chains. Thus (Figure 2A) raster

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8 lines 8,001-8,100 represent the activity of neurons in the first link, and lines 8,001-10,000 comprise the activity of the entire fifth (arbitrarily chosen) synfire chain. The strong near vertical lines in the raster of Figure 2A are firings of this synfire chain; other, background neuron firings in the raster represent either spontaneous activity or the activity of other chains, since each neuron in this chain is also a member of 2-3 other chains. Figure 2B shows a portion of this raster with high time resolution revealing details of several chain runs. The offset between short vertical segments corresponds to activations of successive links; the offset is the interlink propagation or synaptic delay. Note that both link synchrony and interlink propagation time are variable and that there is a failure of propagation in the middle run of the chain.

Without the remapping of neuron identities as in Figure 2 synfire chain sequences are not visible in rasters. Figure 3A shows a 5 second segment of the activity of 150 excitatory neurons randomly selected (and not remapped according to chain membership) from a synfire simulation as above (50,000 neurons, 50 fully connected synfire chains independently stimulated by low frequency Poisson trains); the raster has no particular structure. The raster from a larger set of 10,000 excitatory neurons over the same time range, is shown in Figure 3B. Again, since the neurons are chosen randomly as they would be in a real recording, there are no structures like those in Figure 2 B that are directly ascribable to activations of individual synfire chains. However, there is an obvious broader vertical striping which connotes a coordinated fluctuation in the population activity; this striping becomes increasingly visible when the raster display shows a large number of neurons (contrast Figures 3A and 3B). It turns out that these coordinated rate fluctuations are the result of fluctuations in the number of synfire chains that have been activated at any time. Individual neurons, however, experience no rate increase. Figure 4 juxtaposes (a) the times of chain stimulations with (b) their (low pass) temporal representation

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9 (i.e. the number of chains activated at any time) and (c) the fluctuations of total population rate. These are clearly strongly although not completely related. We note that raster displays of simulation data from random nets, which contain no explicit synfire chains, also exhibit similar vertical striping (i.e. population rate fluctuations), but presumably caused by a different mechanism (Kriener et al. 2008). Analysis Method The basic data source is a large file specifying the firing times of each of many neurons. The exact format is a matter of laboratory preference, and irrelevant here. For the work in this paper networks were simulated with a temporal precision of 0.1ms, the observed number of neurons N=50,000, and the total observed time T=100s. The first step in the analysis is to define a bin size for time based on the expected interlink propagation delay (3ms in Figures 6-8 and 1113; 2ms in Figure 10C for greater clarity). The number of bins is K = T/binsize, so that a bin is specified by the index i lying between 1 and K. For each such bin along the entire length of the data we find the set S(i) of all the neurons that fired in that (3ms) interval. For large networks the S(i) can be of the order of 100 or more. The size of each S(i) defines the population firing rate at the time of that bin. Figure 4 illustrates a smoothed version of S(i) size over a selected epoch. The fundamental calculation we carry out is to compare the net activity at time bins i and j. This can be done by two conceptually different approaches. The first uses the S(i) sets and calculates the size of the intersection of S(i) and S(j), i.e. the number of neuron identifications that appear in both sets. The appropriate normalization here is the size of the lesser of the two sets, thus producing a number between 0 and 1. With these definitions:

M S (i, j ) = S (i ) I S ( j ) / min( S (i ) , S ( j ) )

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(1)

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10 The second, more traditional approach, defines a 0/1 vector V(i) of length N at each bin i; the entries of 1 indicating the identities of neurons active at that time. Now we take the dot product of the vectors and normalize by the product of their magnitudes (norms). This defines the cosine of the angle between the two vectors V(i) and V(j), and is a frequently used measure of similarity (or difference) between the vectors. Thus:

M V (i , j ) = V (i ) • V ( j ) / V (i ) V ( j ) 2

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(2)

Note that the numerators of the two expressions are numerically precisely the same (for 0/1 vectors), but the normalizations make MS generally larger and more nonlinear with respect to the number of neuron identity overlaps (higher contrast) than MV if the two sets S(i) and S(j) differ in size. Clearly M(i,i) = 1 and M(j,i) = M(i,j). Therefore it suffices to inspect only the triangular matrix i

j. With the stated bin structure i and j are both discrete measures of time; we

decided to locate the origin at the lower left corner for visualization as in the traditional two dimensional correlation displays like the JPSTH (Joint Peri Stimulus Time Histogram, Aertsen et al. 1989). Here the diagonal of i=j extends from the lower left to the upper right and we are inspecting the lower triangular part of the intersection matrix. The programs have been designed to allow examination of any time ranges for i and j with i2 times within our analysis time window. We now observe short diagonal stripes at k*(k-1)/2 locations; if the successive firings are numbered, these will be at (binned) times 1,2 1,3 ...1,k; 2,3 2,4...2,k; ....k-2,k-1 k-2,k; k-1,k;. This situation is shown in Figure 6 for k=4. Finally if there are several active chains, with each producing its appropriate set of short diagonal stripes, we obtain Figure 7 which analyzes an arbitrarily chosen 1.5 seconds of the 100 second data set. Figure 7 is calculated from a full data

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15 set, in effect observing all its 50,000 neurons; the result clearly detects the existence and activity of some number of the embedded synfire chains. Although we have not done so in Figure 7, we could obviously identify and label all diagonal stripes that originate from the activity of a particular synfire chain. Note that besides the prominent short diagonal stripes, Figure 7 also shows some faint, non-uniform background which will be discussed later. Typical detail of the short diagonal stripes together with chain labeling are shown in Figures 6 and 8A which are enlargements of portions of Figure 7.

Signal/Noise, Detection sensitivity In the context of simulated data we may examine the performance limits of the MS measure by reducing the number of observed neurons. The neurons are chosen randomly with no relation to the synfire chain structures. The analysis time window is constant for Figures 8A through 8D. When observing only 500 randomly chosen neurons out of the 40,000 excitatory network, the short diagonal stripes are somewhat noisier, so that not all elements of a particular stripe appear. However, comparing Figures 8A and 8B, most if not all stripes remain clearly visible. When only 200 randomly selected neurons are observed, we get Figure 8C. The stripes are now much more noisy, with many omissions, and not all stripes can be seen. This situation probably represents the current limit of sensitivity for detection of possible synfire chains in a data set. Such numbers are however just becoming possible in experiments, both those carried out by multiple electrodes or wires and with optical methods using Ca2+ indicators. When small numbers of neurons are observed, considerable visual enhancement for detecting the diagonal stripe signature of synfire chain activity can be achieved by passing an appropriate short 45 degree diagonal filter (6-8 pixels along the diagonal, rectangular) over the

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16 noisy M matrix. When this is done for the data shown in Figure 8C we get the matrix shown in Figure 8D. All short diagonal stripes visible from the large data set (Figure 8A) can now clearly be seen with the N=200 analysis, so that detection of at least some synfire chains should be possible with even smaller N. Note that even with the noisy matrix of Figure 8C the filtration has produced only a single spurious diagonal stripe (circled). A more rigorous depiction of the signal/noise situation can be made by varying the number of observed neurons and comparing the average and standard deviation of matrix values on and off a small diagonal stripe which had been detected with the full large data set. In order to have reasonable statistics we calculate over 40 disjoint sets of random choices of each number of neurons and in each iteration sum over 15 pixels along 45 degrees both on and off the short diagonal stripe location. The results are shown in Figure 9A, and give a synfire chain detection threshold of about 50 observed neurons. Another approach to the signal/noise separation does not depend on knowing about the location of short diagonal stripes as above and uses the entire matrix. Here we examine a histogram (probability density function - pdf) of the pixel population as a function of filtered pixel value, comparing the results of a 45 degree and a 135 degree narrow (6 pixel) filter. It is convenient to examine the results as a “survivor function”, i.e. (1-cdf) where cdf is the cumulative distribution function. For the data shown in Figure 8B or 8C (N=500 or 200) the survivor functions of the two filtered matrices all show a steep drop at filtered pixel value 0.166 on a scale of 0-1 (Figure 9C). This drop is an appropriate threshold value for comparing the pixel population in the two filter situations but is data and filter shape dependent, and must be examined for each application of such a calculation. Values larger than this threshold correspond to “signal” (the short diagonal stripes or equivalent pixel values in the 135 degree filtered case) and values below that threshold correspond to “noise” events. Again calculating over 40 disjoint

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17 sets of random choices of neuron at each number of neurons the comparison of pixel counts with values >0.166 after the 45 and 135 degree filters is shown in Figure 9B. Thus our second type of signal/noise analysis yields a threshold for detecting synfire chain activity at about 100 observed neurons. Note that the validity of this second analysis does depend on using a matrix window that contains 5-10 of short diagonal stripes as in Figure 8. In this paper we have used data from simulations of fully connected synfire chains, i.e. each neuron in a given link receives input from all neurons of the preceding link but with other parameter values set so there were about 30% failures of complete chain propagation. If we increase the link width but keep the number of converging inputs to each neuron the same as before, we have a diluted chain with partial connectivity. The detection of chain activity in such this case will be easier than in the fully connected case. During a run of this effectively wider chain the set of active neurons in each link will be larger and the set intersections between any two chain activations will also be larger (the numerator of Equation 1). The denominator of Equation 1, however, includes the amount of ongoing background activity at that time bin, and therefore increases by a smaller fraction than does the numerator. Therefore the normalized matrix pixel value corresponding to a particular link activity repetition will increase. One could also use just the un-normalized pixel value (i.e. just the numerator of Equation 1) although variability in firing may make this measure more noisy. Other parametric chain changes such as reduction of the converging input number would cause many more propagation failures which in turn would shorten many of the signature diagonal lines in the matrix and might make detection of synfire activity more difficult. In the signal-to-noise analyses of Figure 9 we repeatedly sampled (without replacement) various numbers of excitatory neurons from a total pool of 50,000. Each excitatory neuron in the pool was involved in several of the 50 synfire chains, and a given chain comprised about 4% of

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18 the total pool. Consider dilution of the data so that we leave intact the pool size and the neurons in some smaller number of chains, but replace the rest of the data by similar rate neurons that do not participate in chain activity. The only result in the intersection analysis matrix (like Figure 7) will be a diminution of the number of short diagonal stripes, but not their intensity or contrast. If on the other hand the dilution process also replaces neurons in the remaining chains, the detection of synfire activity becomes more difficult and depends on the fraction of pool size represented by each remaining (now only partially observable) synfire chain. With a dilution such that the observable part of a given synfire chain comprises about 1% of the neuron pool, detection requires simultaneous observation of 200-500 neurons (not shown).

Intersecting Synfire Chains In the case of intersecting synfire chains we might have some or all neurons in a given link of chain A also appear in a given link of chain B. In such a situation activity propagating along either chain could begin to propagate in the other chain starting at the shared link. A cartoon of his situation is illustrated in Figure 10A. Figure 10B is the associated raster with remapped Identities showing activities of chain 1 in raster lines 1-2,000 and chain 2 in raster lines 2,001-4,000. Link 10 comprises the same 100 neurons in both chains, and it is clear that activity in chain 2 begins when link 10 of chain 1 is active (or vice-versa if chain 2 is stimulated). In terms of the sets of active neurons in the appropriate time bins, this means after the 10th links are active that there will be an increase of active set size used as denominator of Equation 1 (less than doubling since there is also background activity). There will also be a doubling of intersection value to a repetition of the (double) chain run (used as numerator in Equation 1). The increase in

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19 pixel value in the Ms matrix (Equation 1) is clearly visible in the gray levels of Figure 10C which shows an enlarged and filtered image of the diagonal stripes corresponding to the three 2-1 chain firing repetitions shown in Figure 10B. For this purpose we could also have used the unnormalized matrix of intersection values, i.e. the numerator of Equation 1 alone. This may however be more noisy if activities are variable between runs. If instead link 10 of chain 1 were identical with link 5 of chain 2 we would expect a longer stripe with a profile along the stripe that increases and then returns to the single values. Thus within the limitations of count statistics it is possible in simple cases to infer details of intersecting synfire chains, using either normalized or un-normalized versions of the matrix.

More generally, detailed examination of the identities and numbers contributing to successive pixels of one or more short diagonal stripes could be used both to determine the membership of a particular chain (described in more detail in the last section of methods) and to detect partial coupling between chains with resulting probabilistic ignition. Propagation failures would shorten the corresponding diagonal stripe, and could be assigned to the responsible chain. However, general methods for appropriate comparisons of identity lists and for interpretation of such comparisons is a meta problem that remains to be addressed. Another aspect of intersecting chains occurs in Figure 8A in the short stripe labeled (1,2). This stripe represents the neuron identity intersection of the 2nd half of a full run of chain 2 with an earlier run of the 2nd half of chain 2 which was caused by a full run of chain 1. Thus unlike most of the diagonal stripes, this does not represent a repetition of a directly stimulated synfire chain. Such a detailed explanation is only possible here because we know all about the data; it would not be possible in the case of experimental data.

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20 Background and Blobs The small diagonal lines in the intersection matrix that are the signature of synfire chain activity have pixel values near 0.6 and the gray scale of the figures in this paper has been chosen to emphasize these larger values. As shown in Figure 11, a change of gray scale in Figure 7 allows examination of the many small values in the intersection matrix; these take the form of a general non-uniform background haze. There are broad, diffuse vertical and horizontal bands with blobs of somewhat higher density at their intersections. With high temporal resolution (not shown) individual pixels are spaced and have roughly the same low values whether in or out of a broad band or blob; it is the density of pixels that creates these features. Referring back to Figures 3B and 4, we note that the fluctuations of overall population firing rates have a similar time structure. During periods of higher population rate the set of active neurons in each time bin is larger and correspondingly the probability of a small identity overlap between two close times within a rate surge is higher, thus yielding a higher density of small pixels in the matrix. There is little overlap of neuron identities in the events leading to different broad bands or different blobs. We have demonstrated above (Figure 4) that much of the population rate fluctuation in these data is caused by overlap of synfire chain stimulations. Similar population rate fluctuations in randomly connected nets without any synfire structures remain to be investigated by the active population intersection methods of this paper.

Controls

We use data from a number of simulated networks specified in detail below. For each such data set the intersection matrix is featureless, with randomly distributed darker and lighter pixels. An example from a stimulated random network is shown in Figure 12. For all other

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21 control cases the intersection matrices are similar and are therefore not shown.

(a) The first data set consists of 40,000 independent inhomogeneous spike trains with a gamma interval distribution of order 4, and a noisy rate profile matching the average population firing rates in the synfire simulations. Individual neuron firing rates are partially correlated so that the population firing rate is a noisy oscillation similar to that shown in Figure 4. (b) The second data set consists of spike trains from a randomly connected network, each neuron with the same general parameters and degree of inward and outward connectivity as in the synfire networks. Random activity without specific external stimulation. (c) The third data set consists of spike trains from the same randomly connected network but with additional independent Poisson stimulation of 50 arbitrarily chosen groups of 100 neurons, matching the stimulation conditions used in the synfire simulations (Figure 12). (d) The fourth data set consists of spike trains from a network with connectivity for 50 synfire chains but without the external stimulation (Y in Figure 1) that was used to start chain activity. The network had only its intrinsic "spontaneous" activity. In an additional control procedure we have increasingly disorganized the orderly timing progression of activity along a synfire chain. This was done by dithering, i.e. randomly shifting within some time window the roughly synchronous firing of all neurons in each link of each chain. For several values of dithering time window Figure 13 shows a raster display of a typical run of dithered chain data, and also shows a portion of the corresponding intersection matrix. Note that this procedure leaves intact the original approximate synchrony of activity in each 100 neuron link of the chain, but destroys the systematic sequential timing between successive links. As the sequential nature of the link firing timing is increasingly disorganized (Figure 13 top to bottom) the corresponding short diagonal lines in the matrix blur and dissolve. With a dither time

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22 window of 50ms, which exceeds the ~45msec duration of a normal synfire sequence, the intersection matrix has no remaining structure and shows only a random scattering of high value pixels.

A final control calculation has to do with the possible consequences of bad spike sorting in multi-neuron recording. Spike shape sorting is required when more than one neuron is recorded on a given (multi-) electrode. Although the literature is full of methods, sorting continues to be somewhat problematic and frequently results to some extent in multi rather than single neuron spike trains (Harris et al. 2000). Spike train measures related to synchrony, auto- and crosscorrelation and time structure in general are considerably impacted by bad sorting (Gerstein 2000, Pazienti et al. 2006), although population measures are little affected. It is therefore appropriate to examine the sensitivity of the intersection matrix to bad spike sorting. Data sets mimicking this situation were produced by overlaying sets of 5 or 10 randomly selected spike trains in the original 50,000 neuron synfire chain simulation file analyzed in Figure 7, thus producing data sets with either 10,000 or 5,000 “mixed” spike trains. The resulting intersection matrices have somewhat larger values in the background gray pixels than in Figure 7, but all the short diagonal lines continue to be dark and prominent. Thus even in this rather worst case of bad sorting scenario, the intersection matrix calculation is robust, and continues to deliver the same information as in a clean, well isolated neuron data situation.

These several control computations demonstrate clearly that the short diagonal structures seen in the intersection matrices of Figures 6-8, 11-13 do not arise accidentally from various other types of network activity, and are in fact the unique signatures of the orderly and sequential activities of groups of neurons arranged in synfire chains.

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23 DISCUSSION

In this paper we have presented a new method related to “recurrence plots” for detection and visualization of synfire chain activity. The calculation examines the normalized intersection of identities for neurons firing at any two distinct (binned) times i and j. With a binning resolution of milliseconds, the rapid and orderly sequence of link activation in a synfire chain produces a signature of short diagonal lines in the intersection matrix M(i,j). We have shown that two related approaches are possible, one based on set intersections, one on vector representation of firing. Both types of measures are normalized to 0/1 but behave somewhat differently. The set intersection approach is computationally more convenient with very large numbers of neurons as in the simulated nets used in this paper. In comparison to the vector approach, it also offers an improvement in contrast between high and low identity overlaps when the number of active neurons (set sizes) at times i and j differ. We have demonstrated that limited random samples of the order of 100 to 200 neurons from the large network are sufficient to detect synfire chains if they are present. This level of sensitivity should in the fairly near future allow experimental test and either verification or rejection of the synfire idea. Current recording methods, electrode and optical, are allowing simultaneous observation of some 100 neurons in vivo, and rapid improvements can be expected. We note that in the simulated data used in this paper all excitatory neurons were involved in several synfire chains and that the neurons from any one synfire chain comprised about 4% of the total pool of observed neurons. Detection required a sample of about 100 neurons from the total pool. In experimental data from real nervous systems it may be that each synfire chain will comprise a smaller percentage of the observed neuron pool. This form of dilution would require larger random sampling numbers: if a single synfire chain comprises only 1% of the total pool it

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24 is necessary to sample 300-500 neurons for detection. The vector representation of neuronal firing activity in some time window appears in the literature in a number of ways. It has been used in comparing simultaneously or sequentially recorded responses from several neurons to different and/or repeating stimuli (Georgopoulos et al.1983; Schild 1988; DiLorenzo 1989). In these studies the response of each of the N neurons in the observed population is characterized by a spike count over some appropriate single time window for each repetition of the stimulus. Clustering in the N dimensional population response space is used to predict the stimulus parameters from the neural activity. More detailed “activity vectors” (the time pattern of spikes after a stimulus) have been used to examine variability of single unit responses to repeated stimulus presentations (Schreiber et al. 2003; Sasaki et al. 2007). Vector representation in the sense used in the present paper (an N length 0/1 vector representing data with millisecond time resolution) also underlies unitary event analysis (Riehle et al. 1997; Grün et al. 2002). In that work such vectors are compared to what is expected from the firing rates of the individual neurons. Comparison of such vectors at two different times as in the present paper (but with very low time resolution >1sec and consequently vectors that may not be 0/1) has been used to follow the slowly varying state of an observed neuronal network both by a matrix representation and with a principal components analysis (Sasaki et al. 2007). Although computationally similar in comparing vectors (or active neuron sets) at two times, in the present paper we have used a time resolution of 3 milliseconds which is sufficiently small to make vectors that are always 0/1. Such time resolution is essential in order to detect the firing of synfire chains with the expected time scale of their ordered activity. A similar display has independently been developed in the field of music processing (Goto 2006). With a time bin resolution at the value of inter link delays expected in a synfire chain, the short diagonal stripes appearing in the intersection matrix each represent a particular repetition of

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25 activation of a particular chain. Repeated activity of a particular chain therefore appears as multiple short diagonal stripes arranged in rows and columns corresponding to the chain activation times. Different chains form separate series of short diagonal stripes. Examination of the details in a particular short diagonal stripe yields considerable information about the synfire chain properties. For example if in one repetition the activity of a synfire chain dies out before running through all links, all signature diagonal stripes involving that repetition will be shorter than the full chain length. If there are partially shared links between two chains this will be evident in the intersection identities and by modulation of the intersection numbers along the signature short diagonal stripe. Partial or full ignition of one chain by another can be extracted from the intersection identities. Link propagation times and their variation can be estimated from the way the signature diagonal is made up of short offset diagonal line segments. The precision of synchronization in a synfire chain link (temporal jitter in spike coincidences), and the delay between the activation of successive neuron links are two distinct biophysically determined parameters in the simulation. Synchronization is mainly determined by the rise time of the post-synaptic potential (Gödeke and Diesmann 2008), while interlink delay is determined by conduction time or synaptic delay. Such parametric properties and issues will be examined in a subsequent paper about the network simulator. In the present application, the bin size of our analysis needs to be adjusted to a compromise between the effects of these two parameters. When the synaptic delay is larger than the precision of synchronization and varies between synfire links, the signature of repeated synfire activity in the matrix remains a dark stripe at 45 degrees. However, in this situation, the stripe may contain holes with values of background activity or may consist of several contiguous shorter diagonal stripes offset by one bin. For this reason processing the matrix with a 45 degree diagonal filter generally enhances the detection of synfire activity. Fluctuations in background activity may also scatter the matrix elements

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26 comprising the stripe away from the diagonal; a 45 degree diagonal filter extending a few bins perpendicular to the orientation of the diagonal may then be useful. One may wonder why the simple analysis presented here may perform better than the elaborate statistical tools previously developed to detect the presence of individual spatiotemporal spike patterns. A conceptual difference is that our method automatically solves the puzzle of which patterns to consider as sub-patterns of a larger one. We directly obtain a prominent diagonal matrix structure if synfire activity is present. Previously developed methods applied to such data detect many individual repeating patterns of neuron firings which do mostly originate from individual chains. However, such detected patterns must then be combined to determine the overall pattern of synfire activity, a computationally daunting project in a blind situation of real rather than simulated data. By intersecting identities of neurons firing at two different times our method integrates over the space of recorded neurons. By visual or filtered detection of diagonal structures that signify synfire activity, the method integrates over the temporal domain. This spatio-temporal integration provides sensitivity and robustness to the method.

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27 ACKNOWLEDGEMENTS

Major parts of this work were conducted in sequence of three scientific stays: Aug. 2005, SG and MD with GG at Jay, New York; Sept. to Dec., 2006, GG with SS and MD at BCCN Freiburg; Mar. 2007, GG with SG and MD at RIKEN BSI. Partially supported by DIP F1.2, EU Grant 15879 (FACETS) and BMBF Grant 01GQ0420 to BCCN Freiburg. We thank Moshe Abeles for urging development of a sensitivity test based on the full intersection matrix.

REFERENCES

1.Abeles M. Local Cortical Circuits: An Electrophysiological Study. Springer-Verlag, 1982.

2.Abeles M and Gerstein GL. Detecting Spatiotemporal Firing Patterns Among Simultaneously Recorded Single Neurons. J Neurophysiol 60: 909-924, 1988.

3.Abeles M. Corticonics: Neural Circuits of the Cerebral Cortex. Cambridge University Press, 1991.

4.Abeles M, Bergman H, Margalit E and Vaadia E. Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J Neurophysiol 70: 1629-1638, 1993.

5.Aertsen A, Gerstein GL, Habib M and Palm G. Dynamics of Neuronal Firing Correlation: Modulation of `Effective Connectivity'. J Neurophysiol 61: 900-917, 1989.

6.Amit D and Brunel N. Model of Global Spontaneous Activity and Local Structured Activity

Page 28 of 54

28 During Delay periods in the Cerebral Cortex. Cereb Cortex 7: 237-252, 1997.

7.Aviel Y, Mehring C, Abeles M and Horn D. On embedding synfire chains in a balanced network. Neural Comput 15: 1321-1340, 2003.

8.Baker S and Lemon RN. Precise Spatiotemporal Repeating Patterns in Monkey Primary and Supplementary Motor Areas Occur at Chance Level. J Neurophysiol 84: 1770-1780, 2000.

9.Baker S, Spinks R, Jackson A and Lemon RN. Synchronization in monkey motor cortex during a precision grip task. I. Task-dependent modulation in single-unit synchrony. J Neurophysiol 85: 869-885, 2001.

10. Bienenstock E. A model of neocortex. Network: Computation in Neural Systems 6: 179224, 1995.

11. Braitenberg V and Schüz A. Anatomy of the Cortex: Statistics and Geometry. SpringerVerlag, 1991.

12. Brunel N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8: 183-208, 2000.

13. Csicvari J, Henze DA, Jamieson B, Harris KD, Sirota A, Bartho P, Wise KD and Buszáki G. Massively parallel recording of unit and local field potentials with siliconbased electrodes. J Neurophysiol 90: 1314-1323, 2003.

Page 29 of 54

29 14. Diesmann M, Gewaltig MO and Aertsen A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402: 529-533, 1999.

15. DiLorenzo PM. Across unit patterns in the neural response to taste: vector space analysis . J Neurophysiol 62: 823-833, 1989.

16. Donoghue J. Connecting cortex to machines: recent advances. Nature Neurosci 5: 10851989, 2002.

17. Douglas RJ and Martin KAC. Neural circuits of the neocortex. Ann Rev Neurosci 27: 1419-451, 2004.

18. Euston DR, Tatsuno M and McNaughton BL. Fast forward playback of recent memory sequences in prefrontal cortex during sleep. Science 138: 1147-1150, 2007.

19. Georgopoulos AP, Caminiti R, Kalaska JF and Massey JT. Spatial coding of movement: A hypothesis concerning the coding of movement direction by motor cortical populations. Experimental Brain Research [Suppl ] 7: 327-336, 1983.

20. Gerstein GL. Cross-correlation measures of unresolved multi-neuron recordings. J Neurosci Methods 100: 41-51, 2000.

21. Gerstein GL. Searching for significance in spatio-temporal firing patterns. Acta neurobiologiae experimentalis (Warschau) 2: 203, 2004.

Page 30 of 54

30 22. Gewaltig MO and Diesmann M. NEST (Neural Simulation Tool) [Online]. Scholarpedia 2(4):1430. http://www.scholarpedia.org/article/NEST [2008].

23. Goedeke S and Diesmann M. The mechanism of synchronization in feed-forward neuronal networks. New Journal of Physics 10: 015007.

24. Goto M. A Chorus Section Detection Method for Musical Audio Signals and Its Application to a Music Listening Station. IEEE Transactions on Audio, Speech and Language Processing 14(5): 1783-1794, 2006.

25. Griffith J and Horn G. Functional coupling between cells in the visual cortex of unrestrained cat. Nature 199: 893-895, 1963.

26. Grün S, Diesmann M and Aertsen A. ‘Unitary Events’ in Multiple Single-Neuron Spiking Activity. I. Detection and Significance. Neural Comput 14: 43-80, 2002.

27. Harris KD, Henze DA, Csicsvari J, Hirase H and Buszáki G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J Neurophysiol 84(1): 401-414, 2000.

28. Hayon G, Abeles M and Lehmann D. Modeling compositionality by dynamic binding of synfire chains. J Comput Neurosci 17: 179-201, 2004.

29. Hayon G, Abeles M and Lehmann D. A model for representing the dynamics of a system of synfire chains. J Comput Neurosci 18: 41-53, 2005.

Page 31 of 54

31 30. Hebb D. The organization of behavior: A neuropsychological theory. John Wiley & Sons, 1949.

31. Ikegaya Y, Aaron G, Cossart R, Aronov D, Lampl I, Ferster D and Yuste R. Synfire chains and temporal songs: temporal modules of cortical activity. Science 304: 559-564, 2004.

32. Kohn A and Smith M. Stimulus dependence of neuronal correlations in primary visual cortex of the Macaque. J Neurosci 25: 3661-3673, 2005.

33. Kriener B, Tetzlaff T, Diesmann M, Aertsen A and Rotter S. Correlations and population dynamics in cortical networks. Neural Comput 20(9): 2185-2226, 2008.

34. Marwan N, Romano MC, Thiel M and Kurths J. Recurrence plots for the analysis of complex systems. Physics Reports 438: 237-329, 2007.

35. Mehring C, Hehl U, Kubo M, Diesmann M and Aertsen A. Activity dynamics and propagation of synchronous spiking in locally connected random networks. Biol Cybern 88: 395-408, 2003.

36. Morrison A, Straube S, Plesser HE and Diesmann M. Exact subthreshold integration with continuous spike times in discrete time neural network simulations. Neural Comput 19: 47-79, 2007.

37. Nicolelis M and Ribeiro S. Multielectrode recordings: the next steps. Curr Op Neurobiol

Page 32 of 54

32 12(5): 602-606, 2002.

38. Ohki K, Chung S, Ch'ng YH, Kara P and Reid RC. Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 433: 597-603, 2005.

39. Ohki K, Chung S, Kara P, Hübener M, Bonhoeffer T and Reid RC. Highly ordered arrangement of single neurons in orientation pinwheels. Nature 442: 925-928, 2006.

40. Pazienti A and Grün S. Robustness of the significance of spike correlation with respect to sorting errors. J Comp Neurosci 21: 329-342, 2006.

41. Petersen CCH and Sakmann B. The excitatory neuronal network of rat layer 4 barrel cortex. J Neurosci 20(20): 7579-7586-342, 2000.

42. Plesser HE, Eppler JM, Morrison A, Diesmann M and Gewaltig MO. Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers. Lecture Notes in Computer Science 4641: 672-681. 2007.

43. Prut Y, Vaadia E, Bergman H, Haalman I, Slovin H and Abeles M. Spatiotemporal Structure of Cortical Activity: Properties and Behavioral Relevance. J Neurophysiol 79: 2857-2874, 1998.

44. Riehle A, Grün S, Diesmann M and Aertsen A. Spike Synchronization and Rate Modulation Differentially Involved in Motor Cortical Function. Science 278: 1950-1953, 1997.

Page 33 of 54

33 45. Rolston JD, Wagenaar DA and Potter SM. Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures. Neuroscience 18: 294-303, 2007.

46. Rotter S and Diesmann M. Exact digital simulation of time-invariant linear systems with applications to neuronal modelling. Biol Cybern 81: 381-402, 1999.

47. Sasaki T, Matsuki N and Ikegaya Y. Metastability of Active CA3 Networks. J Neurosci 27: 517–528, 2007.

48. Schild D. Principles of odor coding and a neural network for odor discrimination. Biophys J 54: 1001-1011, 1988.

49. Schrader S, Morrison A and Diesmann M. A composition machine for complex movements. Proceedings of the 31st Göttingen Neurobiology Conference, p TS18-1C, 2007. Available online at http://www.neuro.uni-goettingen.de/archiv/2007/pdf/ProceedingsGoettingen2007.pdf

50. Schreiber S, Fellous JM, Whitmer D, Tiesinga P and Sejnowski TJ. A new correlationbased measure of spike timing reliability. Neurocomputing 925-931, 2003.

51. Song S, Sjöström PJ, Reigl M, Nelson S and Chklovskii DB. Highly non-random features of synaptic connectivity in local cortical circuits. Public Library of Science, Biology 3(3): 0507-0519, 2005.

52. Tetzlaff T, Morrison A, Timme M and Diesmann M. Heterogeneity breaks global

Page 34 of 54

34 synchrony in large networks. Proceedings of the 30th Göttingen Neurobiology Conference, p 206B, 2005. Available online at http://www.neuroanatomie.unigoettingen.de/neurobio_archiv/2005/pdf/Proceedings-Goettingen2005.pdf.

53. Vogels T, Rajan K and Abbott LF. Neural Network Dynamics. Annu RevNeurosci 28: 357-376, 2005.

54. Vreeswijk C and Sompolinsky H. Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity. Science 274: 1724-1726, 1996.

55. Warren DJ, Fernandez E and Normann RA. High-resolution two-dimensional spatial mapping of cat striate cortex using a 100-microelectrode array. Neuroscience 105(1): 19-31, 2001.

56. Yosimura Y, Dantzker JL and Callaway EM. Excitatory cortical neurons form fine-scale functional networks. Nature 433(24): 868-873, 2005.

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35 TABLES

N;T 50,000;100s 500; 100s 500; 50s 100; 100s 100; 50s

Matrix 58m 32m 16m 14m 7m

Pattern 7.4h 1.9h 0.8h 0.2h

Table 1. Computing time comparisons. Five different subsets of neurons from a single network simulation are analyzed. N = number of neurons in data; T data duration (simulated seconds); Analysis durations in minutes (m) or hours (h); Matrix = intersection matrix method; Pattern = pattern search (Abeles and Gerstein 1988). Application of the pattern search to the largest data set (50,000 neurons) is computationally unfeasible. For the pattern program the computing time changes as the square of data length, and changes by about 1.9x the multiplication factor of N, the number of neurons. For the matrix program the computing time is linear data length, for N