Detecting synfire chains in parallel spike data.

The synfire chain model of brain organization has received much theoretical attention since its introduction (Abeles, 1982, 1991). However there has been no convincing experimental demonstration of synfire chains due partly to limitations of recording technology but also due to lack of appropriate analytic methods for large scale recordings of parallel spike trains. We have previously published one such method based on intersection of the neural populations active at two different times (Schrader et al., 2008). In the present paper we extend this analysis to deal with higher firing rates and noise levels, and develop two additional tools based on properties of repeating firing patterns. All three measures show characteristic signatures if synfire chains underlie the recorded data. However we demonstrate that the detection of repeating firing patterns alone (as used in several papers) is not enough to infer the presence of synfire chains. Positive results from all three measures are needed.

Surrogate spike train generation through dithering in operational time.

Detecting the excess of spike synchrony and testing its significance can not be done analytically for many types of spike trains and relies on adequate surrogate methods. The main challenge for these methods is to conserve certain features of the spike trains, the two most important being the firing rate and the inter-spike interval statistics. In this study we make use of operational time to introduce generalizations to spike dithering and propose two novel surrogate methods which conserve both features with high accuracy. Compared to earlier approaches, the methods show an improved robustness in detecting excess synchrony between spike trains.

Detecting synfire chain activity using massively parallel spike train recording.

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 feedforward subnetworks (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 spatiotemporal 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 50 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.

Measurement of time-dependent changes in the irregularity of neural spiking.

Irregularity of firing in spike trains has been associated with coding processes and information transfer or alternatively treated as noise. Previous studies of irregularity have mainly used the coefficient of variation (CV) of the interspike interval distribution. Proper estimation of CV requires a constant underlying firing rate, a condition that most experimental situations do not fulfill either within or across trials. Here we introduce a novel irregularity metric based on the ratio of adjacent intervals in the spike train. The new metric is not affected by firing rate and is very localized in time so that it can be used to examine the time course of irregularity relative to an alignment marker. We characterized properties of the new metric with simulated spike trains of known characteristics and then applied it to data recorded from 108 single neurons in the motor cortex of two monkeys during performance of a precision grip task. Fifty-six cells were antidromically identified as pyramidal tract neurons (PTNs). Sixty-one cells (30 PTNs) exhibited significant temporal modulation of their irregularity during task performance with the contralateral hand. The irregularity modulations generally differed in sign and latency from the modulations of firing rate. High irregularity tended to occur during the task phases requiring the most detailed control of movement, whereas neural firing became more regular during the steady hold phase. Such irregularity modulation could have important consequences for the response of downstream neurons and may provide insight into the nature of the cortical code.

Two enhancements of the gravity algorithm for multiple spike train analysis.

The gravity method for neuronal assembly analysis represents each neuron as a particle in N-space with a time varying charge that is a filtered version of the corresponding spike train, with appropriate rules for forces between and movements of the charged particles. Resulting trajectories reflect neuronal timing relationships. The usual short time constants in the filter restrict aggregation to highly synchronized neurons and reduce the sensitivity for delayed correlations; long time constants in the filter reduce selectivity. Here we describe an enhancement that modifies rules for assigning charge increment times to allow mixtures of short and long lag correlations. Charge increments for each pair are offset from the actual spike times by time lags defined by features in corresponding cross-correlograms; no such charge offsets are invoked if the correlogram is flat. Tuning increases charge products and aggregation of long lag correlated pairs. A second enhancement uses a new three-dimensional display of particle pair trajectories to parse the type of neuronal relationship. For each pair, we record and display the inter-particle distance and the distance each particle moves from its original location in the N-space. The resulting trajectories cluster according to the type of interaction between the represented neurons. Results from simulated networks and in vivo multi-site recordings show that these modifications detect assembly properties not identified by the standard methods.

Searching for significance in spatio-temporal firing patterns.

We examine a specific candidate for temporal coding of information by spike trains, the occurrence of a temporal firing pattern among some number of neurons that repeats more often than expected by chance. Methods for detection of repeating patterns have long been available, but there are no analytic methods for calculating the expected numbers of repeating patterns to enable assignment of significance to the results from the experimental data. The expected numbers can be calculated by Monte-Carlo methods by repeatedly modifying the original data spike trains. Ideally the surrogates produced by such changes should destroy all patterns and cross-correlations but preserve other aspects of the trains such as rate, interval structure etc. We present here a novel variant of the "dither surrogate" (Date et al. 1998) and use surrogates generated by this algorithm to evaluate repeating pattern significance in data recorded in monkey motor cortex during behavior. Although we can demonstrate high statistical significance for the excess repetition of some spike patterns, it is not obvious that this has physiological meaning or that such patterns are used for information transfer.

A chronic multi-electrode microdrive for small animals.

We describe a simple microdrive device appropriate for chronic microelectrode recording in rats. No precision machining is required; all parts are stock or cut from standard stock material with hand tools and assembled with epoxy. The device together with its electrodes can be discarded at the completion of the experiment.