This letter presents a noninvasive imaging technique that captures the exact timing and locations of cortical activity sequences that are specific to a cognitive process. These precise spatiotemporal sequences can be detected in the human brain as specific time-position pattern associated with a cognitive task. They are consistent with direct measurements of population activity recorded in nonhuman primates, thus suggesting that specific time-position patterns associated with a cognitive task can be identified. This imaging technique is based on estimating the amplitude of cortical current dipoles from MEG recordings. Although the spatial resolution of these estimations is poor (approximately 2 cm), the temporal resolution is high (milliseconds). We show that within these cortical current dipoles, time points of cortical activation can be identified as brief amplitude undulations and that sequences of these transients repeat with millisecond accuracy, hence making it possible to treat the timing of these transients as point processes. We illustrate the feasibility of finding spatiotemporal templates specific to the cognitive processes associated with following the rhythm of drumbeats that involve the activation at multiple cortical and cerebellar loci. These templates evolve at an accuracy of a few milliseconds. This approach can thus pave the way for new perspectives on the relationships between brain dynamics and cognition.

Some studies suggest that complex arm movements in humans and monkeys may optimize several objective functions, while others claim that arm movements satisfy geometric constraints and are composed of elementary components. However, the ability to unify different constraints has remained an open question. The criterion for a maximally smooth (minimizing jerk) motion is satisfied for parabolic trajectories having constant equi-affine speed, which thus comply with the geometric constraint known as the two-thirds power law. Here we empirically test the hypothesis that parabolic segments provide a compact representation of spontaneous drawing movements. Monkey scribblings performed during a period of practice were recorded. Practiced hand paths could be approximated well by relatively long parabolic segments. Following practice, the orientations and spatial locations of the fitted parabolic segments could be drawn from only 2-4 clusters, and there was less discrepancy between the fitted parabolic segments and the executed paths. This enabled us to show that well-practiced spontaneous scribbling movements can be represented as sequences ("words") of a small number of elementary parabolic primitives ("letters"). A movement primitive can be defined as a movement entity that cannot be intentionally stopped before its completion. We found that in a well-trained monkey a movement was usually decelerated after receiving a reward, but it stopped only after the completion of a sequence composed of several parabolic segments. Piece-wise parabolic segments can be generated by applying affine geometric transformations to a single parabolic template. Thus, complex movements might be constructed by applying sequences of suitable geometric transformations to a few templates. Our findings therefore suggest that the motor system aims at achieving more parsimonious internal representations through practice, that parabolas serve as geometric primitives and that non-Euclidean variables are employed in internal movement representations (due to the special role of parabolas in equi-affine geometry).

Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. It is largely unknown how the spike train correlation structure is altered by this filtering and what the consequences for the dynamics of the system and for the interpretation of measured correlations are. In this study, we focus on linearly filtered spike trains and particularly consider correlations caused by overlapping presynaptic neuron populations. We demonstrate that correlation functions and statistical second-order measures like the variance, the covariance, and the correlation coefficient generally exhibit a complex dependence on the filter properties and the statistics of the presynaptic spike trains. We point out that both contributions can play a significant role in modulating the interaction strength between neurons or neuron populations. In many applications, the coherence allows a filter-independent quantification of correlated activity. In different network models, we discuss the estimation of network connectivity from the high-frequency coherence of simultaneous intracellular recordings of pairs of neurons.

We study the problem of memory capacity in balanced networks of spiking neurons. Associative memories are represented by either synfire chains (SFC) or Hebbian cell assemblies (HCA). Both can be embedded in these balanced networks by a proper choice of the architecture of the network. The size w(E) of a pool in an SFC or of an HCA is limited from below and from above by dynamical considerations. Proper scaling of w(E) by radicalK, where K is the total excitatory synaptic connectivity, allows us to obtain a uniform description of our system for any given K. Using combinatorial arguments, we derive an upper limit on memory capacity. The capacity allowed by the dynamics of the system, alpha(c), is measured by simulations. For HCA, we obtain alpha(c) of order 0.1, and for SFC, we find values of order 0.065. The capacity can be improved by introducing shadow patterns, inhibitory cell assemblies that are fed by the excitatory assemblies in both memory models. This leads to a doubly balanced network, where, in addition to the usual global balancing of excitation and inhibition, there exists specific balance between the effects of both types of assemblies on the background activity of the network. For each of the memory models and for each network architecture, we obtain an allowed region (phase space) for w(E)/ radicalK in which the model is viable.

A balanced network leads to contradictory constraints on memory models, as exemplified in previous work on accommodation of synfire chains. Here we show that these constraints can be overcome by introducing a 'shadow' inhibitory pattern for each excitatory pattern of the model. This is interpreted as a double- balance principle, whereby there exists both global balance between average excitatory and inhibitory currents and local balance between the currents carrying coherent activity at any given time frame. This principle can be applied to networks with Hebbian cell assemblies, leading to a high capacity of the associative memory. The number of possible patterns is limited by a combinatorial constraint that turns out to be P=0.06N within the specific model that we employ. This limit is reached by the Hebbian cell assembly network. To the best of our knowledge this is the first time that such high memory capacities are demonstrated in the asynchronous state of models of spiking neurons.

We investigate the formation of synfire waves in a balanced network of integrate-and-fire neurons. The synaptic connectivity of this network embodies synfire chains within a sparse random connectivity. This network can exhibit global oscillations but can also operate in an asynchronous activity mode. We analyze the correlations of two neurons in a pool as convenient indicators for the state of the network. We find, using different models, that these indicators depend on a scaling variable. Beyond a critical point, strong correlations and large network oscillations are obtained. We looked for the conditions under which a synfire wave could be propagated on top of an otherwise asynchronous state of the network. This condition was found to be highly restrictive, requiring a large number of neurons for its implementation in our network. The results are based on analytic derivations and simulations.

We present results from an ongoing electrophysiological study of cortical function in the awake, behaving monkey. Single and multiple neuron activity is recorded from the frontal cortex, while the monkey is engaged in a sensory-motor association task. Results show that neighboring neurons in the frontal cortex may be functionally related and share common features. However, even when neurons were reduced by the same microelectrode, they were not all activated in unison, nor did they all show the same functional properties. Correlation analysis reveals that interactions between neurons may strongly depend on stimulus context and/or behavioral state. Moreover, the interactions may be highly dynamic, with time constants of modulation as low as tens of milliseconds. These findings point at the need to distinguish between anatomical connectivity and functional coupling. The underlying mechanisms as well as the functional implications of such dynamic coupling in cortical networks are discussed.

The way in which three-spike correlation techniques may be used to study the organization of activity among three neurons and the temporal organization of activity of a single neuron is described. This analysis was carried out for single units recorded in the auditory cortex of nitrous-oxide-anesthetized cats. It revealed the occasional presence of three-spike patterns that cannot be explained by simple synaptic relations. Such patterns may appear during the spontaneous activity of the units or may be elicited by sound stimulation. When elicited by sound, the patterns may be time-locked to the stimulus, in which case they may be revealed also by peristimulus time (PST) histograms. On some occasions the three-spike patterns were not time-locked to the stimulus and therefore could not be revealed by PST histograms. It is suggested that spatiotemporal patterns of firing which are induced by a stimulus but are not time-locked to it may reflect higher levels of processing (such as perception, or interrogation of memory) elicited by the stimulus.