Precise Firing Sequences Research Articles

Endogenous sequential cortical activity evoked by visual stimuli.

Although the functional properties of individual neurons in primary visual cortex have been studied intensely, little is known about how neuronal groups could encode changing visual stimuli using temporal activity patterns. To explore this, we used in vivo two-photon calcium imaging to record the activity of neuronal populations in primary visual cortex of awake mice in the presence and absence of visual stimulation. Multidimensional analysis of the network activity allowed us to identify neuronal ensembles defined as groups of cells firing in synchrony. These synchronous groups of neurons were themselves activated in sequential temporal patterns, which repeated at much higher proportions than chance and were triggered by specific visual stimuli such as natural visual scenes. Interestingly, sequential patterns were also present in recordings of spontaneous activity without any sensory stimulation and were accompanied by precise firing sequences at the single-cell level. Moreover, intrinsic dynamics could be used to predict the occurrence of future neuronal ensembles. Our data demonstrate that visual stimuli recruit similar sequential patterns to the ones observed spontaneously, consistent with the hypothesis that already existing Hebbian cell assemblies firing in predefined temporal sequences could be the microcircuit substrate that encodes visual percepts changing in time.

A biologically plausible learning rule for the Infomax on recurrent neural networks.

A fundamental issue in neuroscience is to understand how neuronal circuits in the cerebral cortex play their functional roles through their characteristic firing activity. Several characteristics of spontaneous and sensory-evoked cortical activity have been reproduced by Infomax learning of neural networks in computational studies. There are, however, still few models of the underlying learning mechanisms that allow cortical circuits to maximize information and produce the characteristics of spontaneous and sensory-evoked cortical activity. In the present article, we derive a biologically plausible learning rule for the maximization of information retained through time in dynamics of simple recurrent neural networks. Applying the derived learning rule in a numerical simulation, we reproduce the characteristics of spontaneous and sensory-evoked cortical activity: cell-assembly-like repeats of precise firing sequences, neuronal avalanches, spontaneous replays of learned firing sequences and orientation selectivity observed in the primary visual cortex. We further discuss the similarity between the derived learning rule and the spike timing-dependent plasticity of cortical neurons.

Interneuron firing precedes sequential activation of neuronal ensembles in hippocampal slices

Neuronal firing sequences that occur during behavioral tasks are precisely reactivated in the neocortex and the hippocampus during rest and sleep. These precise firing sequences are likely to reflect latent memory traces, and their reactivation is believed to be essential for memory consolidation and working memory maintenance. However, how the organized repeating patterns emerge through the coordinated interplay of distinct types of neurons remains unclear. In this study, we monitored ongoing spatiotemporal firing patterns using a multi-neuron calcium imaging technique and examined how the activity of individual neurons is associated with repeated ensembles in hippocampal slice cultures. To determine the cell types of the imaged neurons, we applied an optical synapse mapping method that identifies network connectivity among dozens of neurons. We observed that inhibitory interneurons exhibited an increase in their firing rates prior to the onset of repeating sequences, while the overall activity level of excitatory neurons remained unchanged. A specific repeating sequence emerged preferentially after the firing of a specific interneuron that was located close to the neuron first activated in the sequence. The times of repeating sequences could be more precisely predicted based on the activity patterns of inhibitory cells than excitatory cells. In line with these observations, stimulation of a single interneuron could trigger the emergence of repeating sequences. These findings provide a conceptual framework that interneurons serve as a key regulator of initiating sequential spike activity.

Interpyramid Spike Transmission Stabilizes the Sparseness of Recurrent Network Activity

Cortical synaptic strengths vary substantially from synapse to synapse and exhibit a skewed distribution with a small fraction of synapses generating extremely large depolarizations. Using multiple whole-cell recordings from rat hippocampal CA3 pyramidal cells, we found that the amplitude of unitary excitatory postsynaptic conductances approximates a lognormal distribution and that in the presence of synaptic background noise, the strongest fraction of synapses could trigger action potentials in postsynaptic neurons even with single presynaptic action potentials, a phenomenon termed interpyramid spike transmission (IpST). The IpST probability reached 80%, depending on the network state. To examine how IpST impacts network dynamics, we simulated a recurrent neural network embedded with a few potent synapses. This network, unlike many classical neural networks, exhibited distinctive behaviors resembling cortical network activity in vivo. These behaviors included the following: 1) infrequent ongoing activity, 2) firing rates of individual neurons approximating a lognormal distribution, 3) asynchronous spikes among neurons, 4) net balance between excitation and inhibition, 5) network activity patterns that was robust against external perturbation, 6) responsiveness even to a single spike of a single excitatory neuron, and 7) precise firing sequences. Thus, IpST captures a surprising number of recent experimental findings in vivo. We propose that an unequally biased distribution with a few select strong synapses helps stabilize sparse neuronal activity, thereby reducing the total spiking cost, enhancing the circuit responsiveness, and ensuring reliable information transfer.

Functional Interactions in Hierarchically Organized Neural Networks Studied with Spatiotemporal Firing Patterns and Phase-Coupling Frequencies

A scalable hardware/software hybrid module--called Ubidule--endowed with bio-inspired ontogenetic and epigenetic features is configured to run a neural networks simulation with developmental and evolvable capabilities. We simulated the activity of hierarchically organized spiking neural networks characterized by an initial developmental phase featuring cell death followed by spike timing dependent synaptic plasticity in presence of background noise. An upstream 'sensory' network received a spatiotemporally organized external input and downstream networks were activated only via the upstream network. Precise firing sequences, formed by recurrent patterns of spikes intervals above chance levels, were observed in all recording conditions, thus suggesting the build-up of a connectivity able to sustain temporal information processing. The activity of a Ubinet--a network of Ubidules--is analyzed by means of virtual electrodes that recorded neural signals similar to EEG. The analysis of these signals was compared with a small set of human recordings and revealed common patterns of shift in quadratic phase coupling. The results suggest some interpretations of changes and plasticity of functional interactions between cortical areas driven by external stimuli and by learning/cognitive

A long-tailed EPSP distribution accounts for the up-state, low-rate asynchronous firings, and precise firing sequences in cortical networks

A long-tailed EPSP distribution accounts for the up-state, low-rate asynchronous firings, and precise firing sequences in cortical networks

Temporally Precise Cortical Firing Patterns Are Associated With Distinct Action Segments

Despite many reports indicating the existence of precise firing sequences in cortical activity, serious objections have been raised regarding the statistics used to detect them and the relations of these sequences to behavior. We show that in behaving monkeys, pairs of spikes from different neurons tend to prefer certain time delays when measured in relation to a specific behavior. Single-unit activity was recorded from eight microelectrodes inserted into the motor and premotor cortices of two monkeys while they were performing continuous drawinglike hand movements. Repeated scribbling paths, termed drawing components, were extracted by data-mining techniques. The set of the least predictable relations between drawing components and pairs of neurons was determined and represented by one statistic termed the relations score. The chance probability of the relations score was evaluated by teetering the spike times: 1,000 surrogates were generated by randomly teetering the original time of each spike in a small window. In nine of 13 experimental days the precision was better than 12 ms and, in the best case, spike precision reached 0.5 ms.

Detecting precise firing sequences in experimental data

A precise firing sequence (PFS) is defined here as a sequence of three spikes with fixed delays (up to some time accuracy Δ), that repeat excessively. This paper provides guidelines for detecting PFSs, verifying their significance through surrogate spike trains, and identifying existing PFSs. The method is based on constructing a three-fold correlation among spikes, estimating the expected shape of the correlation by smoothing, and detecting points for which the correlations significantly protrude above the expected correlation. Validation is achieved by generating surrogate spike trains in which the time of each of the real spikes is randomly jittered within a small time window. The method is extensively tested through application to simulated spike trains, and the results are illustrated with recordings of single units in the frontal cortex of behaving monkeys. Pitfalls which may cause false detection of PFSs, or loss of existing PFSs, include searching for PFSs in which the same neuron participates more than once, and attempting to produce a surrogate with some fixed statistical property.

Spatiotemporal structure of cortical activity: properties and behavioral relevance.

The study was designed to reveal occurrences of precise firing sequences (PFSs) in cortical activity and to test their behavioral relevance. Two monkeys were trained to perform a delayed-response paradigm and to open puzzle boxes. Extracellular activity was recorded from neurons in premotor and prefrontal areas with an array of six microelectrodes. An algorithm was developed to detect PFSs, defined as a set of three spikes and two intervals with a precision of +/-1 ms repeating significantly more than expected by chance. The expected level of repetition was computed based on the firing rate and the pairwise correlation of the participating units, assuming a Poisson distribution of event counts. Accordingly, the search for PFSs was corrected for rate modulations. PFSs were found in 24/25 recording sessions. Most PFSs (76%) were composed of spikes of more than one unit but usually not more than two units (67%). The PFSs spanned hundreds of milliseconds, and the average interval between two events within the PFSs was 200 ms. No traces of periodic oscillations were found in the PFS intervals. The bins of the matrix that were defined as PFSs were isolated temporally: the spikes that generated PFSs were not associated with high-frequency bursts or rapid coherent rate fluctuations. A given PFS tended to be correlated with the animal's behavior. Furthermore, for 19% of the PFS pairs that shared the same unit composition, each member of the pair was associated with a different type of behavior. The PFSs often appeared in clusters that were associated with particular phases of the behavior. The firing rate of single units did not provide a full explanation for the timing and structure of these clusters. A reduced spike train (RST) was defined for each unit by taking all spikes of that unit that were part of any PFS. In 88% of the cases the degree of modulation of the RST was higher than that of the complete spike train. The results suggest that relevant information is carried by the fine temporal structure of cortical activity. A coding scheme that involves such temporal structures is rich and sufficiently flexible to facilitate a rapid organization of cortical neurons into functional groups. The results can be accounted for by the synfire chain model, which suggests that cortical activity is mediated by synchronous activation of neural groups in a reverberatory mode.