Detection of task-related synchronous firing patterns

Poster presentation: Background To test the importance of synchronous neuronal firing for information processing in the brain, one has to investigate if synchronous firing strength is correlated to the experimental subjects. This requires a tool that can compare the strength of the synchronous firing across different conditions, while at the same time it should correct for other features of neuronal firing such as spike rate modulation or the auto-structure of the spike trains that might co-occur with synchronous firing. Here we present the bi- and multivariate extension of previously developed method NeuroXidence [1,2], which allows for comparing the amount of synchronous firing between different conditions. ..

How specific is synchronous neuronal firing? : Poster presentation

Background Synchronous neuronal firing has been discussed as a potential neuronal code. For testing first, if synchronous firing exists, second if it is modulated by the behaviour, and third if it is not by chance, a large set of tools has been developed. However, to test whether synchronous neuronal firing is really involved in information processing one needs a direct comparison of the amount of synchronous firing for different factors like experimental or behavioural conditions. To this end we present an extended version of a previously published method NeuroXidence [1], which tests, based on a bi- and multivariate test design, whether the amount of synchronous firing above the chance level is different for different factors

How specific is synchronous neuronal firing? : Poster presentation

Background Synchronous neuronal firing has been discussed as a potential neuronal code. For testing first, if synchronous firing exists, second if it is modulated by the behaviour, and third if it is not by chance, a large set of tools has been developed. However, to test whether synchronous neuronal firing is really involved in information processing one needs a direct comparison of the amount of synchronous firing for different factors like experimental or behavioural conditions. To this end we present an extended version of a previously published method NeuroXidence [1], which tests, based on a bi- and multivariate test design, whether the amount of synchronous firing above the chance level is different for different factors

NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events

We present a non-parametric and computationally efficient method named NeuroXidence that detects coordinated firing of two or more neurons and tests whether the observed level of coordinated firing is significantly different from that expected by chance. The method considers the full auto-structure of the data, including the changes in the rate responses and the history dependencies in the spiking activity. Also, the method accounts for trial-by-trial variability in the dataset, such as the variability of the rate responses and their latencies. NeuroXidence can be applied to short data windows lasting only tens of milliseconds, which enables the tracking of transient neuronal states correlated to information processing. We demonstrate, on both simulated data and single-unit activity recorded in cat visual cortex, that NeuroXidence discriminates reliably between significant and spurious events that occur by chance

Bivariate and Multivariate NeuroXidence: A Robust and Reliable Method to Detect Modulations of Spike–Spike Synchronization Across Experimental Conditions

Synchronous neuronal firing has been proposed as a potential neuronal code. To determine whether synchronous firing is really involved in different forms of information processing, one needs to directly compare the amount of synchronous firing due to various factors, such as different experimental or behavioral conditions. In order to address this issue, we present an extended version of the previously published method, NeuroXidence. The improved method incorporates bi- and multivariate testing to determine whether different factors result in synchronous firing occurring above the chance level. We demonstrate through the use of simulated data sets that bi- and multivariate NeuroXidence reliably and robustly detects joint-spike-events across different factors

Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory

Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferentially synchronize their spiking when stimulus information is processed. Furthermore, synchronous spike firing should intensify if memory processes require the induction of neuronal plasticity, even if this is only for short-term. Here we show with multiple simultaneously recorded units in ventral prefrontal cortex that neurons participate in 3 ms precise synchronous discharges distributed across multiple sites separated by at least 500 μm. The frequency of synchronous firing is modulated by behavioral performance and is specific for the memorized visual stimuli. In particular, during the memory period in which activity is not stimulus driven, larger groups of up to seven sites exhibit performance dependent modulation of their spike synchronization

Distributed processing and temporal codes in neuronal networks

The cerebral cortex presents itself as a distributed dynamical system with the characteristics of a small world network. The neuronal correlates of cognitive and executive processes often appear to consist of the coordinated activity of large assemblies of widely distributed neurons. These features require mechanisms for the selective routing of signals across densely interconnected networks, the flexible and context dependent binding of neuronal groups into functionally coherent assemblies and the task and attention dependent integration of subsystems. In order to implement these mechanisms, it is proposed that neuronal responses should convey two orthogonal messages in parallel. They should indicate (1) the presence of the feature to which they are tuned and (2) with which other neurons (specific target cells or members of a coherent assembly) they are communicating. The first message is encoded in the discharge frequency of the neurons (rate code) and it is proposed that the second message is contained in the precise timing relationships between individual spikes of distributed neurons (temporal code). It is further proposed that these precise timing relations are established either by the timing of external events (stimulus locking) or by internal timing mechanisms. The latter are assumed to consist of an oscillatory modulation of neuronal responses in different frequency bands that cover a broad frequency range from <2 Hz (delta) to >40 Hz (gamma) and ripples. These oscillations limit the communication of cells to short temporal windows whereby the duration of these windows decreases with oscillation frequency. Thus, by varying the phase relationship between oscillating groups, networks of functionally cooperating neurons can be flexibly configurated within hard wired networks. Moreover, by synchronizing the spikes emitted by neuronal populations, the saliency of their responses can be enhanced due to the coincidence sensitivity of receiving neurons in very much the same way as can be achieved by increasing the discharge rate. Experimental evidence will be reviewed in support of the coexistence of rate and temporal codes. Evidence will also be provided that disturbances of temporal coding mechanisms are likely to be one of the pathophysiological mechanisms in schizophrenia

Performance-dependent changes in monkey prefrontal cortex during short-term memory

Conclusion Scale Integration Based on the results of spike-field coherence, the underlying process of shortterm memory seems to involve networks of different sizes within and, most probably, beyond prefrontal cortex. Spikes, which were generated by single neurons, cooperate with local field potentials, which were the slower fluctuations of the environment. Although differences among behavioral conditions appear to be based on rather few instances of phase-locked spikes, the task-related effects on spike-field coherence are highly reliable and cannot be explained by chance, as the comparison of results from experimental and simulated data shows. The differential locking of prefrontal neuron populations with two different frequency bands in their input signals suggests that neuronal activity underlying short-term memory in prefrontal cortex transiently engages cortical circuits on different spatial scales, probably in order to coordinate distributed processes. NeuroXidence method and Synchronizedfiring Based on the results of the calibration datasets, for bi- and multi-variate cases, the extension of NeuroXidence remains its sensitivity and reliability of detecting coordinate firing events for different processes. Based on this extension of NeuroXidence, we demonstrated that in monkey’s prefrontal cortex during short-term memory task, encoding and maintenance of the information rely on the formation of neuronal assemblies characterized by precise and reliable synchronization of spiking activity on a millisecond time scale, which is consistent with the results from spike-spike coherence. The task and performance dependent modulation of synchrony reflects the dynamic formation of group of neurons has large effect on short-term-memory.Zwecks Untersuchung der neuronalen Verarbeitung im Kurzzeit-Gedächtnis nahmen wir im präfrontalen Kortex zweier Affen, welche eine visuelle Kurzzeitgedächtnisaufgabe lösten (0, 5 Sekunden Aufnahme, 3 Sekunden Verzögerung, 2 Sekunden Test), gleichzeitig LFPs und Spikes auf. Wir untersuchten das aufgenommene Signal auf der Grundlage der Richtig-Falsch-Antworten der Affen nach einem zugrunde liegenden Mechanismus im Kurzzeit-Gedächtnis des Affen. Zunächst analysierten wir verhaltensabhängige Veränderungen der Kopplung zwischen simultan abgeleiteten lokalen Feld-Potentialen (,LFPs’) und der Aktivität einzelner (,Single-Unit-Aktivität’) oder kleiner Gruppen von Neuronen (,Multi-Unit-Aktivität’), um die neuronalen Mechanismen im Kurzzeitgedächtnis bei der Informations-Kodierung und -Aufrechterhaltung über verschiedene räumliche Skalen hinweg zu untersuchen. Informationsverarbeitungs-Abläufe beinhalten neuronale Kreisläufe auf verschiedenen räumlichen Skalen. Ihr Beitrag kann mittels der Analyse verschiedener Signale wie von einzelnen oder wenigen einzelnen Neuronen (,Mikroskopisch’), kleineren Populationen von Neuronen (,Mesoskopisch’), und Massen-Signalen wie LFP (,Makroskopisch’) studiert werden. Interaktionen zwischen diesen verschiedenen Ebenen sind von besonderem Interesse, wenn die Informationsverarbeitung Verhaltensübergängen oder Zustandsänderungen unterliegt, selbst wenn diese klein sind. Wir studierten diese Interaktionen und testeten, ob eine Änderung der Beziehung zwischen der synaptischen Aktivität, gemessen durch das mesoskopische Signal des LFP und der Spike-Aktivität kleiner neuronaler Populationen im lateralen präfrontalen Kortex, wenn aufgenommene Information gespeichert und beim Vergleichen mit neuem Sinneseindruck wieder abgerufen werden muss, die Grundlage zur Wahl der passenden Verhaltensantwort ist. ..

Multiple firing coherence resonances in excitatory and inhibitory coupled neurons

The impact of inhibitory and excitatory synapses in delay-coupled Hodgkin--Huxley neurons that are driven by noise is studied. If both synaptic types are used for coupling, appropriately tuned delays in the inhibition feedback induce multiple firing coherence resonances at sufficiently strong coupling strengths, thus giving rise to tongues of coherency in the corresponding delay-strength parameter plane. If only inhibitory synapses are used, however, appropriately tuned delays also give rise to multiresonant responses, yet the successive delays warranting an optimal coherence of excitations obey different relations with regards to the inherent time scales of neuronal dynamics. This leads to denser coherence resonance patterns in the delay-strength parameter plane. The robustness of these findings to the introduction of delay in the excitatory feedback, to noise, and to the number of coupled neurons is determined. Mechanisms underlying our observations are revealed, and it is suggested that the regularity of spiking across neuronal networks can be optimized in an unexpectedly rich variety of ways, depending on the type of coupling and the duration of delays.Comment: 7 two-column pages, 6 figures; accepted for publication in Communications in Nonlinear Science and Numerical Simulatio