Optimal synchrony state for maximal information transmission

Energy-efficient population coding constrains network size of a neuronal array system

We consider the open issue of how the energy efficiency of the neural information transmission process, in a general neuronal array, constrains the network size, and how well this network size ensures the reliable transmission of neural information in a noisy environment. By direct mathematical analysis, we have obtained general solutions proving that there exists an optimal number of neurons in the network, where the average coding energy cost (defined as energy consumption divided by mutual information) per neuron passes through a global minimum for both subthreshold and superthreshold signals. With increases in background noise intensity, the optimal neuronal number decreases for subthreshold signals and increases for suprathreshold signals. The existence of an optimal number of neurons in an array network reveals a general rule for population coding that states that the neuronal number should be large enough to ensure reliable information transmission that is robust to the noisy environment but small enough to minimize energy cost.

Modulatory effects of inhibition on persistent activity in a cortical microcircuit model

Neocortical network activity is generated through a dynamic balance between excitation, provided by pyramidal neurons, and inhibition, provided by interneurons. Imbalance of the excitation/inhibition ratio has been identified in several neuropsychiatric diseases, such as schizophrenia, autism and epilepsy, which also present with other cognitive deficits and symptoms associated with prefrontal cortical (PFC) dysfunction. We undertook a computational approach to study how changes in the excitation/inhibition balance in a PFC microcircuit model affect the properties of persistent activity, considered the cellular correlate of working memory function in PFC. To this end, we constructed a PFC microcircuit, consisting of pyramidal neuron models and all three different interneuron types: fast-spiking (FS), regular-spiking (RS), and irregular-spiking (IS) interneurons. Persistent activity was induced in the microcircuit model with a stimulus to the proximal apical dendrites of the pyramidal neuron models, and its properties were analyzed, such as the induction profile, the interspike intervals (ISIs) and neuronal synchronicity. Our simulations showed that (a) the induction but not the firing frequency or neuronal synchronicity is modulated by changes in the NMDA-to-AMPA ratio on FS interneuron model, (b) removing or decreasing the FS model input to the pyramidal neuron models greatly limited the biophysical modulation of persistent activity induction, decreased the ISIs and neuronal synchronicity during persistent activity, (c) the induction and firing properties could not be altered by the addition of other inhibitory inputs to the soma (from RS or IS models), and (d) the synchronicity change could be reversed by the addition of other inhibitory inputs to the soma, but beyond the levels of the control network. Thus, generic somatic inhibition acts as a pacemaker of persistent activity and FS specific inhibition modulates the output of the pacemaker.

Magnetoencephalography in the study of epilepsy and consciousness

The neural bases of altered consciousness in patients with epilepsy during seizures and at rest have raised significant interest in the last decade. This exponential growth has been supported by the parallel development of techniques and methods to investigate brain function noninvasively with unprecedented spatial and temporal resolution. In this article, we review the contribution of magnetoencephalography to deconvolve the bioelectrical changes associated with impaired consciousness during seizures. We use data collected from a patient with refractory absence seizures to discuss how spike-wave discharges are associated with perturbations in optimal connectivity within and between brain regions and discuss indirect evidence to suggest that this phenomenon might explain the cognitive deficits experienced during prolonged 3/s spike-wave discharges.

Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition

Neurons respond to sensory stimuli by altering the rate and temporal pattern of action potentials. These spike trains both encode and propagate information that guides behavior. Local inhibitory networks can affect the information encoded and propagated by neurons by altering correlations between different spike trains. Correlations introduce redundancy that can reduce encoding but also facilitate propagation of activity to downstream targets. Given this trade-off, how can networks maximize both encoding and propagation efficacy? Here, we examine this problem by measuring the effects of olfactory bulb inhibition on the pairwise statistics of mitral cell spiking. We evoked spiking activity in the olfactory bulb in vitro and measured how lateral inhibition shapes correlations across timescales. We show that inhibitory circuits simultaneously increase fast correlation (i.e., synchrony increases) and decrease slow correlation (i.e., firing rates become less similar). Further, we use computational models to show the benefits of fast correlation/slow decorrelation in the context of odor coding. Olfactory bulb inhibition enhances population-level discrimination of similar inputs, while improving propagation of mitral cell activity to cortex. Our findings represent a targeted strategy by which a network can optimize the correlation structure of its output in a dynamic, activity-dependent manner. This trade-off is not specific to the olfactory system, but rather our work highlights mechanisms by which neurons can simultaneously accomplish multiple, and sometimes competing, aspects of sensory processing.

Auditory information coding by modeled cochlear nucleus neurons

In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 μs). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.

Increased absolute magnitude of gamma synchrony in first-episode psychosis

OBJECTIVES

Recent studies have explored a model of the disconnection hypothesis of schizophrenia through the demonstration of abnormal stimulus induced gamma phase synchrony (GPS). These studies have principally examined synchrony in the 40 Hz band elicited in post-stimulus time periods, relative to a pre-stimulus baseline. In this study we examined the absolute magnitude of GPS elicited by a selective attention task, in first-episode psychosis (FEP). We hypothesized that FEP would be associated with abnormalities in absolute GPS, particularly when required to selectively attend to task-relevant stimuli.

METHODS

Fifty-five first-episode psychosis (FEP) subjects and one hundred and ten matched healthy control subjects underwent an auditory oddball selective attention task during EEG recording. The absolute magnitude of GPS was extracted for the range 35-45 Hz, and time-locked to stimulus onset. GPS averaged were computed for oddball 'target' (task-relevant) and 'non-target' (task-irrelevant) stimuli, for each subject.

RESULTS

FEP subjects showed a significant elevation in absolute GPS relative to controls, apparent across the 35-45 Hz range. This elevation was most marked in the left centro-temporal region, across the 800 ms post-stimulus period. In FEP subjects, the elevation in GPS was also greater for target compared to non-target stimuli, while healthy controls did not show a stimulus effect.

CONCLUSION

These findings complement previous evidence for reductions in peak gamma synchrony, calculated relative to a pre-stimulus baseline, in schizophrenia. The results an excess of absolute GPS in schizophrenia may contribute to an inability to effectively integrate task-relevant information, which underlie psychotic symptoms.

Fast activity as a surrogate marker of epileptic network function?

The detection of epileptiform discharges in electroencephalography recordings is a crucial part in diagnosing epilepsy. Thorough electrophysiologic evaluation yields information that allows for tailored surgical therapy in many cases, and thus improves treatment outcome. In recent years, fast activity (>60-80Hz) has been investigated for its diagnostic value in addition to well-known patterns such as epileptic transients. It was shown that these high frequency oscillations are highly specific for epileptic network function and might provide valuable information for localization of epileptic networks and understanding of their mechanisms. In this review, an overview of the electrophysiologic characteristics, putative cellular and network mechanisms in epilepsy is given. Recent studies are reviewed and interpreted in the context of a common hypothetical model.