Signal integration enhances the dynamic range in neuronal systems

The dynamic range measures the capacity of a system to discriminate the intensity of an external stimulus. Such an ability is fundamental for living beings to survive: to leverage resources and to avoid danger. Consequently, the larger is the dynamic range, the greater is the probability of survival. We investigate how the integration of different input signals affects the dynamic range, and in general the collective behavior of a network of excitable units. By means of numerical simulations and a mean-field approach, we explore the nonequilibrium phase transition in the presence of integration. We show that the firing rate in random and scale-free networks undergoes a discontinuous phase transition depending on both the integration time and the density of integrator units. Moreover, in the presence of external stimuli, we find that a system of excitable integrator units operating in a bistable regime largely enhances its dynamic range.Comment: 5 pages, 4 figure

Subthreshold dynamics of the neural membrane potential driven by stochastic synaptic input

In the cerebral cortex, neurons are subject to a continuous bombardment of synaptic inputs originating from the network's background activity. This leads to ongoing, mostly subthreshold membrane dynamics that depends on the statistics of the background activity and of the synapses made on a neuron. Subthreshold membrane polarization is, in turn, a potent modulator of neural responses. The present paper analyzes the subthreshold dynamics of the neural membrane potential driven by synaptic inputs of stationary statistics. Synaptic inputs are considered in linear interaction. The analysis identifies regimes of input statistics which give rise to stationary, fluctuating, oscillatory, and unstable dynamics. In particular, I show that (i) mere noise inputs can drive the membrane potential into sustained, quasiperiodic oscillations (noise-driven oscillations), in the absence of a stimulus-derived, intraneural, or network pacemaker; (ii) adding hyperpolarizing to depolarizing synaptic input can increase neural activity (hyperpolarization-induced activity), in the absence of hyperpolarization-activated currents

The Neuronal Transition Probability (NTP) Model for the Dynamic Progression of Non-REM Sleep EEG: The Role of the Suprachiasmatic Nucleus

Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP) – in fitting the data well – successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN) activity we show that the SCN has the information required to provide a threshold-triggered flip-flop for timing the towards-and-away alternations, information provided by sleep-relevant feedback to the SCN. NTP then determines the pattern of spectral power within each dynamic-state. NTP was fitted to individual NREM episodes 1–4, using data from 30 healthy subjects aged 20–30 years, and the quality of fit for each NREM measured. We show that the model fits well all NREM episodes and the best-fit probability-set is found to be effectively the same in fitting all subject data. The significant model-data agreement, the constant probability parameter and the proposed role of the SCN add considerable strength to the model. With it we link for the first time findings at cellular level and detailed time-course data at EEG level, to give a coherent picture of NREM dynamics over the entire night and over hierarchic brain levels all the way from the SCN to the EEG

General anesthesia, sleep and coma

In the United States, nearly 60,000 patients per day receive general anesthesia for surgery.1 General anesthesia is a drug-induced, reversible condition that includes specific behavioral and physiological traits — unconsciousness, amnesia, analgesia, and akinesia — with concomitant stability of the autonomic, cardiovascular, respiratory, and thermoregulatory systems.2 General anesthesia produces distinct patterns on the electroencephalogram (EEG), the most common of which is a progressive increase in low-frequency, high-amplitude activity as the level of general anesthesia deepens3,4 (Figure 1Figure 1Electroencephalographic (EEG) Patterns during the Awake State, General Anesthesia, and Sleep.). How anesthetic drugs induce and maintain the behavioral states of general anesthesia is an important question in medicine and neuroscience.6 Substantial insights can be gained by considering the relationship of general anesthesia to sleep and to coma. Humans spend approximately one third of their lives asleep. Sleep, a state of decreased arousal that is actively generated by nuclei in the hypothalamus, brain stem, and basal forebrain, is crucial for the maintenance of health.7,8 Normal human sleep cycles between two states — rapid-eye-movement (REM) sleep and non-REM sleep — at approximately 90-minute intervals. REM sleep is characterized by rapid eye movements, dreaming, irregularities of respiration and heart rate, penile and clitoral erection, and airway and skeletal-muscle hypotonia.7 In REM sleep, the EEG shows active high-frequency, low-amplitude rhythms (Figure 1). Non-REM sleep has three distinct EEG stages, with higher-amplitude, lower-frequency rhythms accompanied by waxing and waning muscle tone, decreased body temperature, and decreased heart rate. Coma is a state of profound unresponsiveness, usually the result of a severe brain injury.9 Comatose patients typically lie with eyes closed and cannot be roused to respond appropriately to vigorous stimulation. A comatose patient may grimace, move limbs, and have stereotypical withdrawal responses to painful stimuli yet make no localizing responses or discrete defensive movements. As the coma deepens, the patient's responsiveness even to painful stimuli may diminish or disappear. Although the patterns of EEG activity observed in comatose patients depend on the extent of the brain injury, they frequently resemble the high–amplitude, low-frequency activity seen in patients under general anesthesia10 (Figure 1). General anesthesia is, in fact, a reversible drug-induced coma. Nevertheless, anesthesiologists refer to it as “sleep” to avoid disquieting patients. Unfortunately, anesthesiologists also use the word “sleep” in technical descriptions to refer to unconsciousness induced by anesthetic drugs.11 (For a glossary of terms commonly used in the field of anesthesiology, see the Supplementary Appendix, available with the full text of this article at NEJM.org.) This review discusses the clinical and neurophysiological features of general anesthesia and their relationships to sleep and coma, focusing on the neural mechanisms of unconsciousness induced by selected intravenous anesthetic drugs.Massachusetts General Hospital. Dept. of Anesthesia and Critical Care, and Pain MedicineNational Institutes of Health (NIH) (Director’s Pioneer Award DP1OD003646)University of Michigan. Dept. of AnesthesiologyNational Institutes of Health (U.S.) (grant HL40881)National Institutes of Health (U.S.) (grant HL65272)James S. McDonnell FoundationNational Institutes of Health (U.S.) (grant HD51912

A role for fast rhythmic bursting neurons in cortical gamma oscillations in vitro

Basic cellular and network mechanisms underlying gamma frequency oscillations (30–80 Hz) have been well characterized in the hippocampus and associated structures. In these regions, gamma rhythms are seen as an emergent property of networks of principal cells and fast-spiking interneurons. In contrast, in the neocortex a number of elegant studies have shown that specific types of principal neuron exist that are capable of generating powerful gamma frequency outputs on the basis of their intrinsic conductances alone. These fast rhythmic bursting (FRB) neurons (sometimes referred to as "chattering" cells) are activated by sensory stimuli and generate multiple action potentials per gamma period. Here, we demonstrate that FRB neurons may function by providing a large-scale input to an axon plexus consisting of gap-junctionally connected axons from both FRB neurons and their anatomically similar counterparts regular spiking neurons. The resulting network gamma oscillation shares all of the properties of gamma oscillations generated in the hippocampus but with the additional critical dependence on multiple spiking in FRB cells

Neocortical hyperexcitability in a genetic model of absence seizures and its reduction by levetiracetam

PURPOSE: To study the effect of the antiepileptic drug levetiracetam (LEV) on the patterns of intrinsic optical signals (IOSs) generated by slices of the somatosensory cortex obtained from 3- and 6-month-old WAG/Rij and age-matched, nonepileptic control (NEC) rats. METHODS: WAG/Rij and NEC animals were anesthetized with enfluorane and decapitated. Brains were quickly removed, and neocortical slices were cut coronally with a vibratome, transferred to a submerged tissue chamber, and superfused with oxygenated artificial cerebrospinal fluid (aCSF). Slices were illuminated with a dark-field condensor and examined with a x2.5 objective; images were processed with a real time digital video image-enhancement system. Images were acquired before (background) and during electrical stimulation with a temporal resolution of 10 images/s and were displayed in pseudocolors. Extracellular stimuli (200 micros; <4 V) were delivered through bipolar stainless steel electrodes placed in the white matter. RESULTS: IOSs recorded in NEC slices bathed in control aCSF became less intense and of reduced size with age (p < 0.05); this trend was not seen in WAG/Rij slices. Age-dependent decreases in IOS intensity and area size were also seen in NEC slices superfused with aCSF containing the convulsant 4-aminopyridine (4-AP, 5 microM); in contrast, significant increases in both parameters occurred with age in 4-AP-treated WAG/Rij slices (p < 0.05). Under any of these conditions, the IOS intensity and area size slices were larger in WAG/Rij than in NEC slices. LEV (50-500 microM) application to WAG/Rij slices caused dose-dependent IOS reductions that were evident both in control and in 4-AP-containing aCSF and were more pronounced in 6-month-old tissue. CONCLUSIONS: These data demonstrate age-dependent IOS modifications in NEC and WAG/Rij rat slices and identify a clear pattern of hyperexcitability that occurs in 6-month-old WAG/Rij neocortical tissue, an age when absence seizures occur in all animals. The ability of LEV to reduce these patterns of network hyperexcitability supports the potential use of this new antiepileptic drug in primary generalized epileptic disorders

Analysis of the Temporal Organization of Sleep Spindles in the Human Sleep EEG Using a Phenomenological Modeling Approach

The sleep electroencephalogram (EEG) is characterized by typical oscillatory patterns such as sleep spindles and slow waves. Recently, we proposed a method to detect and analyze these patterns using linear autoregressive models for short (≈ 1 s) data segments. We analyzed the temporal organization of sleep spindles and discuss to what extent the observed interevent intervals correspond to properties of stationary stochastic processes and whether additional slow processes, such as slow oscillations, have to be assumed. We have found evidence for such an additional slow process, most pronounced in sleep stage 2

Synaptic Transmission and Plasticity in an Active Cortical Network

BACKGROUND: The cerebral cortex is permanently active during both awake and sleep states. This ongoing cortical activity has an impact on synaptic transmission and short-term plasticity. An activity pattern generated by the cortical network is a slow rhythmic activity that alternates up (active) and down (silent) states, a pattern occurring during slow wave sleep, anesthesia and even in vitro. Here we have studied 1) how network activity affects short term synaptic plasticity and, 2) how synaptic transmission varies in up versus down states. METHODOLOGY/PRINCIPAL FINDINGS: Intracellular recordings obtained from cortex in vitro and in vivo were used to record synaptic potentials, while presynaptic activation was achieved either with electrical or natural stimulation. Repetitive activation of layer 4 to layer 2/3 synaptic connections from ferret visual cortex slices displayed synaptic augmentation that was larger and longer lasting in active than in silent slices. Paired-pulse facilitation was also significantly larger in an active network and it persisted for longer intervals (up to 200 ms) than in silent slices. Intracortical synaptic potentials occurring during up states in vitro increased their amplitude while paired-pulse facilitation disappeared. Both intracortical and thalamocortical synaptic potentials were also significantly larger in up than in down states in the cat visual cortex in vivo. These enhanced synaptic potentials did not further facilitate when pairs of stimuli were given, thus paired-pulse facilitation during up states in vivo was virtually absent. Visually induced synaptic responses displayed larger amplitudes when occurring during up versus down states. This was further tested in rat barrel cortex, where a sensory activated synaptic potential was also larger in up states. CONCLUSIONS/SIGNIFICANCE: These results imply that synaptic transmission in an active cortical network is more secure and efficient due to larger amplitude of synaptic potentials and lesser short term plasticity

Quantification of depth of anesthesia by nonlinear time series analysis of brain electrical activity

We investigate several quantifiers of the electroencephalogram (EEG) signal with respect to their ability to indicate depth of anesthesia. For 17 patients anesthetized with Sevoflurane, three established measures (two spectral and one based on the bispectrum), as well as a phase space based nonlinear correlation index were computed from consecutive EEG epochs. In absence of an independent way to determine anesthesia depth, the standard was derived from measured blood plasma concentrations of the anesthetic via a pharmacokinetic/pharmacodynamic model for the estimated effective brain concentration of Sevoflurane. In most patients, the highest correlation is observed for the nonlinear correlation index D*. In contrast to spectral measures, D* is found to decrease monotonically with increasing (estimated) depth of anesthesia, even when a "burst-suppression" pattern occurs in the EEG. The findings show the potential for applications of concepts derived from the theory of nonlinear dynamics, even if little can be assumed about the process under investigation.Comment: 7 pages, 5 figure