Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks.

Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. The introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state but can be reactivated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.

Prospective documentation and analysis of the pre- and early clinical management in severe head injury in southern Bavaria at a population based level.

Treatment of patients suffering from severe head injury is so far restricted to general procedures, whereas specific pharmacological agents of neuroprotection including hypothermia have not been found to improve the outcome in clinical trials. Albeit effective, symptomatic measures of the preclinical rescue of patients (i.e. stabilization or reestablishment of the circulatory and respiratory system) or of the early clinical care (e.g. prompt diagnosis and treatment of an intracranial space occupying mass, maintenance of a competent circulatory and respiratory system, and others) by and large constitute the current treatment based on considerable organizational and logistical efforts. These and other components of the head injury treatment are certainly worthwhile of a systematic analysis as to their efficacy or remaining deficiencies, respectively. Deficits could be associated with delays of providing preclinical rescue procedures (e.g. until intubation of the patient or administration of fluid). Delays could also be associated in the hospital with the diagnostic establishment of intracranial lesions requiring prompt neurosurgical intervention. By support of the Federal Ministry of Education and Research and under the auspices of the Forschungsverbund Neurotraumatology, University of Munich, a prospective system analysis was carried out on major aspects of the pre- and early clinical management at a population based level in patients with traumatic brain injury. Documentation of pertinent data was made from August 1998 to July 1999 covering a catchment area of Southern Bavaria (5.6 mio inhabitants). Altogether 528 cases identified to suffer from severe head injury (GCS < or = 8 or deteriorating to that level within 48 hrs) were enrolled following admission to the hospital and establishment of the diagnosis. Further, patients dying on the scene or during transport to the hospital were also documented, particularly as to the frequency of severe head injury as underlying cause of mortality. The analysis included also cases with additional peripheral trauma (polytrauma). The efficacy of the logistics and organization of the management was studied by documentation of prognosis-relevant time intervals, as for example until arrival of the rescue squad at the scene of an accident, until intubation and administration of fluid, or upon hospital admission until establishment of the CT-diagnosis and commencement of surgery or transfer to the intensive care unit, respectively. The severity of cases studied in the present analysis is evident from a mortality of far above 40% of cases admitted to the hospital, which was increased by about 20% when including prehospital mortality. The outcome data notwithstanding, the emerging results demonstrate a high efficacy of the pre- and early clinical management, as indicated by a prompt arrival of the rescue squad at the scene, a competent prehospital and early clinical management and care, indicative of a low rate of avoidable complications. It is tentatively concluded on the basis of these findings that the patient prognosis is increasingly determined by the manifestations of primary brain damage vs. the development of secondary complications.

Neuronavigation combined with electrophysiological monitoring for surgery of lesions in eloquent brain areas in 42 cases: a retrospective comparison of the neurological outcome and the quality of resection with a control group with similar lesions.

The purpose of this study was to achieve a more radical resection of tumors in the area of the motor cortex via minimal craniotomy using a combination of neuronavigation and neurophysiological monitoring with direct electrical cortical stimulation and to compare retrospectively the clinical outcome and postoperative magnetic resonance imaging with a control group that was operated on in our service when the combination of these monitoring techniques was not available. A total of 42 patients with tumors in or near the central region underwent surgery with neuronavigation guidance and neurophysiological monitoring. The primary motor cortex was identified intraoperatively by the somatosensory evoked phase reversal method and direct cortical stimulation. The functional areas were transferred into the neuronavigation system. By stimulating the identified primary motor cortex and displaying the motor area in the operating microscope a permanent control of the motor function was possible during the whole operation. Using these techniques a more radical tumor resection - evaluated by postoperative MRI - was achieved in the study group (p = 0.04) and also a trend toward a better neurological outcome.

Surgery and outcome for aneurysmal subarachnoid hemorrhage in elderly patients.

OBJECTIVE: The goal was to report treatment results of elderly patients (over 70 years) who underwent clipping of aneurysms after subarachnoid hemorrhage (SAH). MATERIAL AND METHODS: From 1994 to 2000 41/284 (14%) patients older than 70 years were operated on aneurysmal SAH in our department. Localization of ruptured aneurysm was anterior communicating artery (n = 14), middle cerebral artery (n = 14), internal carotid artery (n = 6), anterior cerebral artery (n = 2), pericallosal artery (n = 1) and multiple in 4 patients. We used the Hunt and Hess classification for initial grading and the Glasgow Outcome Score at day 30 after surgery. RESULTS: Patients with HH 1-3 had a low mortality (1/18, 6%), whereas 9 of 23 patients (39%) with HH 4-5 decreased within 30 days after surgery. Overall mortality was 24.5% (10/41) at 30 days after surgery. Most patients (n = 32) underwent early surgery (within 72 hours). Shunt dependent hydrocephalus developed in 15 patients (37%). The outcome was better in patients graded HH 1-3, in those without serious atherosclerotic changes in angiography, and in AcoA and ICA localization compared to MCA. CONCLUSION: Advanced age does not preclude successful surgery for ruptured aneurysm. Most important factor for outcome was a good initial clinical status, though the majority of our patients presented with poor grades. Early surgical clipping and postoperative intensive care can attain a favorable outcome in a significant percentage of elderly patients.

Noise and the PSTH response to current transients: II. Integrate-and-fire model with slow recovery and application to motoneuron data.

A generalized version of the integrate-and-fire model is presented that qualitatively reproduces firing rates and membrane trajectories of motoneurons. The description is based on the spike-response model and includes three different time constants: the passive membrane time constant, a recovery time of the input conductance after each spike, and a time constant of the spike afterpotential. The effect of stochastic background input on the peristimulus time histogram (PSTH) response to spike input is calculated analytically. Model results are compared with the experimental data of Poliakov et al. (1996). The linearized theory shows that the PSTH response to an input spike is proportional to a filtered version of the postsynaptic potential generated by the input spike. The shape of the filter depends on the background activity. The full nonlinear theory is in close agreement with simulated PSTH data.

Intrinsic stabilization of output rates by spike-based Hebbian learning.

We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time differences between presynaptic and postsynaptic spikes. The changes due to correlated input and output spikes are quantified by means of a learning window. We show that plasticity can lead to an intrinsic stabilization of the mean firing rate of the postsynaptic neuron. Subtractive normalization of the synaptic weights (summed over all presynaptic inputs converging on a postsynaptic neuron) follows if, in addition, the mean input rates and the mean input correlations are identical at all synapses. If the integral over the learning window is positive, firing-rate stabilization requires a non-Hebbian component, whereas such a component is not needed if the integral of the learning window is negative. A negative integral corresponds to anti-Hebbian learning in a model with slowly varying firing rates. For spike-based learning, a strict distinction between Hebbian and anti-Hebbian rules is questionable since learning is driven by correlations on the timescale of the learning window. The correlations between presynaptic and postsynaptic firing are evaluated for a piecewise-linear Poisson model and for a noisy spiking neuron model with refractoriness. While a negative integral over the learning window leads to intrinsic rate stabilization, the positive part of the learning window picks up spatial and temporal correlations in the input.

Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron.

An analytical model is proposed that can predict the shape of the poststimulus time histogram (PSTH) response to a current pulse of a neuron subjected to uncorrelated background input. The model is based on an explicit description of noise in the form of an escape rate and corresponding hazard function. Two forms of the model are presented. The full model is nonlinear and can be integrated numerically, while the linearized version can be solved analytically. In the linearized version, the PSTH response to a current input is proportional to a filtered version of the input pulse. The bandwidth of the filter is determined by the amount of noise. In the limit of high noise, the response is similar to the time course of the potential induced by the input pulse, while for low noise it is proportional to its derivative. For low noise, a second peak occurs after one mean interval. The full nonlinear model predicts an asymmetry between excitatory and inhibitory current inputs. We compare our results with simulations of the integrate-and-fire model with stochastic background input. We predict that changes in PSTH shape due to noise should be observable in many types of neurons in both subthreshold and suprathreshold regimes.

Coding properties of spiking neurons: reverse and cross-correlations.

What is the 'meaning' of a single spike? Spike-triggered averaging ('reverse correlations') yields the typical input just before a spike. Similarly, cross-correlations describe the probability of firing an output spike given (one additional) presynaptic input spike. In this paper, we analytically calculate reverse and cross-correlations for a spiking neuron model with escape noise. The influence of neuronal parameters (such as the membrane time constant, the noise level, and the mean firing rate) on the form of the correlation function is illustrated. The calculation is done in the framework of a population theory that is reviewed. The relation of the population activity equations to population density methods is discussed. Finally, we indicate the role of cross-correlations in spike-time dependent Hebbian plasticity.

Effect of lateral connections on the accuracy of the population code for a network of spiking neurons.

We study how neuronal connections in a population of spiking neurons affect the accuracy of stimulus estimation. Neurons in our model code for a one-dimensional orientation variable phi. Connectivity between two neurons depends on the absolute difference absolute value(phi - phi') between the preferred orientation of the two neurons. We derive an analytical expression of the activity profile for a population of neurons described by the spike response model with noisy threshold. We estimate the stimulus orientation and the trial-to-trial fluctuations using the population vector method. For stationary stimuli, uniform inhibitory connections produce a more reliable estimation of the stimulus than short-range excitatory connections with long-range inhibitions, although the latter interaction type produces a sharper tuning curve. These results are consistent with previous analytical studies of the Fisher information.

Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity.

A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On the level of the hippocampus, allothetic and idiothetic representations are integrated to yield a stable representation of the environment by a population of localized overlapping CA3-CA1 place fields. The hippocampal spatial representation is used as a basis for goal-oriented spatial behavior. We focus on the neural pathway connecting the hippocampus to the nucleus accumbens. Place cells drive a population of locomotor action neurons in the nucleus accumbens. Reward-based learning is applied to map place cell activity into action cell activity. The ensemble action cell activity provides navigational maps to support spatial behavior. We present experimental results obtained with a mobile Khepera robot.

Evaluation of a method for noninvasive intracranial pressure assessment during infusion studies in patients with hydrocephalus.

OBJECT: A mathematical model previously introduced by the authors allowed noninvasive intracranial pressure (nICP) assessment. In the present study the authors investigated this model as an aid in predicting the time course of raised ICP during infusion tests in patients with hydrocephalus and its suitability for estimating the resistance to outflow of cerebrospinal fluid (Rcsf). METHODS: Twenty-one patients with hydrocephalus were studied. The nICP was calculated from the arterial blood pressure (ABP) waveform by using a linear signal transformation, which was dynamically modified by the relationship between ABP and cerebral blood flow velocity. This model was verified by comparison of nICP with "real" ICP measured during lumbar infusion tests. In all simulations, parallel increases in real ICP and nICP were evident. The simulated Rcsf was computed using nICP and then compared with Rcsf computed from real ICP. The mean absolute error between real and simulated Rcsf was 4.1 +/- 2.2 mm Hg minute/ml. By the construction of simulations specific to different subtypes of hydrocephalus arising from various causes, the mean error decreased to 2.7 +/- 1.7 mm Hg minute/ml, whereas the correlation between real and simulated Rcsf increased from R = 0.73 to R = 0.89 (p < 0.001). CONCLUSIONS: The validity of the mathematical model was confirmed in this study. The creation of type-specific simulations resulted in substantial improvements in the accuracy of ICP assessment. Improvement strategies could be important because of a potential clinical benefit from this method.

Population dynamics of spiking neurons: fast transients, asynchronous states, and locking.

An integral equation describing the time evolution of the population activity in a homogeneous pool of spiking neurons of the integrate-and-fire type is discussed. It is analytically shown that transients from a state of incoherent firing can be immediate. The stability of incoherent firing is analyzed in terms of the noise level and transmission delay, and a bifurcation diagram is derived. The response of a population of noisy integrate-and-fire neurons to an input current of small amplitude is calculated and characterized by a linear filter L. The stability of perfectly synchronized"locked"solutions is analyzed.

Noise in integrate-and-fire neurons: from stochastic input to escape rates.

We analyze the effect of noise in integrate-and-fire neurons driven by time-dependent input and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for time-dependent subthreshold input, diffusive noise can be replaced by escape noise with a hazard function that has a gaussian dependence on the distance between the (noise-free) membrane voltage and threshold. The approximation is improved if we add to the hazard function a probability current proportional to the derivative of the voltage. Stochastic resonance in response to periodic input occurs in both noise models and exhibits similar characteristics.

Noise shaping in populations of coupled model neurons.

Biological information-processing systems, such as populations of sensory and motor neurons, may use correlations between the firings of individual elements to obtain lower noise levels and a systemwide performance improvement in the dynamic range or the signal-to-noise ratio. Here, we implement such correlations in networks of coupled integrate-and-fire neurons using inhibitory coupling and demonstrate that this can improve the system dynamic range and the signal-to-noise ratio in a population rate code. The improvement can surpass that expected for simple averaging of uncorrelated elements. A theory that predicts the resulting power spectrum is developed in terms of a stochastic point-process model in which the instantaneous population firing rate is modulated by the coupling between elements.

Cerebral vasodilatation causing acute intracranial hypertension: a method for noninvasive assessment.

Deep spontaneous vasodilatatory events are frequently recorded in various cerebral diseases, causing dramatic increases (A-waves) in intracranial pressure (ICP) and subsequently provoking ischemic brain insults. The relationship between fluctuations in CBF, ICP, and arterial blood pressure (ABP) is influenced by properties of cerebrovascular control mechanisms and the cerebrospinal pressure-volume compensation. The goal of this study was to construct a mathematical model of this relationship and to assess its ability to predict the occurrence and time course of A-waves. A group of 17 severely head-injured patients were included in the study. In our model ICP was derived from the ABP waveform using a linear signal transformation. The transformation was modified during the simulation by a relationship between ABP and flow velocity, i.e., by the characterization of the cerebrovascular bed. In this way the ICP could be calculated from the ABP waveform. This model was verified by comparison of simulated and directly measured ICP during A-waves recorded in seven of the patients. In all simulations, plateau elevations of ICP were well replicated. The mean absolute error between real and simulated ICP was 8.3 +/- 5.4 mm Hg at the baseline and 7.9 +/- 4.3 mm Hg at the top of plateau waves. The correlation coefficient between real and simulated increase in ICP was R = 0.98; P < .001. Similarly, correlation between real and simulated increase in pulse amplitude of ICP was highly significant (R = 0.94; P < .001). The mathematical model of the relationship between ABP, flow velocity, and ICP is of potential clinical use for the noninvasive detection of A-waves in patients in whom invasive ICP assessment is not conducted.

Noise spectrum and signal transmission through a population of spiking neurons.

How reliably can a population of spiking neurons transmit a continuous-time signal? We study the noise spectrum of a fully connected population of spiking neurons with relative and absolute refractoriness. Spikes are generated stochastically with a rate that depends on the postsynaptic potential. The analytical solution of the noise spectrum of the population activity is compared with simulations. We find that strong inhibitory couplings can considerably reduce the noise level in a certain frequency band. This allows the population to reliably transmit signals at frequencies close to or even above the single-neuron firing rate.

Extracting oscillations. Neuronal coincidence detection with noisy periodic spike input.

How does a neuron vary its mean output firing rate if the input changes from random to oscillatory coherent but noisy activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence-detection properties of an integrate-and-fire neuron. We derive an expression indicating how coincidence detection depends on neuronal parameters. Specifically, we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thus can convert a temporal code into a rate code.

How the threshold of a neuron determines its capacity for coincidence detection.

Coherent oscillatory activity of a population of neurons is thought to be a vital feature of temporal coding in the brain. We focus on the question of whether a single neuron can transform a spike code into a rate code. More precisely, how does a neuron vary its mean output firing rate, if its input changes from random to coherent? We investigate the coincidence detection properties of an integrate-and-fire neuron in dependence upon internal parameters and input statistics. In particular, we show how coincidence detection depends on the membrane time constant and the threshold. Furthermore, we demonstrate that there is an optimal threshold for coincidence detection and that there is a broad range of near-optimal threshold values. Fine-tuning is not necessary.

Analysis of a correlation-based model for the development of orientation-selective receptive fields in the visual cortex.

We analyse a model for the development of orientation-selective receptive fields of simple cells in a locally connected network of cortical neurons. The Hebbian learning rule that underlies the development is described by a linear differential equation. The structure of the emerging cortical map can be predicted by deriving the eigenfunctions corresponding to the leading eigenvalues of the associated matrix. We show that the receptive fields have the typical form of a wavelet. Mathematically, receptive fields are given by a Hermitian polynomial with Gaussian cut-off and a phase factor. Both the phase of the wavelet and the orientation are changing periodically along the surface of the cortical map as suggested by previous simulation studies and as also found in experiments. In order to get orientation-selective receptive fields, the spatial correlation function of the inputs that drive the development must have a zero crossing.