A critical assessment of the information processing capabilities of neuronal microtubules using coherent excitations

Evidence for signaling, communication, and conductivity in microtubules (MTs) has been shown through both direct and indirect means, and theoretical models predict their potential use in both classical and quantum information processing in neurons. The notion of quantum information processing within neurons has been implicated in the phenomena of consciousness, although controversies have arisen in regards to adverse physiological temperature effects on these capabilities. To investigate the possibility of quantum processes in relation to information processing in MTs, a biophysical MT model is used based on the electrostatic interior of the tubulin protein. The interior is taken to constitute a double-well potential structure within which a mobile electron is considered capable of occupying at least two distinct quantum states. These excitonic states together with MT lattice vibrations determine the state space of individual tubulin dimers within the MT lattice. Tubulin dimers are taken as quantum well structures containing an electron that can exist in either its ground state or first excited state. Following previous models involving the mechanisms of exciton energy propagation, we estimate the strength of exciton and phonon interactions and their effect on the formation and dynamics of coherent exciton domains within MTs. Also, estimates of energy and timescales for excitons, phonons, their interactions, and thermal effects are presented. Our conclusions cast doubt on the possibility of sufficiently long-lived coherent exciton/phonon structures existing at physiological temperatures in the absence of thermal isolation mechanisms. These results are discussed in comparison with previous models based on quantum effects in non-polar hydrophobic regions, which have yet to be disproved.     

A critical comparison of different residence time measures in aneurysms

Flow stagnation and residence time (RT) are important features of diseased arterial flows that influence biochemical transport processes and thrombosis. RT calculation methods are classified into Eulerian and Lagrangian approaches where several measures have been proposed to quantify RT. Each of these methods has a different definition of RT, and it is not clear how they are related. In this study, image-based computational models of blood flow in an abdominal aortic aneurysm and a cerebral aneurysm were considered and RT was calculated using different methods. In the Lagrangian methods, discrete particle tracking of massless tracers was used to calculate particle residence time and mean exposure time. In the Eulerian methods, continuum transport models were used to quantify RT using Eulerian RT and virtual ink approaches. Point-wise RT and Eulerian indicator RT were also computed based on measures derived from velocity. A comparison of these methods is presented and the implications of each method are discussed. Our results highlight that most RT methods have a conceptually distinct definition of RT and therefore should be utilized depending on the specific application of interest.     

A homeostatic model of neuronal firing governed by feedback signals from the extracellular matrix

Molecules of the extracellular matrix (ECM) can modulate the efficacy of synaptic transmission and neuronal excitability. These mechanisms are crucial for the homeostatic regulation of neuronal firing over extended timescales. In this study, we introduce a simple mathematical model of neuronal spiking balanced by the influence of the ECM. We consider a neuron receiving random synaptic input in the form of Poisson spike trains and the ECM, which is modeled by a phenomenological variable involved in two feedback mechanisms. One feedback mechanism scales the values of the input synaptic conductance to compensate for changes in firing rate. The second feedback accounts for slow fluctuations of the excitation threshold and depends on the ECM concentration. We show that the ECM-mediated feedback acts as a robust mechanism to provide a homeostatic adjustment of the average firing rate. Interestingly, the activation of feedback mechanisms may lead to a bistability in which two different stable levels of average firing rates can coexist in a spiking network. We discuss the mechanisms of the bistability and how they may be related to memory function.     

A note on neuronal firing and input variability.

The Ornstein-Uhlenbeck process with a constant forcing function has often been used as a model for the subthreshold membrane potential of a neuron. The mean, variance and coefficient of variation of the first passage time to a constant threshold are examined for this model in the limit of small synaptic noise and low thresholds. A comparison is made between the asymptotic results of Wan & Tuckwell, who used perturbation analysis, and several computationally simpler approximation methods. A generalization of Stein's method gives an overestimate of the mean interval while an approximation by a Wiener process with linear drift gives an underestimate of the mean interval. These bounds are simple to calculate and can be used as a prelude to a more detailed perturbation analysis.     

A critical assessment of the h-index

Editor's suggested further reading in BioEssays: Can we do better than existing author citation metrics? Abstract and Counting citations in texts rather than reference lists to improve the accuracy of assessing scientific contribution Abstract     

Effects of afterhyperpolarization on neuronal firing.

Stein's model for a neuron is studied. This model is modified to take into account the effects of afterhyperpolarization on the neuronal firing. The relative refractory phase, following the absolute one, is modelled by a time-increasing amplitude of postsynaptic potentials and it is also incorporated into the model. Besides the simulation of the model, some theoretical results and approximation methods are derived. Afterhyperpolarization tends to preserve the linearity of the frequency transfer characteristic and it has a limited effect on the moments of the interspike intervals in general. The main effects are seen at high firing rates and in the removal of short intervals in the interspike interval histogram.     

Unbiased measures of transmitted information and channel capacity from multivariate neuronal data

Two measures from information theory, transmitted information and channel capacity, can quantify the ability of neurons to convey stimulus-dependent information. These measures are calculated using probability functions estimated from stimulus-response data. However, these estimates are biased by response quantization, noise, and small sample sizes. Improved estimators are developed in this paper that depend on both an estimate of the sample-size bias and the noise in the data.This work was supported in part by Air Force Office of Scientific Research Grant AFOSR-ISSA-88-0073     

Dependence of different spike sequences on the mean neuronal firing rate in the neocortex in vivo and in vitro

Neuronal spikes were recorded extracellularly in rabbit visual cortex in vivo (88 cells) and in surviving slices of guinea pig sensorimotor cortex in vitro (50 cells). Spike sequences (SS) with monotonically increasing (SS+) and decreasing (SS-) interspike intervals were detected. Relative number of spikes of SS in the recording was closely associated with SS generation. The relative number of spikes was plotted against the average firing rate, this function had a biphasic character with the critical point around 7 Hz. The rate of change in interspike duration (the slope) was virtually independent of the firing rate, but was significantly different in vivo and in vitro conditions for both SS+ (325 and 180 ms/s, respectively) and SS- (270 and 160 ms/s, respectively). By and large, in vivo and in vitro the spike sequence parameters depended in the average firing rate in the same manner. The role of the spike sequences in rhythmic and information processes in neocortex is discussed.     

Non-monotonic effects of GABAergic synaptic inputs on neuronal firing

GABA is generally known as the principal inhibitory neurotransmitter in the nervous system, usually acting by hyperpolarizing membrane potential. However, GABAergic currents sometimes exhibit non-inhibitory effects, depending on the brain region, developmental stage or pathological condition. Here, we investigate the diverse effects of GABA on the firing rate of several single neuron models, using both analytical calculations and numerical simulations. We find that GABAergic synaptic conductance and output firing rate exhibit three qualitatively different regimes as a function of GABA reversal potential, E_GABA: monotonically decreasing for sufficiently low E_GABA (inhibitory), monotonically increasing for E_GABA above firing threshold (excitatory); and a non-monotonic region for intermediate values of E_GABA. In the non-monotonic regime, small GABA conductances have an excitatory effect while large GABA conductances show an inhibitory effect. We provide a phase diagram of different GABAergic effects as a function of GABA reversal potential and glutamate conductance. We find that noisy inputs increase the range of E_GABA for which the non-monotonic effect can be observed. We also construct a micro-circuit model of striatum to explain observed effects of GABAergic fast spiking interneurons on spiny projection neurons, including non-monotonicity, as well as the heterogeneity of the effects. Our work provides a mechanistic explanation of paradoxical effects of GABAergic synaptic inputs, with implications for understanding the effects of GABA in neural computation and development.     

Contributions of T-type calcium channel isoforms to neuronal firing

Low voltage-activated (LVA) T-type calcium channels play critical roles in the excitability of many cell types and are a focus of research aimed both at understanding the physiological basis of calcium channel-dependent signaling and the underlying pathophysiology associated with hyperexcitability disorders such as epilepsy. These channels play a critical role towards neuronal firing in both conducting calcium ions during action potentials and also in switching neurons between distinct modes of firing. In this review the properties of the CaV3.1, CaV3.2 and CaV3.3 T-type channel isoforms is discussed in relation to their individual contributions to action potentials during burst and tonic firing states as well their roles in switching between firing states.     

Integration of light-controlled neuronal firing and fast circuit imaging

For understanding normal and pathological circuit function, capitalizing on the full potential of recent advances in fast optical neural circuit control will depend crucially on fast, intact-circuit readout technology. First, millisecond-scale optical control will be best leveraged with simultaneous millisecond-scale optical imaging. Second, both fast circuit control and imaging should be adaptable to intact-circuit preparations from normal and diseased subjects. Here we illustrate integration of fast optical circuit control and fast circuit imaging, review recent work demonstrating utility of applying fast imaging to quantifying activity flow in disease models, and discuss integration of diverse optogenetic and chemical genetic tools that have been developed to precisely control the activity of genetically specified neural populations. Together these neuroengineering advances raise the exciting prospect of determining the role-specific cell types play in modulating neural activity flow in neuropsychiatric disease.     

Dephosphorylation of the pre-initiation complex is critical for origin firing

1. Download : Download high-res image (110KB)
2. Download : Download full-size image

     

Effect of inhibitory feedback on correlated firing of spiking neural network

Understanding the properties and mechanisms that generate different forms of correlation is critical for determining their role in cortical processing. Researches on retina, visual cortex, sensory cortex, and computational model have suggested that fast correlation with high temporal precision appears consistent with common input, and correlation on a slow time scale likely involves feedback. Based on feedback spiking neural network model, we investigate the role of inhibitory feedback in shaping correlations on a time scale of 100 ms. Notably, the relationship between the correlation coefficient and inhibitory feedback strength is non-monotonic. Further, computational simulations show how firing rate and oscillatory activity form the basis of the mechanisms underlying this relationship. When the mean firing rate holds unvaried, the correlation coefficient increases monotonically with inhibitory feedback, but the correlation coefficient keeps decreasing when the network has no oscillatory activity. Our findings reveal that two opposing effects of the inhibitory feedback on the firing activity of the network contribute to the non-monotonic relationship between the correlation coefficient and the strength of the inhibitory feedback. The inhibitory feedback affects the correlated firing activity by modulating the intensity and regularity of the spike trains. Finally, the non-monotonic relationship is replicated with varying transmission delay and different spatial network structure, demonstrating the universality of the results.     

Catalytic antibodies: A critical assessment

In the past few years, antibodies that catalyze a variety of reactions with enzyme-like properties have been produced. The present review is of a critical nature, rather than a survey or an introduction to the field of catalytic antibodies. Here, we examine the performance of catalytic antibodies in light of the features that define an enzyme: substrate specificity, rate enhancement, and turnover. We also refer to some limitations of the technologies currently used for their generation. In the future, antibodies may provide a new repertoire of tailor-made, enzyme-like, catalysts with possible applications in biology, medicine, and biotechnology. In the following sections, we emphasize that these applications will require far more efficient catalysts than are presently available, and we point to several trends for future research that may offer more efficient catalytic antibodies.     

Negatively correlated firing: the functional meaning of lateral inhibition within cortical columns

Lateral inhibition is a well documented aspect of neural architecture in the main sensory systems. Existing accounts of lateral inhibition focus on its role in sharpening distinctions between inputs that are closely related. However, these accounts fail to explain the functional role of inhibition in cortical columns, such as those in V1, where neurons have similar response properties. In this paper, we outline a model of position tracking using cortical columns of integrate-and-fire and Hodgkin-Huxley-type neurons which respond optimally to a particular location, to show that negatively correlated firing patterns arise from lateral inhibition in cortical columns and that this provides a clear benefit for population coding in terms of stability, accuracy, estimation time and neural resources.