First human hNT neurons patterned on parylene-C/silicon dioxide substrates: Combining an accessible cell line and robust patterning technology for the study of the pathological adult human brain

In this communication, we describe a new method which has enabled the first patterning of human neurons (derived from the human teratocarcinoma cell line (hNT)) on parylene-C/silicon dioxide substrates. We reveal the details of the nanofabrication processes, cell differentiation and culturing protocols necessary to successfully pattern hNT neurons which are each key aspects of this new method. The benefits in patterning human neurons on silicon chip using an accessible cell line and robust patterning technology are of widespread value. Thus, using a combined technology such as this will facilitate the detailed study of the pathological human brain at both the single cell and network level. © 2010 Elsevier B.V.

Isolating single primary rat hippocampal neurons and astrocytes on ultra-thin patterned parylene-C/silicon dioxide substrates

We report here the patterning of primary rat neurons and astrocytes from the postnatal hippocampus on ultra-thin parylene-C deposited on a silicon dioxide substrate, following observations of neuronal, astrocytic and nuclear coverage on strips of different lengths, widths and thicknesses. Neuronal and glial growth was characterized 'on', 'adjacent to' and 'away from' the parylene strips. In addition, the article reports how the same material combination can be used to isolate single cells along thin tracks of parylene-C. This is demonstrated with a series of high magnification images of the experimental observations for varying parylene strip widths and thicknesses. Thus, the findings demonstrate the possibility to culture cells on ultra-thin layers of parylene-C and localize single cells on thin strips. Such work is of interest and significance to the Neuroengineering and Multi-Electrode Array (MEA) communities, as it provides an alternative insulating material in the fabrication of embedded micro-electrodes, which can be used to facilitate single cell stimulation and recording in capacitive coupling mode. © 2010 Elsevier Ltd.

Guided growth of neurons and glia using microfabricated patterns of parylene-C on a SiO2 background.

This paper describes a simple technique for the patterning of glia and neurons. The integration of neuronal patterning to Multi-Electrode Arrays (MEAs), planar patch clamp and silicon based 'lab on a chip' technologies necessitates the development of a microfabrication-compatible method, which will be reliable and easy to implement. In this study a highly consistent, straightforward and cost effective cell patterning scheme has been developed. It is based on two common ingredients: the polymer parylene-C and horse serum. Parylene-C is deposited and photo-lithographically patterned on silicon oxide (SiO(2)) surfaces. Subsequently, the patterns are activated via immersion in horse serum. Compared to non-activated controls, cells on the treated samples exhibited a significantly higher conformity to underlying parylene stripes. The immersion time of the patterns was reduced from 24 to 3h without compromising the technique. X-ray photoelectron spectroscopy (XPS) analysis of parylene and SiO(2) surfaces before and after immersion in horse serum and gel based eluant analysis suggests that the quantity and conformation of proteins on the parylene and SiO(2) substrates might be responsible for inducing glial and neuronal patterning.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo.

Action Potential (APs) patterns of sensory cortex neurons encode a variety of stimulus features, but how can a neuron change the feature to which it responds? Here, we show that in vivo a spike-timing-dependent plasticity (STDP) protocol-consisting of pairing a postsynaptic AP with visually driven presynaptic inputs-modifies a neurons' AP-response in a bidirectional way that depends on the relative AP-timing during pairing. Whereas postsynaptic APs repeatedly following presynaptic activation can convert subthreshold into suprathreshold responses, APs repeatedly preceding presynaptic activation reduce AP responses to visual stimulation. These changes were paralleled by restructuring of the neurons response to surround stimulus locations and membrane-potential time-course. Computational simulations could reproduce the observed subthreshold voltage changes only when presynaptic temporal jitter was included. Together this shows that STDP rules can modify output patterns of sensory neurons and the timing of single-APs plays a crucial role in sensory coding and plasticity.DOI:http://dx.doi.org/10.7554/eLife.00012.001.

Code-specific policy gradient rules for spiking neurons

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.

How modeling can reconcile apparently discrepant experimental results: The case of pacemaking in dopaminergic neurons

Midbrain dopaminergic neurons are endowed with endogenous slow pacemaking properties. In recent years, many different groups have studied the basis for this phenomenon, often with conflicting conclusions. In particular, the role of a slowly-inactivating L-type calcium channel in the depolarizing phase between spikes is controversial, and the analysis of slow oscillatory potential (SOP) recordings during the blockade of sodium channels has led to conflicting conclusions. Based on a minimal model of a dopaminergic neuron, our analysis suggests that the same experimental protocol may lead to drastically different observations in almost identical neurons. For example, complete L-type calcium channel blockade eliminates spontaneous firing or has almost no effect in two neurons differing by less than 1% in their maximal sodium conductance. The same prediction can be reproduced in a state of the art detailed model of a dopaminergic neuron. Some of these predictions are confirmed experimentally using single-cell recordings in brain slices. Our minimal model exhibits SOPs when sodium channels are blocked, these SOPs being uncorrelated with the spiking activity, as has been shown experimentally. We also show that block of a specific conductance (in this case, the SK conductance) can have a different effect on these two oscillatory behaviors (pacemaking and SOPs), despite the fact that they have the same initiating mechanism. These results highlight the fact that computational approaches, besides their well known confirmatory and predictive interests in neurophysiology, may also be useful to resolve apparent discrepancies between experimental results. © 2011 Drion et al.

M-type channels selectively control bursting in rat dopaminergic neurons

Midbrain dopaminergic neurons in the substantia nigra, pars compacta and ventral tegmental area are critically important in many physiological functions. These neurons exhibit firing patterns that include tonic slow pacemaking, irregular firing and bursting, and the amount of dopamine that is present in the synaptic cleft is much increased during bursting. The mechanisms responsible for the switch between these spiking patterns remain unclear. Using both in-vivo recordings combined with microiontophoretic or intraperitoneal drug applications and in-vitro experiments, we have found that M-type channels, which are present in midbrain dopaminergic cells, modulate the firing during bursting without affecting the background low-frequency pacemaker firing. Thus, a selective blocker of these channels, 10,10-bis(4-pyridinylmethyl)-9(10H)- anthracenone dihydrochloride, specifically potentiated burst firing. Computer modeling of the dopamine neuron confirmed the possibility of a differential influence of M-type channels on excitability during various firing patterns. Therefore, these channels may provide a novel target for the treatment of dopamine-related diseases, including Parkinson's disease and drug addiction. Moreover, our results demonstrate that the influence of M-type channels on the excitability of these slow pacemaker neurons is conditional upon their firing pattern. © 2010 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex.

BACKGROUND: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. RESULTS: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. CONCLUSIONS: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

Rapid electrohydrodynamic lithography using low-viscosity polymers.

This study explores a number of low-viscosity glass-forming polymers for their suitability as high-speed materials in electrohydrodynamic (EHD) lithography. The use of low-viscosity polymer films significantly reduces the patterning time (to below 10 s) compared to earlier approaches, without compromising the high fidelity of the replicated structures. The rapid pace of this process requires a method to monitor the completion of EHD pattern formation. To this end, the leakage current across the device is monitored and the sigmoidal shape of the current curve is correlated with the various stages of EHD pattern formation.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.

Patterning and detailed study of human hNT astrocytes on parylene-C/silicon dioxide substrates to the single cell level.

It is estimated that the adult human brain contains 100 billion neurons with 5-10 times as many astrocytes. Although it has been generally considered that the astrocyte is a simple supportive cell to the neuron, recent research has revealed new functionality of the astrocyte in the form of information transfer to neurons of the brain. In our previous work we developed a protocol to pattern the hNT neuron (derived from the human teratocarcinoma cell line (hNT)) on parylene-C/SiO(2) substrates. In this work, we report how we have managed to pattern hNT astrocytes, on parylene-C/SiO(2) substrates to single cell resolution. This article disseminates the nanofabrication and cell culturing steps necessary for the patterning of such cells. In addition, it reports the necessary strip lengths and strip width dimensions of parylene-C that encourage high degrees of cellular coverage and single cell isolation for this cell type. The significance in patterning the hNT astrocyte on silicon chip is that it will help enable single cell and network studies into the undiscovered functionality of this interesting cell, thus, contributing to closer pathological studies of the human brain.

Nucleated polymerization with secondary pathways. I. Time evolution of the principal moments.

Self-assembly processes resulting in linear structures are often observed in molecular biology, and include the formation of functional filaments such as actin and tubulin, as well as generally dysfunctional ones such as amyloid aggregates. Although the basic kinetic equations describing these phenomena are well-established, it has proved to be challenging, due to their non-linear nature, to derive solutions to these equations except for special cases. The availability of general analytical solutions provides a route for determining the rates of molecular level processes from the analysis of macroscopic experimental measurements of the growth kinetics, in addition to the phenomenological parameters, such as lag times and maximal growth rates that are already obtainable from standard fitting procedures. We describe here an analytical approach based on fixed-point analysis, which provides self-consistent solutions for the growth of filamentous structures that can, in addition to elongation, undergo internal fracturing and monomer-dependent nucleation as mechanisms for generating new free ends acting as growth sites. Our results generalise the analytical expression for sigmoidal growth kinetics from the Oosawa theory for nucleated polymerisation to the case of fragmenting filaments. We determine the corresponding growth laws in closed form and derive from first principles a number of relationships which have been empirically established for the kinetics of the self-assembly of amyloid fibrils.

Binding of the molecular chaperone αB-crystallin to Aβ amyloid fibrils inhibits fibril elongation.

The molecular chaperone αB-crystallin is a small heat-shock protein that is upregulated in response to a multitude of stress stimuli, and is found colocalized with Aβ amyloid fibrils in the extracellular plaques that are characteristic of Alzheimer's disease. We investigated whether this archetypical small heat-shock protein has the ability to interact with Aβ fibrils in vitro. We find that αB-crystallin binds to wild-type Aβ(42) fibrils with micromolar affinity, and also binds to fibrils formed from the E22G Arctic mutation of Aβ(42). Immunoelectron microscopy confirms that binding occurs along the entire length and ends of the fibrils. Investigations into the effect of αB-crystallin on the seeded growth of Aβ fibrils, both in solution and on the surface of a quartz crystal microbalance biosensor, reveal that the binding of αB-crystallin to seed fibrils strongly inhibits their elongation. Because the lag phase in sigmoidal fibril assembly kinetics is dominated by elongation and fragmentation rates, the chaperone mechanism identified here represents a highly effective means to inhibit fibril proliferation. Together with previous observations of αB-crystallin interaction with α-synuclein and insulin fibrils, the results suggest that this mechanism is a generic means of providing molecular chaperone protection against amyloid fibril formation.