Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation

Cortical neurons are subject to sustained and irregular synaptic activity which causes important fluctuations of the membrane potential (Vm). We review here different methods to characterize this activity and its impact on spike generation. The simplified, fluctuating point-conductance model of synaptic activity provides the starting point of a variety of methods for the analysis of intracellular Vm recordings. In this model, the synaptic excitatory and inhibitory conductances are described by Gaussian-distributed stochastic variables, or colored conductance noise. The matching of experimentally recorded Vm distributions to an invertible theoretical expression derived from the model allows the extraction of parameters characterizing the synaptic conductance distributions. This analysis can be complemented by the matching of experimental Vm power spectral densities (PSDs) to a theoretical template, even though the unexpected scaling properties of experimental PSDs limit the precision of this latter approach. Building on this stochastic characterization of synaptic activity, we also propose methods to qualitatively and quantitatively evaluate spike-triggered averages of synaptic time-courses preceding spikes. This analysis points to an essential role for synaptic conductance variance in determining spike times. The presented methods are evaluated using controlled conductance injection in cortical neurons in vitro with the dynamic-clamp technique. We review their applications to the analysis of in vivo intracellular recordings in cat association cortex, which suggest a predominant role for inhibition in determining both sub- and supra-threshold dynamics of cortical neurons embedded in active networks.Comment: 9 figures, Journal of Neuroscience Methods (in press, 2008

Towards Synthetic Life: Establishing a Minimal Segrosome for the Rational Design of Biomimetic Systems

DNA segregation is a fundamental life process, crucial for renewal, reproduction and propagation of all forms of life. Hence, a dedicated segregation machinery, a segrosome, must function reliably also in the context of a minimal cell. Conceptionally, the development of such a minimal cell follows a minimalistic approach, aiming at engineering a synthetic entity only consisting of the essential key elements necessary for a cell to survive. In this thesis, various prokaryotic segregation systems were explored as possible candidates for a minimal segrosome. Such a minimal segrosome could be applied for the rational design of biomimetic systems including, but not limited to, a minimal cell. DNA segregation systems of type I (ParABS) and type II (ParMRC) were compared for ensuring genetic stabilities in vivo using vectors derived from the natural secondary chromosome of Vibrio cholerae. The type II segregation system R1-ParMRC was chosen as the most promising candidate for a minimal segrosome, and it was characterized and reconstituted in vitro. This segregation system was encapsulated into biomimetic micro-compartments and its lifetime prolonged by coupling to ATP-regenerating as well as oxygen-scavenging systems. The segregation process was coupled to in vitro DNA replication using DNA nanoparticles as a mimic of the condensed state of chromosomes. Furthermore, another type II segregation system originating from the pLS20 plasmid from Bacillus subtilis (Alp7ARC) was reconstituted in vitro as a secondary orthogonal segrosome. Finally, a chimeric RNA segregation system was engineered that could be applied for an RNA-based protocell. Overall, this work demonstrates successful bottom-up assemblies of functional molecular machines that could find applications in biomimetic systems and lead to a deeper understanding of living systems

Effect of myocyte-fibroblast coupling on the onset of pathological dynamics in a model of ventricular tissue

Managing lethal cardiac arrhythmias is one of the biggest challenges in modern cardiology, and hence it is very important to understand the factors underlying such arrhythmias. While early afterdepolarizations (EAD) of cardiac cells is known to be one such arrhythmogenic factor, the mechanisms underlying the emergence of tissue level arrhythmias from cellular level EADs is not fully understood. Another known arrhythmogenic condition is fibrosis of cardiac tissue that occurs both due to aging and in many types of heart diseases. In this paper we describe the results of a systematic insilico study, using the TNNP model of human cardiac cells and MacCannell model for (myo) fibroblasts, on the possible effects of diffuse fibrosis on arrhythmias occurring via EADs. We find that depending on the resting potential of fibroblasts (VFR), M-F coupling can either increase or decrease the region of parameters showing EADs. Fibrosis increases the probability of occurrence of arrhythmias after a single focal stimulation and this effect increases with the strength of the M-F coupling. While in our simulations, arrhythmias occur due to fibrosis induced ectopic activity, we do not observe any specific fibrotic pattern that promotes the occurrence of these ectopic sources

Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI

Creation of Defined Single Cell Resolution Neuronal Circuits on Microelectrode Arrays

The way cell-cell organization of neuronal networks influences activity and facilitates function is not well understood. Microelectrode arrays (MEAs) and advancing cell patterning technologies have enabled access to and control of in vitro neuronal networks spawning much new research in neuroscience and neuroengineering. We propose that small, simple networks of neurons with defined circuitry may serve as valuable research models where every connection can be analyzed, controlled and manipulated. Towards the goal of creating such neuronal networks we have applied microfabricated elastomeric membranes, surface modification and our unique laser cell patterning system to create defined neuronal circuits with single-cell precision on MEAs. Definition of synaptic connectivity was imposed by the 3D physical constraints of polydimethylsiloxane elastomeric membranes. The membranes had 20μm clear-through holes and 2-3μm deep channels which when applied to the surface of the MEA formed microwells to confine neurons to electrodes connected via shallow tunnels to direct neurite outgrowth. Tapering and turning of channels was used to influence neurite polarity. Biocompatibility of the membranes was increased by vacuum baking, oligomer extraction, and autoclaving. Membranes were bound to the MEA by oxygen plasma treatment and heated pressure. The MEA/membrane surface was treated with oxygen plasma, poly-D-lysine and laminin to improve neuron attachment, survival and neurite outgrowth. Prior to cell patterning the outer edge of culture area was seeded with 5x105 cells per cm and incubated for 2 days. Single embryonic day 7 chick forebrain neurons were then patterned into the microwells and onto the electrodes using our laser cell patterning system. Patterned neurons successfully attached to and were confined to the electrodes. Neurites extended through the interconnecting channels and connected with adjacent neurons. These results demonstrate that neuronal circuits can be created with clearly defined circuitry and a one-to-one neuron-electrode ratio. The techniques and processes described here may be used in future research to create defined neuronal circuits to model in vivo circuits and study neuronal network processing