Gamma-range oscillations evoked by Optogenetic stimulation - an in silico study

Gamma-range oscillations are repetitive neuronal activation in various regions of the brain, displaying prominent energy distribution in the 30-90 Hz frequency band. It is shown that these oscillations emerge through excitatory-inhibitory neuronal interplay, but their mechanisms and functions remain unknown. Therefore, it is necessary to simplify the in vivo complexity. This has been accomplished by the host laboratory, which reproduced these rhythms in an in vitro model of cortical microcircuitry using Optogenetic tools and suggested a simple firing-rate mathematical model. Since Optogenetics influences synaptic efficacy, I propose an extension of this mathematical model by dynamical properties of synaptic transmission

Substrate arrays of iridium oxide microelectrodes for in vitro neuronal interfacing

The design of novel bidirectional interfaces for in vivo and in vitro nervous systems is an important step towards future functional neuroprosthetics. Small electrodes, structures and devices are necessary to achieve high-resolution and target-selectivity during stimulation and recording of neuronal networks, while significant charge transfer and large signal-to-noise ratio are required for accurate time resolution. In addition, the physical properties of the interface should remain stable across time, especially when chronic in vivo applications or in vitro long-term studies are considered, unless a procedure to actively compensate for degradation is provided. In this short report, we describe the use and fabrication of arrays of 120 planar microelectrodes (MEAs) of sputtered Iridium Oxide (IrOx). The effective surface area of individual microelectrodes is significantly increased using electrochemical activation, a procedure that may also be employed to restore the properties of the electrodes as required. The electrode activation results in a very low interface impedance, especially in the lower frequency domain, which was characterized by impedance spectroscopy. The increase in the roughness of the microelectrodes surface was imaged using digital holographic microscopy and electron microscopy. Aging of the activated electrodes was also investigated, comparing storage in saline with storage in air. Demonstration of concept was achieved by recording multiple single-unit spike activity in acute brain slice preparations of rat neocortex. Data suggests that extracellular recording of action potentials can be achieved with planar IrOx MEAs with good signal-to-noise ratios. © 2009 Gawad, Giugliano, Heuschkel, Wessling, Markram, Schnakenberg, Renaud and Morgan

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane's inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite-soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neuron

Correlation transfer by layer 5 cortical neurons under recreated synaptic inputs in vitro

Correlated electrical activity in neurons is a prominent characteristic of cortical microcircuits. Despite a growing amount of evidence concerning both spike-count and subthreshold membrane potential pairwise correlations, little is known about how different types of cortical neurons convert correlated inputs into correlated outputs. We studied pyramidal neurons and two classes of GABAergic interneurons of layer 5 in neocortical brain slices obtained from rats of both sexes, and we stimulated them with biophysically realistic correlated inputs, generated using dynamic clamp. We found that the physiological differences between cell types manifested unique features in their capacity to transfer correlated inputs. We used linear response theory and computational modeling to gain clear insights into how cellular properties determine both the gain and timescale of correlation transfer, thus tying single-cell features with network interactions. Our results provide further ground for the functionally distinct roles played by various types of neuronal cells in the cortical microcircuit

Editorial: Brain-inspired computing: Neuroscience drives the development of new electronics and artificial intelligence

Editorial for a special issue (i.e. "Research Topic") launched on the Journal Frontiers in Cellular Neuroscience - Section Cellular Neurophysiology

Advances in Nano Neuroscience: From Nanomaterials to Nanotools

During the last decades, neuroscientists have increasingly exploited a variety of artificial, de-novo synthesized materials with controlled nano-sized features. For instance, a renewed interest in the development of prostheses or neural interfaces was driven by the availability of novel nanomaterials that enabled the fabrication of implantable bioelectronics interfaces with reduced side effects and increased integration with the target biological tissue. The peculiar physical-chemical properties of nanomaterials have also contributed to the engineering of novel imaging devices toward sophisticated experimental settings, to smart fabricated scaffolds and microelectrodes, or other tools ultimately aimed at a better understanding of neural tissue functions. In this review, we focus on nanomaterials and specifically on carbon-based nanomaterials, such as carbon nanotubes (CNTs) and graphene. While these materials raise potential safety concerns, they represent a tremendous technological opportunity for the restoration of neuronal functions. We then describe nanotools such as nanowires and nano-modified MEA for high-performance electrophysiological recording and stimulation of neuronal electrical activity. We finally focus on the fabrication of three-dimensional synthetic nanostructures, used as substrates to interface biological cells and tissues in vitro and in vivo

The Dynamical Response Properties of Neocortical Neurons to Temporally Modulated Noisy Inputs In Vitro

Cortical neurons are often classified by current-frequency relationship. Such a static description is inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. To test these model predictions, we estimated the linear response properties of layer 5 pyramidal cells by injecting a superposition of a small-amplitude sinusoidal wave and a background noise. We characterized the evoked firing probability across many stimulation trials and a range of oscillation frequencies (1-1000 Hz), quantifying response amplitude and phase-shift while changing noise statistics. We found that neurons track unexpectedly fast transients, as their response amplitude has no attenuation up to 200 Hz. This cut-off frequency is higher than the limits set by passive membrane properties (∼50 Hz) and average firing rate (∼20 Hz) and is not affected by the rate of change of the input. Finally, above 200 Hz, the response amplitude decays as a power-law with an exponent that is independent of voltage fluctuations induced by the background nois