File Name: neuron function inferred from behavioral and electrophysiological extimates of refractory period .zip
- Analysis Of Nif Scaling Using Physics Informed Machine Learning
- Inferring and validating mechanistic models of neural microcircuits based on spike-train data
- Absolute refractory period of neurons involved in MFB self-stimulation.
Analysis Of Nif Scaling Using Physics Informed Machine Learning
Quantitative Biology: Dynamics of living systems View all 13 Articles. Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously.
However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry.
We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function. Studying the function and connectivity of neurons in the brain involves coordinated, interdisciplinary efforts among scientists from various fields. Through the years, advancements in genetic markers, immunostaining, optical and electro-optical methods, electrophysiology, and computational tools have been made to identify neuronal types, explain their molecular machinery, untangle their wiring, decipher principles of neural coding, and to attribute functional roles to specific regions of the brain.
The brain is a complex system and its activity spans over multiple temporal and spatial scales that require a comprehensive set of technologies to address these scales. Innovations in experimental methods to observe and perturb brain activity and in computational tools to analyze recorded data are needed to master the brain's complexity and advance our understanding of its function.
Systems biology has allowed to bridge between molecular dynamics and whole cell simulations using multi-scale modeling. Applying similar approaches to brain activity will allow us to gain a more encompassing understanding of it. However, quantitative data at all these spatial and temporal scales are a prerequisite. The electrical nature of neuronal activity makes it possible to detect signals on electrodes at a distance from the source, but not without caveats.
It is necessary to determine the recording capabilities and limits of the device used and to understand how the neuronal signal is transduced into a recorded digital form. Typical electrophysiological methods are shown in Figure 1 and further described below.
Figure 1. Typical electrophysiological methods. A Macroscopic recording via electroencephalography EEG and mesoscopic recording through electrocorticography ECoG and implantable electrodes, with the corresponding representative waveforms recorded in a patient with drug-resistant epilepsy.
B Mesoscopic and microscopic recording using a tetrode extracellular and a glass micropipette intracellular , respectively. The fast EAP extracted from the raw tetrode recordings correlate with the intracellular APs recorded from a pyramidal cell. Right Signals for simultaneous extracellular and intracellular recordings modified with permission from Henze et al.
At the microscale, patch-clamp can be used to measure currents of single ion channels. The function of single neurons is often explored by direct measurements of the intracellular voltage, using patch-clamp or a sharp microelectrode.
It is a powerful but tedious method and often its use is limited to a few neurons per experiment Wood et al. Planar patch-clamp systems allow rapid in vitro patch-clamping, mostly used for high-throughput ion channel screening of dissociated cells Dunlop et al.
Automated patch-clamp allows for fast in vivo intracellular recording and it is feasible to extend the method to measure several neurons simultaneously Kodandaramaiah et al. The bulkiness of current micromanipulators and patch-clamp systems together with the necessity for accurate and precise control have limited simultaneous patch-clamp recordings to a few—maximum of four and twelve for in vivo Kodandaramaiah et al.
At the macroscale, indirect measurement of large areas of the brain's activity is achieved via functional magnetic resonance imaging fMRI , positron emission tomography PET , and electroencephalography EEG. These methods can be used to resolve functional connectivity among brain regions. For example, EEG detects spontaneous or evoked electrical activity from the scalp with low spatial resolution cm range.
This method enables simultaneous and long-term recordings of local field potentials LFPs and extracellular action potentials EAPs from a population of neurons at millisecond time scale. It also allows perturbing neuronal activity using electrical stimulation. As data obtained from in vivo and in vitro experiments are often very similar, the MEA technology, concepts, and applications we include here apply to both and will be helpful for scientists and engineers from either field.
In particular, we explain the interface between the neuron and the electrode in order to understand how to interpret the recordings. The advantages of HDMEAs include the capability to map neuronal activity at sub-cellular resolution, localize single cells, and to constrain full-compartmental neuron models. The outline is as follows. Chapter 2 gives an overview of the MEA technologies, including the comparison between in vivo and in vitro MEA devices from a technical aspect. Chapter 3 describes the current understanding on microelectrode recordings and introduces the different factors that shape the recorded signals.
We then conclude in Chapter 5 with perspectives on advanced measurements and applications of MEAs for studying neuronal function. Over the years, a wide repertoire of terms has been used to refer to and distinguish between all the different forms of MEAs, e. We would therefore like to briefly explain the terminology used in the context of this review. We generalize the term microelectrodes and MEA to cover both substrate-integrated planar MEAs and implantable neural probes.
We also include capacitive-coupled devices, such as multi-transistor arrays in the definition of MEAs. We then distinguish between implantable, in vivo MEAs, such as polytrodes and neural probes, and in vitro MEAs that generally include a cell culture dish or some other sort of medium chamber.
With system, we refer to the MEA and all required components to operate it, such as the data acquisition hardware and software. There are various techniques for fabricating microelectrodes, which are reviewed by Li et al.
Choosing the materials for the insulator, conductor, microelectrode, and substrate is crucial, in particular with respect to biocompatibility. All materials in the MEA that will be near to or in contact with cells and tissue need to be tested for toxicity in prolonged periods of time Hassler et al.
It is also important to consider the biological experiments for which the microelectrodes will be used, whether in vivo or in vitro , culture or acute preparation. If the MEA is to be used for stimulation, the charge capacity of electrodes is an important aspect. The electrode needs to be able to mediate reactions at the electrode-electrolyte interface to allow electron flow in the electrode to transition into ion flow in the electrolyte toward stimulating nearby cells Cogan, Generally, an important goal of electrode fabrication is to achieve low impedance.
Uniformity of the electrode impedance across an array of electrodes may also be important to obtain consistent data. Typically, electrodes are made with metallic conductors such as gold Au , titanium nitride TiN , platinum Pt , stainless steel, aluminum Al , and alloys like iridium oxide IrOx. Since the electrodes used in MEAs are on the micrometer scale, it is a challenge to achieve low electrode impedance with plain conductors only.
Increasing the effective surface area of electrodes can be achieved by modification with porous conductive materials such as Pt-black, Au nanostructures, carbon nanotubes CNTs , and conductive polymers like poly 3,4-ethylenedioxythiophene PEDOT.
By modifying the surface, the electrode impedance can be decreased drastically and neuronal recording can be improved Cui et al. Nam and Wheeler , Kim et al. Non-metallic electrodes have been mostly used in conjunction with field-effect transistor FET based transducers Bergveld, ; Fromherz et al.
However, as Hierlemann et al. OGFET, EGFET, and devices that directly connect the electrode to the first FET usually need to include some measures to properly bias the gate or some calibration mechanism, which may cause transient currents to flow at the electrode. Whereas for devices with a capacitively coupled front-end stage, controlling the electrode input node is generally not needed. Devices with a FET-based transducer, but using a metalized gate exposed to the liquid, have also been developed Jobling et al.
Recently, ultra-small electrodes are being developed to record intracellular activity, including subthreshold signals, as reviewed in Spira and Hai This is achieved by 3D structured electrodes such as silicon nanowires Robinson et al. Electroporation was shown to facilitate measurement of intracellular activity Koester et al.
Since the single extracellular microelectrodes used in the middle of the last century Weale, ; Gesteland et al. The advances in lithographic techniques, fueled by the semiconductor industry, allowed a gradual increase in performance and reliability of such multichannel devices. Passive transducer devices based on electrodes embedded in glass or silicon substrates with fixed wiring to amplifiers for in vitro and also in vivo applications became commercially available in the late 90 s and early years of this century.
Already early on, silicon-based biosensors for interfacing cells with microelectronics were developed Bergveld, ; Parce et al. Devices using CMOS technology were fabricated in academic facilities DeBusschere and Kovacs, and industrial foundries, usually in conjunction with additional processing steps for biocompatibility reasons Berdondini et al.
The key advantage of integrating active electronic components on the same substrate as the actual electrodes is the possibility of a much higher electrode number and density. Due to the possibility of using active switches to time multiplex signals, integrated circuits make it feasible to transfer data from such high channel counts off chip and to overcome the connectivity limitation of passive devices. Additionally, such co-integration allows amplifying the signals with optimal quality, due to minimal parasitic capacitances and resistances Hierlemann et al.
The monolithic co-integration also allows including additional functionality, e. Figure 2A compares a variety of historical and current devices, to illustrate the evolution of MEAs with respect to overall sensing area and electrode densities. The electrode count is shown with solid lines. Multiplexed arrays employ some sort of multiplexing within the actual array Eversmann et al.
Figure 2. Device comparison. MEA comparison with respect to A electrode density and total sensing area, and B parallel recording channel count and noise level. A For devices with a regular sensor pitch, such as most in vitro MEA devices, the total area is calculated as number of electrodes times the pixel area.
For all devices, the number of electrode times the inverse of the electrode density matches the total area.
The light gray lines illustrate the number of electrodes. B The noise values shown are approximated RMS values stated in the respective citations. The conditions under which these measurements were taken usually differ significantly such as noise bandwidth, in- or exclusion of electrode noise, inclusion of ADC quantization noise, etc.
Therefore, this graph only serves as a rough comparison. The waveforms to illustrate the noise levels are simulated and have a spectrum typical for MEA recordings. The simulated spikes are typical spikes for acute brain slice measurements recorded with microelectrodes. The recorded amplitudes may vary significantly depending on preparation and sensor characteristics. Figure 3. Array architectures. This table summarizes and classifies the different architectures that are typically used for MEAs.
Advantages, disadvantages are stated and representative selected references given. A,B Fixed wiring. A Electrodes are directly connected to signal pads with no active circuitry. B Electrodes are directly connected to on-chip active circuitry for signal conditioning.
Inferring and validating mechanistic models of neural microcircuits based on spike-train data
Neuron Function Inferred from Behavioral and. Electrophysiological Estimates of Refractory Period. Abstract. The refrcactory period of neutrons mediating an.
Absolute refractory period of neurons involved in MFB self-stimulation.
Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here, we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-efficient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals.
By the end of this section, you will be able to The range of objects and phenomena studied in physics is immense. Johannes Friedrich joined the Flatiron Institute in as a member of the neuroscience group at the Center for Computational Biology. The integration of structure and high-fidelity material models in heart valve simulations using machine learning , International Journal for Numerical Methods in Biomedical Engineering, Revision Submitted , In Part 2 of the Physics Practical Skills Guide, we looked at reliability, accuracy and validity and how they are affected by different types of errors. Personalize learning, one student at a time.
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