In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

Microelectrode arrays (MEAs) enable fast and high-throughput readout of cell's electrical signals. MEAs are currently used for phenotype characterization and drug toxicity/efficacy testing with iPSC-derived neurons and cardiomyocytes. A key advantage of MEAs is the capability to record and stimulate individual neurons at multiple sites simultaneously. We will present ongoing advancements of MEA technology, with a focus on achieving higher quality recordings by means of monolithic co-integration of circuitry on chip by using CMOS technology [1]. Such high-density MEAs with more than 3000 electrodes per mm2 are a suitable tool for capturing neuronal activity across various scales, including axons, somas, dendrites, entire neurons, and networks.

studies of neuronal network dynamics often require that an accurate spike sorting be done in real time, with low execution latency (on the order of milliseconds). Moreover, modern neuronal recording systems that feature thousands of electrodes require processing of several tens or hundreds of neurons in parallel. The existing algorithms capable of performing spike overlap decomposition are generally very complex and unsuitable for real-time implementation, especially for an on-chip implementation. Here we present a hardware device capable of processing pair-wise spike overlaps in real time. A previously-published spike sorting algorithm, which is not suitable for processing data of large neuronal networks with low latency, has been optimized for high-throughput, low-latency hardware implementation. The designed hardware architecture has been verified on an FPGA platform. Low spike sorting error rates (0.05) for overlapping spikes have been achieved with a latency of 2.75 ms, rendering the system particularly suitable for use in closed-loop experiments.

Complementary semiconductor-metal-oxide (CMOS) technology is a very powerful technology that can be more or less directly interfaced to electrogenic cells, like heart or brain cells in vitro. To this end, the cells are cultured directly atop the CMOS chips, which usually undergo dedicated postprocessing to obtain a reliable bidirectional interface via noble-metal microelectrodes or high-k dielectrics. The big advantages of using CMOS integrated circuits (ICs) include connectivity, the possibility to address a large number of microelectrodes on a tiny chip, and signal quality, the possibility to condition small signals right at the spot of their generation. CMOS will be demonstrated to constitute an enabling technology that opens a route to high-spatio-temporal-resolution and low-noise electrophysiological recordings from a variety of biological preparations, such as brain slices, or cultured cardiac and brain cells. The recording technique is extracellular and noninvasive, and the CMOS chips do not leak out any toxic compounds, so that the cells remain viable for extended times. In turn, the CMOS chips have been demonstrated to survive several months of culturing while being fully immersed in saline solution and being exposed to cellular metabolic products. The latter requires dedicated passivation and packaging techniques as will be shown. Fully integrated, monolithic microelectrode systems, which feature large numbers of tightly spaced microelectrodes and the associated circuitry units for bidirectional interaction (stimulation and recording), will be in the focus of this review. The respective dense microelectrode arrays (MEAs) with small pixels enable subcellular-resolution investigation of regions of interest in, e.g., neurobiological preparations, and, at the same time, the large number of electrodes allows for studying the activity of entire neuronal networks . Application areas include neuroscience, as the devices enable fundamental neurophysiological insights at the cellular and circuit level, as well as medical diagnostics and pharmacology.

Single-chip CMOS-based biosensors that feature microcantilevers as transducer elements are presented. The cantilevers are functionalized to capture specific analytes, e.g., proteins or DNA. The binding of the analyte changes the mechanical properties of the cantilevers such as surface stress and resonant frequency, which can be detected by an integrated Wheatstone bridge. The monolithic integrated readout allows for a high signal-to-noise ratio, lowers the sensitivity to external interference and enables autonomous device operation.

A monolithic integrated tactile sensor array is presented; it is used to perform non-invasive blood pressure monitoring of a patient. The advantage of this device compared to a hand cuff based approach is the capability of recording continuous blood pressure data. The capacitive, membrane-based sensor device is fabricated in an industrial CMOS-technology combined with post-CMOS micromachining. The capacitance change is detected by a /spl Sigma//spl Delta/-modulator. The modulator is operated at a sampling rate of 128 kS/s and achieves a resolution of 12 bits with an external decimation filter and an OSR of 128.

Microfabrication processes for chemical and biochemical sensors are reviewed. Standard processing steps originating from semiconductor technology are detailed, and specific micromachining steps to fabricate three-dimensional mechanical structures are described. Fundamental chemical sensor principles are briefly abstracted and corresponding state-of-the-art examples of microfabricated chemical sensors and biosensors are given. The advantages and disadvantages of either fabricating devices in IC fabrication technology with additional microfabrication steps, or of using custom-designed nonstandard microfabrication process flows are debated. Finally, monolithic integrated chemical and biological microsensor systems are presented, which include transducer structures and operation circuitry on a single chip.