This paper presents a chip-based electrophysiological platform enabling the study of micro- and macro-circuitry in in-vitro neuronal preparations. The approach is based on a 64x64 microelectrode array device providing extracellular electrophysiological activity recordings with high spatial (21 microm of electrode separation) and temporal resolution (from 0.13 ms for 4096 microelectrodes down to 8 micros for 64 microelectrodes). Applied to in-vitro neuronal preparations, we show how this approach enables neuronal signals to be acquired for investigating neuronal activity from single cells and microcircuits to large scale neuronal networks. The main elements of the platform are the metallic microelectrode array (MEA) implemented in Complementary Metal Oxide Semiconductor (CMOS) technology similar to a light imager, the in-pixel integrated low-noise amplifiers (11 microVrms) and the high-speed random addressing logic. The chip is combined with a real-time acquisition system providing the capability to record at 7.8 kHz/electrode the whole array and to process the acquired signals.

A platform for high spatial and temporal resolution electrophysiological recordings of in vitro electrogenic cell cultures handling 4096 electrodes at a full frame rate of 8 kHz is presented and validated by means of cardiomyocyte cultures. Based on an active pixel sensor device implementing an array of metallic electrodes, the system provides acquisitions at spatial resolutions of 42 microm on an active area of 2.67 mm x 2.67 mm, and in the zooming mode, temporal resolutions down to 8 micros on 64 randomly selected electrodes. The low-noise performances of the integrated amplifier (11 microV (rms)) combined with a hardware implementation inspired by image/video processing concepts enable high-resolution acquisitions with real-time preprocessing capabilities adapted to the handling of the large amount of acquired data.

Understanding how the information is coded in large neuronal networks is one of the major challenges for neuroscience. A possible approach to investigate the information processing capabilities of the neuronal ensembles is given by the use of dissociated neuronal cultures coupled to microelectrode arrays (MEAs).Here, we describe a new strategy, based on MEAs, for studying in vitro neuronal network dynamics in interconnected sub-populations of cortical neurons. The rationale is to sub-divide the neuronal network into communicating clusters while preserving a high degree of functional connectivity within each confined sub-population, i.e. to achieve a compromise between a completely random large neuronal population and a patterned network, such as currently used with conventional MEAs.To this end, we have realized and functionally characterized a Pt microelectrode array with an integrated EPON SU-8 clustering structure, allowing to confine five relatively large yet interconnected spontaneously developing neuronal networks (i.e. thousands of cells). The clustering structure consists of five chambers of 3 mm in diameter interconnected via 800 m long and 300 m wide microchannels and is integrated on the MEA of 60 thin-film Pt electrodes of 30 m diameter. Tests of the Pt microelectrodes' stability under stimulation showed a stable behavior up to 35,000 voltage stimuli and the biocompatibility was assessed with the cultures of dissociated rat's cortical neurons achieving cultures' viability up to 60 days in vitro.Compared to conventional MEAs, the monitoring of spontaneous and evoked activity and the computation of the Post-Stimulus Time Histogram (PSTH) within the clusters clearly demonstrates: (i) the capability to selectively activate (through poly-synaptic pathways) specific network regions and (ii) the confinement of the network dynamics mainly in the highly connected sub-networks.

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Based on experiments performed with high-resolution Active Pixel Sensor microelectrode arrays (APS-MEAs) coupled with spontaneously active hippocampal cultures, this work investigates the spatial resolution effects of the neuroelectronic interface on the analysis of the recorded electrophysiological signals. The adopted methodology consists, first, in recording the spontaneous activity at the highest spatial resolution (interelectrode separation of 21 μm) from the whole array of 4096 microelectrodes. Then, the full resolution dataset is spatially downsampled in order to evaluate the effects on raster plot representation, array-wide spike rate (AWSR), mean firing rate (MFR) and mean bursting rate (MBR). Furthermore, the effects of the array-to-network relative position are evaluated by shifting a subset of equally spaced electrodes on the entire recorded area. Results highlight that MFR and MBR are particularly influenced by the spatial resolution provided by the neuroelectronic interface. On high-resolution large MEAs, such analysis better represent the time-based parameterization of the network dynamics. Finally, this work suggest interesting capabilities of high-resolution MEAs for spatial-based analysis in dense and low-dense neuronal preparation for investigating signaling at both local and global neuronal circuitries.