High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes
in a single chip with an area of few square millimeters. The obtained electrode density is
comparable and even higher than the typical density of neuronal cells in cortical cultures.
Commercially available HDMEA-based acquisition systems are able to record the neural
activity from the whole array at the same time with submillisecond resolution. These devices
are a very promising tool and are increasingly used in neuroscience to tackle fundamental
questions regarding the complex dynamics of neural networks. Even if electrical or optical
stimulation is generally an available feature of such systems, they lack the capability of
creating a closed-loop between the biological neural activity and the artificial system. Stimuli
are usually sent in an open-loop manner, thus violating the inherent working basis of neural
circuits that in nature are constantly reacting to the external environment. This forbids to
unravel the real mechanisms behind the behavior of neural networks.
The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable
processing system capable of providing real-time feedback to the ongoing
neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous
FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable
System on Chip (APSoC) has been used. The device features reconfigurable
logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of
these components allows to achieve high elaboration performances while maintaining a high
level of flexibility and adaptivity. The developed system has been embedded in an acquisition
and stimulation setup featuring the following platforms:
\u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of
recording in parallel from 4096 electrodes at 18 kHz per electrode.
\u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with
custom waveforms to 16 different output channels.
\u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting
608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical
stimulation.
All the features of the system, such as band-pass filtering and spike detection of all the
recorded channels, have been validated by means of ex vivo experiments. Very low-latency
has been achieved while processing the whole input data stream in real-time. In the case
of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed,
instead, the total latency is a little higher, being 21 ms in the worst case.
The final setup is ready to be used to infer cellular properties by means of closed-loop
experiments. As a proof of this concept, it has been successfully used for the clustering
and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the
light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in
real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells.
In addition to the closed-loop system, a denoising prototype has been developed. The main
idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased
acquisition systems. The prototype is capable of processing in real-time all the input
signals from the BioCam X, and it is currently being tested to evaluate the performance in
terms of signal-to-noise-ratio improvement
