Fabio Boi

A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals

A portable 16-channels microcontroller-based wireless system for a bi-directional interaction with the central nervous system is presented in this work. The device is designed to be used with freely behaving small laboratory animals and allows recording of spontaneous and evoked neural activity wirelessly transmitted and stored on a personal computer. Biphasic current stimuli with programmable duration, frequency and amplitude may be triggered in real-time on the basis of the recorded neural activity as well as by the animal behavior within a specifically designed experimental setup. An intuitive graphical user interface was developed to configure and to monitor the whole system. The system was successfully tested through bench tests and in vivo measurements on behaving rats chronically implanted with multi-channels microwire arrays.

A bidirectional brain-machine interface connecting alert rodents to a dynamical system

We present a novel experimental framework that implements a bidirectional brain-machine interface inspired by the operation of the spinal cord in vertebrates that generates a control policy in the form of a force field. The proposed experimental set-up allows connecting the brain of freely moving rats to an external device. We tested this apparatus in a preliminary experiment with an alert rat that used the interface for acquiring a food reward. The goal of this approach to bidirectional interfaces is to explore the role of voluntary neural commands in controlling a dynamical system represented by a small cart moving on vertical plane and connected to a water/pellet dispenser.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the objects trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

A modular configurable system for closed-loop bidirectional brain-machine interfaces

Extending bidirectional brain-machine interfaces (BMI) tailored for specific experiments with additional software and hardware tools can be very onerous, if not impossible. To overcome this problem, we developed a modular configurable system by modifying the architecture of an existing bidirectional BMI. This modular system enables the seamless and efficient inclusion of new features and the integration of new protocols without changing the native systems overall structure. By introducing a platform for the implementation of BMI algorithms on neuromorphic chips, this method represents a step towards the development of low-power, compact and computationally powerful tools for clinical applications.

A compact wireless multi-channel system for real-time intracortical microstimulation of behaving rodents

In this paper we present a compact wireless 8-channels stimulating system specifically designed to deliver patterns of electrical stimuli into the cortex of freely behaving small laboratory animals. The stimuli are designed as trains of bi-phasic current pulses with programmable duration, frequency and amplitude using a LabVIEW based graphical user interface. The reduced weight of the stimulation control unit and the possibility to directly connect it to a behavioral box, makes the system suitable to be worn by rodents while performing behavioral experiments.

SiNAPS: an implantable Active Pixel Sensor CMOS-probe for Simultaneous large-scale Neural recordings

Large-scale neural recordings with high spatial and temporal accuracy are instrumental to understand how the brain works. To this end, it is of key importance to develop probes that can be conveniently scaled up to a high number of recording channels. Despite recent achievements in complementary metal-oxide semiconductor (CMOS) multi-electrode arrays probes, in current circuit architectures an increase in the number of simultaneously recording channels would significantly increase the total chip area. A promising approach for overcoming this scaling issue consists in the use of the modular Active Pixel Sensor (APS) concept, in which a small front-end circuit is located beneath each electrode. However, this approach imposes challenging constraints on the area of the in-pixel circuit, power consumption and noise. Here, we present an APS CMOS-probe technology for Simultaneous Neural recording that successfully addresses all these issues for whole-array read-outs at 25 kHz/channel from up to 1024 electrode-pixels. To assess the circuit performances, we realized in a 0.18 μm CMOS technology an implantable single-shaft probe with a regular array of 512 electrode-pixels with a pitch of 28 μm. Extensive bench tests showed an in-pixel gain of 45.4 ± 0.4 dB (low pass, F-3 dB = 4 kHz), an input referred noise of 7.5 ± 0.67 μVRMS (300 Hz to 7.5 kHz) and a power consumption <6 μW/pixel. In vivo acute recordings demonstrate that our SiNAPS CMOS-probe can sample full-band bioelectrical signals from each electrode, with the ability to resolve and discriminate activity from several packed neurons both at the spatial and temporal scale. These results pave the way to new generations of compact and scalable active single/multi-shaft brain recording systems.

A high temporal resolution multiscale recording system for in vivo neural studies

Understanding the interplay among the spectrum of electrophysiological signals in the brain, distributed over broad spatial and temporal scales, is fundamental to decipher how brain circuits operate and might dysfunction in disease. Here, we present a multiscale recording system for simultaneous and synchronous acquisitions from a new generation of im-plantable CMOS active probes (single-shaft, 512 microelectrodes, 25 kHz/electrode full-array sampling) as well as from implantable conventional electrode array probes interfaced with a custom-designed CMOS-based headstage. The platform can manage highresolution recordings from up to 4 differently located probes, and one 36-channels low-resolution passive electrode array. As presented here, and prior to complete in-vivo validation, the design and recording performance of high- and low-density electrode array ICs were experimentally tested ex-vivo on retinal whole-mounts.

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the networks current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical systems position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMIs performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

A study on the effect of multisensory stimulation in behaving rats

This study explored the psychophysical effects of intracortical microstimulation (ICMS) coupled to auditory stimulation during a behavioral detection task in rats. ICMS directed to the sensory areas of the cortex can be instrumental in facilitating operant conditioning behavior. Moreover, multisensory stimulation promotes learning by enabling the subject to access multiple information channels. However, the extent to which multisensory information can be used as a cue to make decisions has not been fully understood. This study addressed the exploration of the parameters of multisensory stimulation delivered to behaving rats in an operant conditioning task. Preliminary data indicate that animal decisions can be shaped by online changing the stimulation parameters.

Bidirectional Brain–Machine Interfaces

Brain–machine interface (BMI) systems establish a connection between the brain and artificial devices, such as a computer, a wheelchair, or a robotic arm, with the main goal of restoring lost sensory or motor functions in patients suffering from neurological injuries or diseases. A possible way of improving the performance of BMIs is to establish a two-way artificial bidirectional channel by integrating and exchanging online motor and sensory information in a closed-loop stream between the nervous system and the controlled device. In this chapter we first describe the essential components of a typical BMI providing an overview of the existing systems, and we then report a novel closed-loop bidirectional BMI developed in our laboratory.