Modular Neuronal Assemblies Embodied in a Closed-Loop Environment: Toward Future Integration of Brains and Machines

Abstract

Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3–8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: “random” and “modular” populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction.

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

“…The introduction of the tetanic stimulation (Cf. Procedure, point 7.2) improves significantly the distance travelled between two consecutive hits, thus improving the navigation performances of the robot 35 .…”

“…Both of these features present a problem: excessive synchronization will make it impossible to distinguish responses to stimuli from different electrodes, while cultures exhibiting epileptiform activity will respond to the first stimulus presented with a long outburst of activity, followed by a silent period, regardless of any successive stimuli delivered. Both these issues can be strongly ameliorated by the employment of patterned cultures 35 , in which the neural population is split into two or more weakly interconnected sub-groups.…”

Towards making a cyborg: A closed-loop reservoir-neuro system

“…Novellino et al (2007) developed a real-time neurorobotic system with a Khepera robot. Massobrio et al (2015); Tessadori et al (2012), also attempted a closed-loop system based on rat hippocampal neurons. Pizzi et al (2009) performed postprocessing using an artificial neural network of recordings from live neuronal cultures, while Takahashi et al (2016) suggested the usage of dissociated neural cultures as reservoir of dynamics.…”

Black boxes on wheels: research challenges and ethical problems in MEA-based robotics

“…The possibility of using biological neurons grown on microelectrode arrays (MEAs) as part of robotic systems has been studied for approximately 15 years, see DeMarse et al (2001), Cozzi et al (2005), Novellino et al (2007), Warwick et al (2010b), Tessadori et al (2012). It is hoped that MEAbased robotics will address the well-known problem that robots based on conventional computer technology are not very good at adapting to surprising or unusual situations, see Tessadori et al (2012).…”

“…It is hoped that MEAbased robotics will address the well-known problem that robots based on conventional computer technology are not very good at adapting to surprising or unusual situations, see Tessadori et al (2012). One could compare the clumsy and Address(es) of author(s) should be given irregular movements of, e.g., most current vacuum cleaner robots to the rapid escape response through jet propulsion of the squid or the sophisticated wall-following behavior of the cockroach, see Dickinson et al (2000), Cowan et al (2014).…”