File:Order-Based-Representation-in-Random-Networks-of-Cortical-Neurons-pcbi.1000228.s004.ogv

English: Braitenberg Vehicle. In a small yet seminal book titled Vehicles: Experiments in Synthetic Psychology (MIT Press, 1986), Valentino Braitenberg describes a set of thought experiments in which agents with simple structure behave in human-like ways; Braitenberg blatantly put forward the hypothesis that the primitives for realizing such machines are cellular and synaptic processes that are amenable for physiological characterization. The reasoning and results presented in this study make the realization of a Braitenberg vehicle that classifies objects in its visual field using a large-scale network of biological neurons a trivial matter. This is demonstrated in the attached clip that was prepared by Danny Eytan, David Ben Shimol and Lior Lev-Tov from the Technion - Israel Institute of Technology. The main text and data of the present study shows that the physical loci from which stimuli are delivered to a recurrent, large scale random network of cortical neurons, albeit causing temporally “noisy” neuronal responses, may be fully classified using the temporal order at which neurons are recruited by the different stimuli. Here, an application of this idea, in the form of a Braitenberg vehicle, is demonstrated: Inputs from the two (Right and Left) ultrasonic “eyes” of a Lego Mindstorms vehicle are sampled at 0.2 Hz and translated into stimulation of a large random network of cortical neurons at two different sites. The side corresponding to the nearest visual object (relative to vehicle's longitudinal axis, depicted by a red arrow) is classified using an Edit-distance metric based on the recruitment order of 8 neurons, similar to procedures shown in Figure 6 of the manuscript. Based on the classified activity, a command is sent to the appropriate motor attached to one of the wheels. The red trace on the left represents the total network activity (points depict evoked activity); the blue numbers in front of vehicle's “eyes” show distances (in cm) from the right and left sensed objects; the Edit distance of the evoked recruitment orders, from a predefined internal representation of the Right and Left objects, is shown in red numbers. Top left: time in seconds.