Design for Information Processing in Living Neuronal Networks

Design for Information Processing in Living Neuronal Networks

Suguru N. Kudoh (Kwansei Gakuin University, Japan)
Copyright: © 2013 |Pages: 16
DOI: 10.4018/978-1-4666-4225-6.ch003
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Abstract

A neurorobot is a model system for biological information processing with vital components and the artificial peripheral system. As a central processing unit of the neurorobot, a dissociated culture system possesses a simple and functional network comparing to a whole brain; thus, it is suitable for exploration of spatiotemporal dynamics of electrical activity of a neuronal circuit. The behavior of the neurorobot is determined by the response pattern of neuronal electrical activity evoked by a current stimulation from outer world. “Certain premise rules” should be embedded in the relationship between spatiotemporal activity of neurons and intended behavior. As a strategy for embedding premise rules, two ideas are proposed. The first is “shaping,” by which a neuronal circuit is trained to deliver a desired output. Shaping strategy presumes that meaningful behavior requires manipulation of the living neuronal network. The second strategy is “coordinating.” A living neuronal circuit is regarded as the central processing unit of the neurorobot. Instinctive behavior is provided as premise control rules, which are embedded into the relationship between the living neuronal network and robot. The direction of self-tuning process of neurons is not always suitable for desired behavior of the neurorobot, so the interface between neurons and robot should be designed so as to make the direction of self-tuning process of the neuronal network correspond with desired behavior of the robot. Details of these strategies and concrete designs of the interface between neurons and robot are be introduced and discussed in this chapter.
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1. Introduction

A neurorobot is a model system in which a living neuronal circuit is electrically connected to a robot body (Bakkum, 2004; Kudoh, 2007, 2011; Novellino, 2007). The neurorobot serves as a simple model for reconnection of brain and peripherals and reconstruction of biological intelligence. The neurorobot concept was pioneered by Potter and colleagues in 2003, who developed “Hybrot,” a contraction of hybrid robot (Bakkum, 2004). Hybrot is so to speak a kind of extension of “Animat” reported by the same group in 2001 (DeMarse, 2001), in which a living neuronal circuit interacts with a computer-simulated environment. Although Hybrot replaced the simulated environment of Animat with a real robot body, both designs shared a common purpose: to elucidate mechanisms of biological intelligence, especially self-organization process of the intelligence during interactions between a neuronal network and the environment. Because the robot body is a mediator between neurons and environment, Hybrot exemplifies the field of “Embodied Cognitive Science,” which emphasizes the critical roles of a body on biological intelligence (Brooks, 1986; Pfeifer, 1999). Although the concept was foresighted, Animat, which simply connects neurons and the environment, could not deliver its intended behavior. It appears that “certain premise rules” should be embedded in the relationship between spatiotemporal activity of neurons and intended behavior—premise rules do not autonomously emerge from random interactions.

As a strategy for embedding premise rules, two ideas are proposed. The first is “shaping” (Chao, 2008), by which a neuronal circuit is trained to deliver a desired output. This strategy requires a supervisor to examine the output. The biological analog of the supervisor is the reward system.

The second strategy is “coordinating” (Kudoh, 2007, 2011). A living neuronal circuit is regarded as the central processing unit of the neurorobot. No supervisor is assigned, and the neuronal circuit is not manipulated by other components of the neurorobot. The desired behavior is generated by simple rules embedded in the connections between the neurons and robot body. The differences between the two strategies will be discussed later in this chapter.

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