The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks. Many social, technological and biological networks exhibit properties that are neither completely random, nor fully regular. They are known as complex networks and statistics exist to characterize their structure. Until recently, such networks have primarily been analyzed as fixed structures, which enable interaction between their components (nodes). The present work is one of the first empirical studies investigating the adaptation of complex networks [1]. Network evolution is particularly important for applying complex network analysis to biological systems, where the evolution of the network reflects the biological processes that drive it. Here, we characterize the functional networks obtained from neurons grown in vitro. Network properties are described at seven day intervals during the neurons' maturation period. Initially, neurons formed random networks, which spontaneously reorganized to a ‘small-world’ architecture. The ‘small-world’ concept derives from the study of social networks, where it is referred to as ‘six-degrees of separation’: the connection of any two individuals by as few as six acquaintances. In brain networks, this translates to rapid interaction between neurons, mediated by a few links between locally connected clusters (cliques) of neurons. This architecture is considered optimal for efficient information processing and its spontaneous emergence in cultured neurons is remarkable.

Review from JAMA Neurology — The Application of Implant Technology for Cybernetic Systems

To find if current dialogue systems use the same, psychotherapist questioning technique as Joseph Weizenbaum's 1960 natural language understanding programme, Eliza, the authors carried out an original experiment comparing five successful artificial dialogue systems, Cleverbot, Elbot, Eugene Goostman, JFred and Ultra Hal with an online version of Eliza. More than one hundred male and female participants with 1st or non-1st English language, age range 13–64, interacted with the systems over the Internet scoring each for conversation ability. Developers of the modern conversation systems show they deploy a variety of techniques to initiate and maintain dialogue learning from interactions with humans over the Internet. Statistical significance shows these dialogue systems are an improvement on their predecessor. Embedded on the web affording round-the-clock interaction the nature of artificial dialogue systems is evolving as these systems learn from the way humans converse. The uses of modern Elizas are proven successful as virtual assistants in e-commerce; their conversational basis is already extending into education. What we can say is modern artificial dialogue systems do talk. They are able to participate in conversation in a way their predecessor Eliza could not: they are able to share personal opinions, relay experience of family dramas, be relevant, but also be vague, and mislead just as humans do.

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.