Creating Brain-Like Intelligence

Sendhoff, Bernhard; Körner, Edgar; Sporns, Olaf

doi:10.1007/978-3-642-00616-6_1

Cited by 15 publications

(12 citation statements)

References 29 publications

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“…As such, computational processes are 'outsourced' to the morphology automatically, which reduces the workload of the entity's control unit/s. 15,16,[20][21][22] This view of natural computing is derived from the precepts of 'morphological computation', a concept usually employed in robot design, wherein entity compliance is exploited to create self-stabilizing systems which reduce the need for constant monitoring by the control unit. 16,22 By amalgamating biological and computer sciences, recent advances in the field have shown promise in modeling natural systems which have not been fully described in purely mathematical (algorithmic) terms, e.g.…”

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confidence: 99%

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

Mayne

Adamatzky

Jones

2015

Communicative & Integrative Biology

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The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently 'intelligent' behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton-a ubiquitous cellular protein scaffold whose functions are manifold and essential to life-and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.

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confidence: 99%

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

Mayne

Adamatzky

Jones

2015

Communicative & Integrative Biology

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“…In humans, a mere six million (6 × 10 6 ) base pairs, of which the majority is not directly expressed, code for an organism of some hundred trillion (10 14 ) cells. Assuming that a great part of this genetic information concerns neural development and function [253], it gives us a rough estimate of a brain-to-genome "compression ratio". In the central nervous system of adult humans, which contains approximately 8.5×10 10 neural cells and an equivalent number of non-neural (mostly glial) cells [8], this ratio would be of the order of 10 4 .…”

Section: Generating a Brainmentioning

confidence: 99%

“…Several authors have compiled a list of "missing" properties, which would be necessary for brain-like AI. These include: the capacity to engage in a behavioural tasks; control via a simulated nervous system; continuously changing self-defined representations; and embodiment in the real world [165,253,263,292]. Embodiment, especially, is viewed as critical because by exploiting the richness of information contained in the morphology and the dynamics of the body and the environment, intelligent behaviour could be generated with far less representational complexity [228,291].…”

Section: Brain-like Ai: What's Missing?mentioning

confidence: 99%

Artificial Neurogenesis: An Introduction and Selective Review

Kowaliw

Bredèche

Chevallier³

et al. 2014

Growing Adaptive Machines

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International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature

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“…To achieve IMs, an agent must be able to represent unexpected external stimuli and states of affairs. Hierarchical open-ended architectures (HOA) are a promising option (Dileep 2008; Kurzweil 2012; Manzotti et al 2012; Sendhoff et al 2009). HOAs stand for architectures designed to represent and to interact with a potentially unlimited hierarchy of external stimuli.…”

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confidence: 99%

“…Furthermore, pace Barkow et al (1992), domain-general learning does not appear any longer a theoretical impossibility. On the contrary, many scholars are working on domain-general cognitive architectures (Dileep 2008; Doya 1999; Horton & Adams 2005; Kurzweil 2012; Markram 2006; Sendhoff et al 2009). However, these models may shed a new light on why “our ability to change our behavioral and cultural practices lags far behind our ability to manipulate the physical environment” (sect.…”

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confidence: 99%