Patented biologically-inspired technological innovations: A twenty year view

Publication rate of patents can be a useful measure of innovation and productivity in fields of science and technology. To assess the growth in industrially-important research, I conducted an appraisal of patents published between 1985 and 2005 on online databases using keywords chosen to select technologies arising as a result of biological inspiration. Whilst the total number of patents increased over the period examined, those with biomimetic content had increased faster as a proportion of total patent publications. Logistic regression analysis reveals that we may be a little over half way through an initial innovation cycle inspired by biological systems.

Synapsing variable length crossover: Biologically inspired crossover for variable length genomes

Biological Crossover occurs during the early stages of meiosis. During this process the chromosomes undergoing crossover are synapsed together at a number of homogenous sequence sections, it is within such synapsed sections that crossover occurs. The SVLC (Synapsing Variable Length Crossover) Algorithm recurrently synapses homogenous genetic sequences together in order of length. The genomes are considered to be flexible with crossover only being permitted within the synapsed sections. Consequently, common sequences are automatically preserved with only the genetic differences being exchanged, independent of the length of such differences. In addition to providing a rationale for variable length crossover it also provides a genotypic similarity metric for variable length genomes enabling standard niche formation techniques to be utilised. In a simple variable length test problem the SVLC algorithm outperforms current variable length crossover techniques.

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.

Peptide mediated formation of hierarchically organized solution and solid state polymer nanostructures

Biologically-inspired peptide sequences have been explored as auxiliaries to mediate self-assembly of synthetic macromolecules into hierarchically organized solution and solid state nanostructures. Peptide sequences inspired by the coiled coil motif and "switch" peptides, which can adopt both amphiphilic alpha-helical and beta-strand conformations, were conjugated to poly(ethylene glycol) (PEG). The solution and solid state self-assembly of these materials was investigated using a variety of spectroscopic, scattering and microscopic techniques. These experiments revealed that the folding and organization properties of the peptide sequences are retained upon conjugation of PEG and that they provide the driving force for the formation of the different nanoscale structures which were observed. The possibility of using defined peptide sequences to direct structure formation of synthetic polymers together with the potential of peptide sequences to induce a specific biological response offers interesting prospects for the development of novel self-assembled and biologically active materials.

Technology trajectories, innovation, and the growth of biomimetics

Publication rate of patents can be a useful measure of innovation and productivity in science and technology. Patenting activity in new technological fields follows a sigmoid (S-shaped) path. Qualitative and quantitative models in management and economics literature explain why such patterns of productivity may occur. TRIZ analysis suggests that patents are generated in bursts during the evolution of a product and that they are at different levels of inventiveness. The tendency is for the inventiveness to reduce as the product is more mature. This makes it possible to guess at the lifetime stage of a product and gauge its maturity and profitability. An analysis of patenting activity and other measures of inventiveness in the emerging field of biomimetics was presented, and future trends in biologically-inspired innovation was discussed.

Control of a compliant two-axis robotic arm

In order to ease control, the links between actuators and robotic limbs are generally made to be as stiff as possible. This is in contrast to natural limbs, where compliance is present. Springs have been added to the drive train between the actuator and load to imitate this natural compliance. The majority of these springs have been in series between the actuator and load. However, a more biologically inspired approach is taken, here springs have been used in parallel to oppose each other. The paper will describe the application of parallel extension springs in a robot arm in order to give it compliance. Advantages and disadvantages of this application are discussed along with various control strategies.

Synapsing variable length crossover: An algorithm for crossing and comparing variable length genomes

The Synapsing Variable Length Crossover (SVLC) algorithm provides a biologically inspired method for performing meaningful crossover between variable length genomes. In addition to providing a rationale for variable length crossover it also provides a genotypic similarity metric for variable length genomes enabling standard niche formation techniques to be used with variable length genomes. Unlike other variable length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses non-random crossover points based on the common parental sequence similarity. The SVLC Algorithm recurrently "glues" or synapses homogenous genetic sub-sequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable length test problem the SVLC algorithm is shown to outperform current variable length crossover techniques. The SVLC algorithm is also shown to work in a more realistic robot neural network controller evolution application.

Synapsing variable-length crossover: Meaningful crossover for variable-length genomes

The synapsing variable-length crossover (SVLC algorithm provides a biologically inspired method for performing meaningful crossover between variable-length genomes. In addition to providing a rationale for variable-length crossover, it also provides a genotypic similarity metric for variable-length genomes, enabling standard niche formation techniques to be used with variable-length genomes. Unlike other variable-length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses non-random crossover points based on the common parental sequence similarity. The SVLC algorithm recurrently "glues" or synapses homogenous genetic subsequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable-length test problem, the SVLC algorithm compares favorably with current variable-length crossover techniques. The variable-length approach is further advocated by demonstrating how a variable-length genetic algorithm (GA) can obtain a high fitness solution in fewer iterations than a traditional fixed-length GA in a two-dimensional vector approximation task.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Associating spontaneous with evoked activity in a neural mass model of cat visual cortex

Spontaneous activity of the brain at rest frequently has been considered a mere backdrop to the salient activity evoked by external stimuli or tasks. However, the resting state of the brain consumes most of its energy budget, which suggests a far more important role. An intriguing hint comes from experimental observations of spontaneous activity patterns, which closely resemble those evoked by visual stimulation with oriented gratings, except that cortex appeared to cycle between different orientation maps. Moreover, patterns similar to those evoked by the behaviorally most relevant horizontal and vertical orientations occurred more often than those corresponding to oblique angles. We hypothesize that this kind of spontaneous activity develops at least to some degree autonomously, providing a dynamical reservoir of cortical states, which are then associated with visual stimuli through learning. To test this hypothesis, we use a biologically inspired neural mass model to simulate a patch of cat visual cortex. Spontaneous transitions between orientation states were induced by modest modifications of the neural connectivity, establishing a stable heteroclinic channel. Significantly, the experimentally observed greater frequency of states representing the behaviorally important horizontal and vertical orientations emerged spontaneously from these simulations. We then applied bar-shaped inputs to the model cortex and used Hebbian learning rules to modify the corresponding synaptic strengths. After unsupervised learning, different bar inputs reliably and exclusively evoked their associated orientation state; whereas in the absence of input, the model cortex resumed its spontaneous cycling. We conclude that the experimentally observed similarities between spontaneous and evoked activity in visual cortex can be explained as the outcome of a learning process that associates external stimuli with a preexisting reservoir of autonomous neural activity states. Our findings hence demonstrate how cortical connectivity can link the maintenance of spontaneous activity in the brain mechanistically to its core cognitive functions.

Body-centric modelling, identification, and acceleration tracking control of a quadrotor UAV

This paper presents the mathematical development of a body-centric nonlinear dynamic model of a quadrotor UAV that is suitable for the development of biologically inspired navigation strategies. Analytical approximations are used to find an initial guess of the parameters of the nonlinear model, then parameter estimation methods are used to refine the model parameters using the data obtained from onboard sensors during flight. Due to the unstable nature of the quadrotor model, the identification process is performed with the system in closed-loop control of attitude angles. The obtained model parameters are validated using real unseen experimental data. Based on the identified model, a Linear-Quadratic (LQ) optimal tracker is designed to stabilize the quadrotor and facilitate its translational control by tracking body accelerations. The LQ tracker is tested on an experimental quadrotor UAV and the obtained results are a further means to validate the quality of the estimated model. The unique formulation of the control problem in the body frame makes the controller better suited for bio-inspired navigation and guidance strategies than conventional attitude or position based control systems that can be found in the existing literature.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

The significance of "nonsignificance" in randomized controlled studies: a discussion inspired by a double-blinded study on St. John's Wort (Hypericum perforatum L.) for premenstrual symptoms

Objectives: This study aimed to investigate the efficacy of St. John's wort extract (SJW) as a treatment for premenstrual symptoms. Design: The study was a randomized, double-blinded, placebo-controlled trial, with two parallel treatment groups. After a no-treatment baseline cycle, volunteers were randomized to either SJW or placebo for a further two menstrual cycles. Settings/location: A postal trial conducted from The University of Reading, Berkshire, England. Subjects: One hundred and sixty-nine (169) normally menstruating women who experienced recurrent premenstrual symptoms were recruited onto the study. One hundred and twenty-five (125) completed the protocol and were included in the analysis. Interventions: Six hundred milligrams (600) mg of SJW (standardized to contain 1800 mug of hypericin) or placebo (containing lactose and cellulose). Outcome measure: A menstrual diary was used to assess changes in premenstrual symptoms. The anxiety-related subgroup of symptoms of this instrument was used as the primary outcome measure. Results: After averaging the effects of treatment over both treatment cycles it was found that there was a trend for SJW to be superior to placebo. However, this finding was not statistically significant. Conclusion: The possibility that this nonsignificant finding resulted from insufficient statistical power in the study, rather than a lack of efficacy of SJW, is discussed. Following this discussion the recommendation is made that, in future, similar studies should be powered to detect a minimum clinically relevant difference between treatments.

The significance of "nonsignificance" in randomized controlled studies: a discussion inspired by a double-blinded study on St. John's Wort (Hypericum perforatum L.) for premenstrual symptoms

Objectives: This study aimed to investigate the efficacy of St. John's wort extract (SJW) as a treatment for premenstrual symptoms. Design: The study was a randomized, double-blinded, placebo-controlled trial, with two parallel treatment groups. After a no-treatment baseline cycle, volunteers were randomized to either SJW or placebo for a further two menstrual cycles. Settings/location: A postal trial conducted from The University of Reading, Berkshire, England. Subjects: One hundred and sixty-nine (169) normally menstruating women who experienced recurrent premenstrual symptoms were recruited onto the study. One hundred and twenty-five (125) completed the protocol and were included in the analysis. Interventions: Six hundred milligrams (600) mg of SJW (standardized to contain 1800 mug of hypericin) or placebo (containing lactose and cellulose). Outcome measure: A menstrual diary was used to assess changes in premenstrual symptoms. The anxiety-related subgroup of symptoms of this instrument was used as the primary outcome measure. Results: After averaging the effects of treatment over both treatment cycles it was found that there was a trend for SJW to be superior to placebo. However, this finding was not statistically significant. Conclusion: The possibility that this nonsignificant finding resulted from insufficient statistical power in the study, rather than a lack of efficacy of SJW, is discussed. Following this discussion the recommendation is made that, in future, similar studies should be powered to detect a minimum clinically relevant difference between treatments.