From sensors to spikes: evolving receptive fields to enhance sensorimotor information in a robot-arm

In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate sensor output per link (encoder) is commonly used to track the position and the velocity. Interfacing bio-inspired control systems with spiking neural networks emulating the cerebellum with conventional robots is not a straight forward task. Therefore, it is necessary to adapt this one-dimensional measure (encoder output) into a multidimensional space (inputs for a spiking neural network) to connect, for instance, the spiking cerebellar architecture; i.e. a translation from an analog space into a distributed population coding in terms of spikes. This paper analyzes how evolved receptive fields (optimized towards information transmission) can efficiently generate a sensorimotor representation that facilitates its discrimination from other "sensorimotor states". This can be seen as an abstraction of the Cuneate Nucleus (CN) functionality in a robot-arm scenario. We model the CN as a spiking neuron population coding in time according to the response of mechanoreceptors during a multi-joint movement in a robot joint space. An encoding scheme that takes into account the relative spiking time of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons is proposed. Due to the enormous number of possible encodings, we have applied an evolutionary algorithm to evolve the sensory receptive field representation from random to optimized encoding. Following the nature-inspired analogy, evolved configurations have shown to outperform simple hand-tuned configurations and other homogenized configurations based on the solution provided by the optimization engine (evolutionary algorithm). We have used artificial evolutionary engines as the optimization tool to circumvent nonlinearity responses in receptive fields.     

Computational nature of human adaptive control during learning of reaching movements in force fields

Learning to make reaching movements in force fields was used as a paradigm to explore the system architecture of the biological adaptive controller. We compared the performance of a number of candidate control systems that acted on a model of the neuromuscular system of the human arm and asked how well the dynamics of the candidate system compared with the movement characteristics of 16 subjects. We found that control via a supra-spinal system that utilized an adaptive inverse model resulted in dynamics that were similar to that observed in our subjects, but lacked essential characteristics. These characteristics pointed to a different architecture where descending commands were influenced by an adaptive forward model. However, we found that control via a forward model alone also resulted in dynamics that did not match the behavior of the human arm. We considered a third control architecture where a forward model was used in conjunction with an inverse model and found that the resulting dynamics were remarkably similar to that observed in the experimental data. The essential property of this control architecture was that it predicted a complex pattern of near-discontinuities in hand trajectory in the novel force field. A nearly identical pattern was observed in our subjects, suggesting that generation of descending motor commands was likely through a control system architecture that included both adaptive forward and inverse models. We found that as subjects learned to make reaching movements, adaptation rates for the forward and inverse models could be independently estimated and the resulting changes in performance of subjects from movement to movement could be accurately accounted for. Results suggested that the adaptation of the forward model played a dominant role in the motor learning of subjects. After a period of consolidation, the rates of adaptation in the internal models were significantly larger than those observed before the memory had consolidated. This suggested that consolidation of motor memory coincided with freeing of certain computational resources for subsequent learning. Received: 01 January 1998 / Accepted in revised form: 26 January 1999     

An adaptive gaze stabilization controller inspired by the vestibulo-ocular reflex

The vestibulo-ocular reflex stabilizes vision in many vertebrates. It integrates inertial and visual information to drive the eyes in the opposite direction to head movement and thereby stabilizes the image on the retina. Its adaptive nature guarantees stable vision even when the biological system undergoes dynamic changes (due to disease, growth or fatigue etc), a characteristic especially desirable in autonomous robotic systems. Based on novel, biologically plausible neurological models, we have developed a robotic testbed to qualitatively evaluate the performance of these algorithms. We show how the adaptive controller can adapt to a time varying plant and elaborate how this biologically inspired control architecture can be employed in general engineering applications where sensory feedback is very noisy and/or delayed.     

Adaptive computer control of anesthesia in humans

This paper presents an efficient computer control technique for regulation of anesthesia in humans. The anesthetic used is propofol and the objective is to control the degree of hypnosis of the patient. The paper describes the basic hardware/software setup of the system and the closed-loop methodologies. The bispectral index (BIS) is considered as the feedback signal. The control methods proposed here are based in the use of proportional integral controllers with dead-time compensation to avoid undesirable oscillations in the BIS signal during the process. The compensation is based on the Smith predictor. To guarantee the applicability of the method to different patients, an adaptive module to tune the compensator is developed. Some real and simulated results are presented in this work to attest the efficiency of the methods used.     

A biomechanical model to estimate corrective changes in muscle activation patterns for stroke patients

We have created a model to estimate the corrective changes in muscle activation patterns needed for a person who has had a stroke to walk with an improved gait-nearing that of an unimpaired person. Using this model, we examined how different functional electrical stimulation (FES) protocols would alter gait patterns. The approach is based on an electromyographically (EMG)-driven model to estimate joint moments. Different stimulation protocols were examined, which generated different corrective muscle activation patterns. These approaches grouped the muscles together into flexor and extensor groups (to simulate FES using surface electrodes) or left each muscle to vary independently (to simulate FES using intramuscular electrodes). In addition, we limited the maximal change in muscle activation (to reduce fatigue). We observed that with the two protocols (grouped and ungrouped muscles), the calculated corrective changes in muscle activation yielded improved joint moments nearly matching those of unimpaired subjects. The protocols yielded different muscle activation patterns, which could be selected based on practical condition. These calculated corrective muscle activation changes can be used in studying FES protocols, to determine the feasibility of gait retraining with FES for a given subject and to determine which protocols are most reasonable.     

Speech-intelligibility improvements using a binaural adaptive-scheme based conceptually on human auditory processing

A speech enhancement scheme is presented using diverse processing in sub-bands spaced according to a human-cochlear describing function. The binaural adaptive scheme decomposes the wide-band input signals into a number of band-limited signals, superficially similar to the treatment the human ears perform on incoming signals. The results of a series of intelligibility and formal listening tests are presented in which acoustic speech signals corrupted with recorded automobile noise were presented to 15 normal hearing volunteer subjects. For the experimental cases considered, the proposed binaural adaptive sub-band processing scheme delivers a statistically significant improvement in terms of both speech-intelligibility and perceived quality when compared with both the conventional wide-band processed and the noisy unprocessed case. The scheme is capable of extension to a potentially more flexible sub-band processing method based on a constrained artificial neural network (ANN).     

A model of the cerebellum in adaptive control of saccadic gain

 We review data showing that the cerebellum is required for adaptation of saccadic gain to repeated presentations of dual-step visual targets and thus, presumably, for providing adaptive corrections for the brainstem saccade generator in response to any error created by the open-loop saccadic system. We model the adaptability of the system in terms of plasticity of synapses from parallel fibers to Purkinje cells in cerebellar cortex, stressing the integration of cerebellar cortex and nuclei in microzones as the units for correction of motor pattern generators. We propose a model of the inferior olive as an error detector, and use a ‘window of eligibility’ to insure that error signals that elicit a corrective movement are used to adjust the original movement, not the secondary movement. In a companion paper we simulate this large, realistic network of neural-like units to study the complex spatiotemporal behavior of neuronal subpopulations implicated in the control and adaptation of saccades. Received: 25 November 1994/Accepted in revised form: 6 February 1996     

A macroecological approach to evolutionary rescue and adaptation to climate change

Despite the widespread use of ecological niche models (ENMs) for predicting the responses of species to climate change, these models do not explicitly incorporate any population‐level mechanism. On the other hand, mechanistic models adding population processes (e.g. biotic interactions, dispersal and adaptive potential to abiotic conditions) are much more complex and difficult to parameterize, especially if the goal is to predict range shifts for many species simultaneously. In particular, the adaptive potential (based on genetic adaptations, phenotypic plasticity and behavioral adjustments for physiological responses) of local populations has been a less studied mechanism affecting species’ responses to climatic change so far. Here, we discuss and apply an alternative macroecological framework to evaluate the potential role of evolutionary rescue under climate change based on ENMs. We begin by reviewing eco‐evolutionary models that evaluate the maximum sustainable evolutionary rate under a scenario of environmental change, showing how they can be used to understand the impact of temperature change on a Neotropical anuran species, the Schneider's toad Rhinella diptycha. Then we show how to evaluate spatial patterns of species’ geographic range shift using such models, by estimating evolutionary rates at the trailing edge of species distribution estimated by ENMs and by recalculating the relative amount of total range loss under climate change. We show how different models can reduce the expected range loss predicted for the studied species by potential ecophysiological adaptations in some regions of the trailing edge predicted by ENMs. For general applications, we believe that parameters for large numbers of species and populations can be obtained from macroecological generalizations (e.g. allometric equations and ecogeographical rules), so our framework coupling ENMs with eco‐evolutionary models can be applied to achieve a more accurate picture of potential impacts from climate change and other threats to biodiversity.     

Iterative design of a simulation-based module for teaching evolution by natural selection

Background

This research builds on a previous study that looked at the effectiveness of a simulation-based module for teaching students about the process of evolution by natural selection. While the previous study showed that the module was successful in teaching how natural selection works, the research uncovered some weaknesses in the design. In this paper, we used design-based research to investigate how design changes to the module affected not only students' understanding of the concepts but also their usage of misconceptions in the assessments. We present results from two studies. In study 1, we looked at gains in understanding on a pre and post-assessment for students who used the revised version of the module. We also examined misconception uses in their answer selections. In study 2, we compared the performance on a summative assessment between students who used the revised version and students who used the original version of the module. We also looked at misconception uses in their answer selections.

Results

In study 1, we saw a significant improvement in the pre-post assessment for students who used the revised version. In study 2, we did not find a significant difference on the overall performance outcome between students who used the revised and those that used the original version of the module. In both studies, however, we saw a  lower use of misconceptions after students used the revised module. In particular, we saw less use of the adaptive mutation misconception, the belief that mutations are adaptive responses to the environment and are biased towards advantageous mutations. This is promising because in the previous study there was no evidence of decreased use of this misconception.

Conclusions

Students showed learning gains on all targeted key concepts, and reduced expression of all targeted misconceptions, which was not found previously for students using the older workbook version of the module. In particular, the revised version appears to help students overcome the adaptive mutation misconception. This article demonstrates how design-based research can contribute to the ongoing improvement of evidence-based instruction in undergraduate biology classrooms.

     

A decentralized control scheme for an effective coordination of phasic and tonic control in a snake-like robot

Autonomous decentralized control has attracted considerable attention because it enables us to understand the adaptive and versatile locomotion of animals and facilitates the construction of truly intelligent artificial agents. Thus far, we have developed a snake-like robot (HAUBOT I) that is driven by a decentralized control scheme based on a discrepancy function, which incorporates phasic control. In this paper, we investigate a decentralized control scheme in which phasic and tonic control are well coordinated, as an extension of our previous study. To verify the validity of the proposed control scheme, we apply it to a snake-like robot (HAUBOT II) that can adjust both the phase relationship between its body segments and the stiffness at each joint. The results indicate that the proposed control scheme enables the robot to exhibit remarkable real-time adaptability over various frictional and inclined terrains. These findings can potentially enable us to gain a deeper insight into the autonomous decentralized control mechanism underlying the adaptive and resilient locomotion of animals.     

Finding scalable configurations for AEDB broadcasting protocol using multi-objective evolutionary algorithms

Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.     

Extended CADLIVE: a novel graphical notation for design of biochemical network maps and computational pathway analysis

Biochemical network maps are helpful for understanding the mechanism of how a collection of biochemical reactions generate particular functions within a cell. We developed a new and computationally feasible notation that enables drawing a wide resolution map from the domain-level reactions to phenomenological events and implemented it as the extended GUI network constructor of CADLIVE (Computer-Aided Design of LIVing systEms). The new notation presents ‘Domain expansion’ for proteins and RNAs, ‘Virtual reaction and nodes’ that are responsible for illustrating domain-based interaction and ‘InnerLink’ that links real complex nodes to virtual nodes to illustrate the exact components of the real complex. A modular box is also presented that packs related reactions as a module or a subnetwork, which gives CADLIVE a capability to draw biochemical maps in a hierarchical modular architecture. Furthermore, we developed a pathway search module for virtual knockout mutants as a built-in application of CADLIVE. This module analyzes gene function in the same way as molecular genetics, which simulates a change in mutant phenotypes or confirms the validity of the network map. The extended CADLIVE with the newly proposed notation is demonstrated to be feasible for computational simulation and analysis.     

Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance

The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC₅₀ and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes, and in terms of how they inform new models for the evolution of drug resistance.     

Peeling off the hidden genetic heterogeneities of cancers based on disease-relevant functional modules

Discovering molecular heterogeneities in phenotypically defined disease is of critical importance both for understanding pathogenic mechanisms of complex diseases and for finding efficient treatments. Recently, it has been recognized that cellular phenotypes are determined by the concerted actions of many functionally related genes in modular fashions. The underlying modular mechanisms should help the understanding of hidden genetic heterogeneities of complex diseases. We defined a putative disease module to be the functional gene groups in terms of both biological process and cellular localization, which are significantly enriched with genes highly variably expressed across the disease samples. As a validation, we used two large cancer datasets to evaluate the ability of the modules for correctly partitioning samples. Then, we sought the subtypes of complex diffuse large B-cell lymphoma (DLBCL) using a public dataset. Finally, the clinical significance of the identified subtypes was verified by survival analysis. In two validation datasets, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Then, for the notoriously heterogeneous DLBCL, we demonstrated that two partitioned subtypes using an identified module ("cellular response to stress") had very different 5-year overall rates (65% vs. 14%) and were highly significantly (P < 0.007) correlated with the clinical survival rate. Finally, we built a multivariate Cox proportional-hazard prediction model that included 4 genes as risk predictors for survival over DLBCL. The proposed modular approach is a promising computational strategy for peeling off genetic heterogeneities and understanding the modular mechanisms of human diseases such as cancers.     

On-line Optimization of Biomimetic Undulatory Swimming by an Experiment-based Approach

An experiment-based approach is proposed to improve the performance of biomimetic undulatory locomotion through on-line optimization. The approach is implemented through two steps： （1） the generation of coordinated swimming gaits by artificial Central Pattern Generators （CPGs）; （2） an on-line searching of optimal parameter sets for the CPG model using Genetic Algorithm （GA）. The effectiveness of the approach is demonstrated in the optimization of swimming speed and energy effi- ciency for a biomimetic fin propulsor. To evaluate how well the input energy is converted into the kinetic energy of the pro- pulsor, an energy-efficiency index is presented and utilized as a feedback to regulate the on-line searching with a closed-loop swimming control. Experiments were conducted on propulsor prototypes with different fin segments and the optimal swimming patterns were found separately. Comparisons of results show that the optimal curvature of undulatory propulsor, which might have different shapes depending on the actual prototype design and control scheme. It is also found that the propulsor with six fin segments, is preferable because of hizher speed and lower energy efficiency.     

A Supervisory Control Approach for Execution Control of an FMC

This paper presents a generic methodology based on formal language theory for the modeling and control of flexible manufacturing cell (FMC) systems. The motivating idea behind the overall approach stems from the supervisory control theory under the framework of Ramadge and Wonham. Essentially, we characterize the asynchronous and dynamic behavior of an FMC as a regular language and formulate the control logic generation problem as a sublanguage calculation problem, which requires the resulting language to satisfy at least two properties: maximal permissiveness and controllability. Then an algorithm for resolving the problem is presented. Based on the solution of the problem called supervisor, we propose a controller architecture that guarantees coordinated operation of an FMC through the regulation of occurrences of events. An adaptive control policy that regenerates supervisors on changes in task configurations is presented and a dynamic equation that describes the evolution of the control logic along time is derived. Then, we show that the proposed maximally permissive adaptive control policy has a number of preferred properties, including computational efficiency and consistency between the successive supervisors. Finally, a controller for an example FMC is implemented, using the object-oriented software modules. Our procedure has the merit of mathematical soundness, modular design, and systematic implementation.     

Crossmodal visual input for odor tracking during fly flight

Flies generate robust and high-performance olfactory and visual behaviors. Adult fruit flies can distinguish small differences in odor concentration across antennae separated by less than 1 mm [1], and a single olfactory sensory neuron is sufficient for near-normal gradient tracking in larvae [2]. During flight a male housefly chasing a female executes a corrective turn within 40 ms after a course deviation by its target [3]. The challenges imposed by flying apparently benefit from the tight integration of unimodal sensory cues. Crossmodal interactions reduce the discrimination threshold for unimodal memory retrieval by enhancing stimulus salience [4], and dynamic crossmodal processing is required for odor search during free flight because animals fail to locate an odor source in the absence of rich visual feedback [5]. The visual requirements for odor localization are unknown. We tethered a hungry fly in a magnetic field, allowing it to yaw freely, presented odor plumes, and examined how visual cues influence odor tracking. We show that flies are unable to use a small-field object or landmark to assist plume tracking, whereas odor activates wide-field optomotor course control to enable accurate orientation toward an attractive food odor.     

GeneCensus: genome comparisons in terms of metabolic pathway activity and protein family sharing

We present a prototype of a new database tool, GeneCensus, which focuses on comparing genomes globally, in terms of the collective properties of many genes, rather than in terms of the attributes of a single gene (e.g. sequence similarity for a particular ortholog). The comparisons are presented in a visual fashion over the web at GeneCensus.org. The system concentrates on two types of comparisons: (i) trees based on the sharing of generalized protein families between genomes, and (ii) whole pathway analysis in terms of activity levels. For the trees, we have developed a module (TreeViewer) that clusters genomes in terms of the folds, superfamilies or orthologs—all can be considered as generalized ‘families’ or ‘protein parts’—they share, and compares the resulting trees side-by-side with those built from sequence similarity of individual genes (e.g. a traditional tree built on ribosomal similarity). We also include comparisons to trees built on whole-genome dinucleotide or codon composition. For pathway comparisons, we have implemented a module (PathwayPainter) that graphically depicts, in selected metabolic pathways, the fluxes or expression levels of the associated enzymes (i.e. generalized ‘activities’). One can, consequently, compare organisms (and organism states) in terms of representations of these systemic quantities. Develop ment of this module involved compiling, calculating and standardizing flux and expression information from many different sources. We illustrate pathway analysis for enzymes involved in central metabolism. We are able to show that, to some degree, flux and expression fluctuations have characteristic values in different sections of the central metabolism and that control points in this system (e.g. hexokinase, pyruvate kinase, phosphofructokinase, isocitrate dehydrogenase and citric synthase) tend to be especially variable in flux and expression. Both the TreeViewer and PathwayPainter modules connect to other information sources related to individual-gene or organism properties (e.g. a single-gene structural annotation viewer).     

Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature.