What does it take to evolve behaviorally complex organisms?

What genotypic features explain the evolvability of organisms that have to accomplish many different tasks? The genotype of behaviorally complex organisms may be more likely to encode modular neural architectures because neural modules dedicated to distinct tasks avoid neural interference, i.e., the arrival of conflicting messages for changing the value of connection weights during learning. However, if the connection weights for the various modules are genetically inherited, this raises the problem of genetic linkage: favorable mutations may fall on one portion of the genotype encoding one neural module and unfavorable mutations on another portion encoding another module. We show that this can prevent the genotype from reaching an adaptive optimum. This effect is different from other linkage effects described in the literature and we argue that it represents a new class of genetic constraints. Using simulations we show that sexual reproduction can alleviate the problem of genetic linkage by recombining separate modules all of which incorporate either favorable or unfavorable mutations. We speculate that this effect may contribute to the taxonomic prevalence of sexual reproduction among higher organisms. In addition to sexual recombination, the problem of genetic linkage for behaviorally complex organisms may be mitigated by entrusting evolution with the task of finding appropriate modular architectures and learning with the task of finding the appropriate connection weights for these architectures

Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects

Background: Motor imagery (MI) is the mental performance of movement without muscle activity. It is generally accepted that MI and motor performance have similar physiological mechanisms. Purpose: To investigate the activity and excitability of cortical motor areas during MI in subjects who were previously trained with an MI-based brain-computer interface (BCI). Subjects and Methods: Eleven healthy volunteers without neurological impairments (mean age, 36 years; range: 24–68 years) were either trained with an MI-based BCI (BCI-trained, n = 5) or received no BCI training (n = 6, controls). Subjects imagined grasping in a blocked paradigm task with alternating rest and task periods. For evaluating the activity and excitability of cortical motor areas we used functional MRI and navigated transcranial magnetic stimulation (nTMS). Results: fMRI revealed activation in Brodmann areas 3 and 6, the cerebellum, and the thalamus during MI in all subjects. The primary motor cortex was activated only in BCI-trained subjects. The associative zones of activation were larger in non-trained subjects. During MI, motor evoked potentials recorded from two of the three targeted muscles were significantly higher only in BCI-trained subjects. The motor threshold decreased (median = 17%) during MI, which was also observed only in BCI-trained subjects. Conclusion: Previous BCI training increased motor cortex excitability during MI. These data may help to improve BCI applications, including rehabilitation of patients with cerebral palsy.Web of Science7art. no. 0016

Voluntary movement frequencies in submaximal one- and two-legged knee extension exercise and pedaling

Understanding of behavior and control of human voluntary rhythmic stereotyped leg movements is useful in work to improve performance, function, and rehabilitation of exercising, healthy, and injured humans. The present study aimed at adding to the existing understanding within this field. To pursue the aim, correlations between freely chosen movement frequencies in relatively simple, single-joint, one- and two-legged knee extension exercise were investigated. The same was done for more complex, multiple-joint, one- and two-legged pedaling. These particular activities were chosen because they could be considered related to some extent, as they shared a key aspect of knee extension, and because they at the same time were different. The activities were performed at submaximal intensities, by healthy individuals (n=16, thereof 8 women; 23.4±2.7 years; 1.70±0.11 m; 68.6±11.2 kg).High and fair correlations (R-values of 0.99 and 0.75) occurred between frequencies generated with the dominant leg and the nondominant leg during knee extension exercise and pedaling, respectively. Fair to high correlations (R-values between 0.71 and 0.95) occurred between frequencies performed with each of the two legs in an activity, and the two-legged frequency performed in the same type of activity. In general, the correlations were higher for knee extension exercise than for pedaling. Correlations between knee extension and pedaling frequencies were of modest occurrence.The correlations between movement frequencies generated separately by each of the legs might be interpreted to support the following working hypothesis, which was based on existing literature. It is likely that involved central pattern generators (CPGs) of the two legs share a common frequency generator or that separate frequency generators of each leg are attuned via interneuronal connections. Further, activity type appeared to be relevant. Thus, the apparent common rhythmogenesis for the two legs appeared to be stronger for the relatively simple single-joint activity of knee extension exercise as compared to the more complex multi-joint activity of pedaling. Finally, it appeared that the shared aspect of knee extension in the related types of activities of knee extension exercise and pedaling was insufficient to cause obvious correlations between generated movement frequencies in the two types of activities

Towards a synergy framework across neuroscience and robotics: Lessons learned and open questions. Reply to comments on: "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands"

We would like to thank all commentators for their insightful commentaries. Thanks to their diverse and complementary expertise in neuroscience and robotics, the commentators have provided us with the opportunity to further discuss state-of-the-art and gaps in the integration of neuroscience and robotics reviewed in our article. We organized our reply in two sections that capture the main points of all commentaries [1–9]: (1) Advantages and limitations of the synergy approach in neuroscience and robotics, and (2) Learning and role of sensory feedback in biological and robotics synergies

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Credit Card Fraud Detection Using LSTM Algorithm

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring

On neuromechanical approaches for the study of biological and robotic grasp and manipulation

abstract: Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-

An analysis of modular patterns in healthy and post-stroke hemiparetic gait

textRecent studies have suggested the biomechanical subtasks of walking can be produced using a reduced set of co-excited muscles or modules. Individuals post-stroke often exhibit poor inter-muscular coordination characterized by poor timing and merging of modules that are normally independent in healthy individuals. However, whether locomotor therapy can influence module quality (timing and composition) and whether these improvements lead to improved walking performance is unclear. Further, it is unknown whether the same modules that produce self-selected walking can also produce the execution of different mobility tasks. In this study, experimental analyses were used to compare module quality pre- and post-therapy. In subjects with four modules pre- and post-therapy, locomotor training resulted in improved timing of the ankle plantarflexor module and a more extended paretic leg angle that allowed the subjects to walk faster with more symmetrical propulsion. In addition, subjects with three modules pre-therapy increased their number of modules and improved walking performance post-therapy. Thus, locomotor training was found to influence module composition and timing, which can lead to improvements in walking performance. Experimental and simulation analyses were then used to characterize modular organization in specific mobility tasks (walking at self-selected speed with maximum cadence, maximum step length, and maximum step height). We found that the same underlying modules (number and composition) in each subject that contribute to steady-state walking also contribute to the different mobility tasks. In healthy subjects, module timing, but not composition, changed when the task demands were altered. This adaptability in module timing, in addition to the ability to adapt to the changing task demands, was limited in the post-stroke subjects. The primary difference in the execution of the walking biomechanical subtasks occurred in the control of the leg during pre-swing and swing. To increase cadence, the ankle plantarflexors and dorsiflexors contributed more power to the ipsilateral leg in pre-swing and swing, respectively. To increase step height, the hamstrings provided energy to the ipsilateral leg that accelerated the leg into swing in pre-swing and swing. These results provide a first step towards linking impaired module patterns to mobility task performance in persons post-stroke.Mechanical Engineerin