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Exactly 100 years ago, Louis Lapicque published a paper on the excitability of nerves that is often cited in the context of integrate-and-fire neurons. We discuss Lapicque’s contributions along with a translation of the original publication. [ABSTRACT FROM AUTHOR]

The study provides an overview of the research activity carried out in the field of cybernetics. To do so, all research papers from 1997 to 2011 (16,445 research papers) under the category of 'Computer Science, Cybernetics' of Web of Science have been processed using our in-house software which is developed specifically for this purpose. Among its multiple capabilities, this software analyses individual and compound keywords, quantifies productivity taking into account the work distribution, estimates the impact of each article and determines the collaborations established at different scales. Keywords analysis identifies the evolution of the most important research topics in the field of cybernetics and their specificity in biological aspects, as well as the research topics with lesser interest. The analysis of productivity, impact and collaborations provides a framework to assess research activity in a specific and realistic context. The geographical and institutional distribution of publications reveals the leading countries and research centres, analysing their relation to main research journals. Moreover, collaborations analysis reveals great differences in terms of internationalization and complexity of research networks. The results of this study may be very useful for the characterization and the decisions made by research in the field of cybernetics. [ABSTRACT FROM AUTHOR]

This paper proposes a theoretical model of the control and perception mechanism in human balance. Human balance perception is evaluated by the subjective upright posture, the posture at which a person does not feel he/she is at an incline. Our balance experiments in the seated posture showed that the subjective upright posture changed after the balancing task where the participants needed to incline to maintain their balance. This paper aimed to explain this adaptive phenomenon by reproducing the experimental results using computer simulations. Hypothesizing that "humans gradually come to recognize the posture they need to take to maintain their balance as being upright," an adaptation rule for subjective upright posture is defined, so that it approaches the averaged posture in the period of the balancing task. For the balance control, center of pressure feedback is adopted. As a result, the similar changes in subjective upright posture are simulated with a two-link model with a base link, implying that our hypothesis is one possible explanation on the mechanism for human balance control and perception. [ABSTRACT FROM AUTHOR]

The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns. [ABSTRACT FROM AUTHOR]

Noises are ubiquitous in sensorimotor interactions and contaminate the information provided to the central nervous system (CNS) for motor learning. An interesting question is how the CNS manages motor learning with imprecise information. Integrating ideas from reinforcement learning and adaptive optimal control, this paper develops a novel computational mechanism to explain the robustness of human motor learning to the imprecise information, caused by control-dependent noise that exists inherently in the sensorimotor systems. Starting from an initial admissible control policy, in each learning trial the mechanism collects and uses the noisy sensory data (caused by the control-dependent noise) to form an imprecise evaluation of the performance of the current policy and then constructs an updated policy based on the imprecise evaluation. As the number of learning trials increases, the generated policies mathematically provably converge to a (potentially small) neighborhood of the optimal policy under mild conditions, despite the imprecise information in the learning process. The mechanism directly synthesizes the policies from the sensory data, without identifying an internal forward model. Our preliminary computational results on two classic arm reaching tasks are in line with experimental observations reported in the literature. The model-free control principle proposed in the paper sheds more lights into the inherent robustness of human sensorimotor systems to the imprecise information, especially control-dependent noise, in the CNS. [ABSTRACT FROM AUTHOR]

Retrieval of episodic memories requires intrinsic reactivation of neuronal activity patterns. The content of the memories is thereby assumed to be stored in synaptic connections. This paper proposes a theory in which these are the synaptic connections that specifically convey the temporal order information contained in the sequences of a neuronal reservoir to the sensory-motor cortical areas that give rise to the subjective impression of retrieval of sensory motor events. The theory is based on a novel recursive version of support vector regression that allows for efficient continuous learning that is only limited by the representational capacity of the reservoir. The paper argues that hippocampal theta sequences are a potential neural substrate underlying this reservoir. The theory is consistent with confabulations and post hoc alterations of existing memories. [ABSTRACT FROM AUTHOR]

The aim of this paper is to integrate different bodies of research including brain traveling waves, brain neuromodulation, neural field modeling and post-stroke language disorders in order to explore the opportunity of implementing model-guided, cortical neuromodulation for the treatment of post-stroke aphasia. Worldwide according to WHO, strokes are the second leading cause of death and the third leading cause of disability. In ischemic stroke, there is not enough blood supply to provide enough oxygen and nutrients to parts of the brain, while in hemorrhagic stroke, there is bleeding within the enclosed cranial cavity. The present paper focuses on ischemic stroke. We first review accumulating observations of traveling waves occurring spontaneously or triggered by external stimuli in healthy subjects as well as in patients with brain disorders. We examine the putative functions of these waves and focus on post-stroke aphasia observed when brain language networks become fragmented and/or partly silent, thus perturbing the progression of traveling waves across perilesional areas. Secondly, we focus on a simplified model based on the current literature in the field and describe cortical traveling wave dynamics and their modulation. This model uses a biophysically realistic integro-differential equation describing spatially distributed and synaptically coupled neural networks producing traveling wave solutions. The model is used to calculate wave parameters (speed, amplitude and/or frequency) and to guide the reconstruction of the perturbed wave. A stimulation term is included in the model to restore wave propagation to a reasonably good level. Thirdly, we examine various issues related to the implementation model-guided neuromodulation in the treatment of post-stroke aphasia given that closed-loop invasive brain stimulation studies have recently produced encouraging results. Finally, we suggest that modulating traveling waves by acting selectively and dynamically across space and time to facilitate wave propagation is a promising therapeutic strategy especially at a time when a new generation of closed-loop cortical stimulation systems is about to arrive on the market. [ABSTRACT FROM AUTHOR]

Natural phenomena can be quantitatively described by means of mathematics, which is actually the only way of doing so. Physics is a convincing example of the mathematization of nature. This paper gives an answer to the question of how mathematization of nature is done and illustrates the answer. Here nature is to be taken in a wide sense, being a substantial object of study in, among others, large domains of biology, such as epidemiology and neurobiology, chemistry, and physics, the most outspoken example. It is argued that mathematization of natural phenomena needs appropriate core concepts that are intimately connected with the phenomena one wants to describe and explain mathematically. Second, there is a scale on and not beyond which a specific description holds. Different scales allow for different conceptual and mathematical descriptions. This is the scaling hypothesis, which has meanwhile been confirmed on many occasions. Furthermore, a mathematical description can, as in physics, but need not be universally valid, as in biology. Finally, the history of science shows that only an intensive gauging of theory, i.e., mathematical description, by experiment leads to progress. That is, appropriate core concepts and appropriate scales are a necessary condition for mathematizing nature, and so is its verification by experiment. [ABSTRACT FROM AUTHOR]

Recently, 1/ f fluctuations have been discovered in the single-unit activity of mesencephalic reticular formation (MRF) neurons during REM sleep. In a previous paper, such behavior could satisfyingly be interpreted on the basis of the clustering Poisson process. The question of applicability of this model to other MRF neurons remained unanswered. The present paper reports on 1/ f fluctuations in 12 MRF neurons all of which can satisfyingly be modeled by the clustering Poisson process. [ABSTRACT FROM AUTHOR]

Detecting small moving targets against a cluttered background in visual data is a challenging task. The main problems include spatio-temporal target contrast enhancement, background suppression and accurate target segmentation. When targets are at great distances from a non-stationary camera, the difficulty of these challenges increases. In such cases the moving camera can introduce large spatial changes between frames which may cause issues in temporal algorithms; furthermore targets can approach a single pixel, thereby affecting spatial methods. Previous literature has shown that biologically inspired methods, based on the vision systems of insects, are robust to such conditions. It has also been shown that the use of divisive optic-flow inhibition with these methods enhances the detectability of small targets. However, the location within the visual pathway the inhibition should be applied was ambiguous. In this paper, we investigated the tunings of some of the optic-flow filters and use of a nonlinear transform on the optic-flow signal to modify motion responses for the purpose of suppressing false positives and enhancing small target detection. Additionally, we looked at multiple locations within the biologically inspired vision (BIV) algorithm where inhibition could further enhance detection performance, and look at driving the nonlinear transform with a global motion estimate. To get a better understanding of how the BIV algorithm performs, we compared to other state-of-the-art target detection algorithms, and look at how their performance can be enhanced with the optic-flow inhibition. Our explicit use of the nonlinear inhibition allows for the incorporation of a wider dynamic range of inhibiting signals, along with spatio-temporal filter refinement, which further increases target-background discrimination in the presence of camera motion. Extensive experiments shows that our proposed approach achieves an improvement of 25% over linearly conditioned inhibition schemes and 2.33 times the detection performance of the BIV model without inhibition. Moreover, our approach achieves between 10 and 104 times better detection performance compared to any conventional state-of-the-art moving object detection algorithm applied to the same, highly cluttered and moving scenes. Applying the nonlinear inhibition to other algorithms showed that their performance can be increased by up to 22 times. These findings show that the application of optic-flow- based signal suppression should be applied to enhance target detection from moving platforms. Furthermore, they indicate where best to look for evidence of such signals within the insect brain. [ABSTRACT FROM AUTHOR]

The visual systems of insects are relatively simple compared to humans. However, they enable navigation through complex environments where insects perform exceptional levels of obstacle avoidance. Biology uses two separable modes of optic flow to achieve this: rapid gaze fixation (rotational motion known as saccades); and the inter-saccadic translational motion. While the fundamental process of insect optic flow has been known since the 1950's, so too has its dependence on contrast. The surrounding visual pathways used to overcome environmental dependencies are less well known. Previous work has shown promise for low-speed rotational motion estimation, but a gap remained in the estimation of translational motion, in particular the estimation of the time to impact. To consistently estimate the time to impact during inter-saccadic translatory motion, the fundamental limitation of contrast dependence must be overcome. By adapting an elaborated rotational velocity estimator from literature to work for translational motion, this paper proposes a novel algorithm for overcoming the contrast dependence of time to impact estimation using nonlinear spatio-temporal feedforward filtering. By applying bioinspired processes, approximately 15 points per decade of statistical discrimination were achieved when estimating the time to impact to a target across 360 background, distance, and velocity combinations: a 17-fold increase over the fundamental process. These results show the contrast dependence of time to impact estimation can be overcome in a biologically plausible manner. This, combined with previous results for low-speed rotational motion estimation, allows for contrast invariant computational models designed on the principles found in the biological visual system, paving the way for future visually guided systems. [ABSTRACT FROM AUTHOR]

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori. [ABSTRACT FROM AUTHOR]

In Bourlard and Kamp (Biol Cybern 59(4):291–294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called "auto-associative multilayer perceptrons") were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least squares solutions, pp 134–151. Springer, 1971), equivalent to principal component analysis (PCA) Hotelling (Educ Psychol 24(6/7):417–441, 1993); Jolliffe (Principal component analysis, springer series in statistics, 2nd edn. Springer, New York). That is, AE are able to derive the eigenvalues that represent the amount of variance covered by each component even with the presence of the nonlinear function (sigmoid-like, or any other nonlinear functions) present on their hidden units. Today, with the renewed interest in "deep neural networks" (DNN), multiple types of (deep) AE are being investigated as an alternative to manifold learning Cayton (Univ California San Diego Tech Rep 12(1–17):1, 2005) for conducting nonlinear feature extraction or fusion, each with its own specific (expected) properties. Many of those AE are currently being developed as powerful, nonlinear encoder–decoder models, or used to generate reduced and discriminant feature sets that are more amenable to different modeling and classification tasks. In this paper, we start by recalling and further clarifying the main conclusions of Bourlard and Kamp (Biol Cybern 59(4):291–294, 1998), supporting them by extensive empirical evidences, which were not possible to be provided previously (in 1988), due to the dataset and processing limitations. Upon full understanding of the underlying mechanisms, we show that it remains hard (although feasible) to go beyond the state-of-the-art PCA/SVD techniques for auto-association. Finally, we present a brief overview on different autoencoder models that are mainly in use today and discuss their rationale, relations and application areas. [ABSTRACT FROM AUTHOR]

If we are ever to move beyond the study of isolated special cases in theoretical neuroscience, we need to develop more general theories of neural circuits over a given neural model. The present paper considers this challenge in the context of continuous-time recurrent neural networks (CTRNNs), a simple but dynamically universal model that has been widely utilized in both computational neuroscience and neural networks. Here, we extend previous work on the parameter space structure of codimension-1 local bifurcations in CTRNNs to include codimension-2 local bifurcation manifolds. Specifically, we derive the necessary conditions for all generic local codimension-2 bifurcations for general CTRNNs, specialize these conditions to circuits containing from one to four neurons, illustrate in full detail the application of these conditions to example circuits, derive closed-form expressions for these bifurcation manifolds where possible, and demonstrate how this analysis allows us to find and trace several global codimension-1 bifurcation manifolds that originate from the codimension-2 bifurcations. [ABSTRACT FROM AUTHOR]

Information about time-dependent sensory stimuli is encoded in the activity of neural populations; distinct aspects of the stimulus are read out by different types of neurons: while overall information is perceived by integrator cells, so-called coincidence detector cells are driven mainly by the synchronous activity in the population that encodes predominantly high-frequency content of the input signal (high-pass information filtering). Previously, an analytically accessible statistic called the partial synchronous output was introduced as a proxy for the coincidence detector cell's output in order to approximate its information transmission. In the first part of the current paper, we compare the information filtering properties (specifically, the coherence function) of this proxy to those of a simple coincidence detector neuron. We show that the latter's coherence function can indeed be well-approximated by the partial synchronous output with a time scale and threshold criterion that are related approximately linearly to the membrane time constant and firing threshold of the coincidence detector cell. In the second part of the paper, we propose an alternative theory for the spectral measures (including the coherence) of the coincidence detector cell that combines linear-response theory for shot-noise driven integrate-and-fire neurons with a novel perturbation ansatz for the spectra of spike-trains driven by colored noise. We demonstrate how the variability of the synaptic weights for connections from the population to the coincidence detector can shape the information transmission of the entire two-stage system. [ABSTRACT FROM AUTHOR]

This hybrid of review and personal essay argues that models of visual construction are essential to extend spatial navigation models to models that link episodic memory and imagination. The starting point is the TAM–WG model, combining the Taxon Affordance Model and the World Graph model of spatial navigation. The key here is to reject approaches in which memory is restricted to unanalyzed views from familiar places, and their later recall. Instead, we will seek mechanisms for imagining truly novel scenes and episodes. We thus introduce a specific variant of schema theory and VISIONS, a cooperative computation model of visual scene understanding in which a scene is represented by an assemblage of schema instances with links to lower-level "patches" of relevant visual data. We sketch a new conceptual framework for future modeling, Visual Integration of Diverse Multi-Modal Aspects, by extending VISIONS from static scenes to episodes combining agents, actions and objects and assess its relevance to both navigation and episodic memory. We can then analyze imagination as a constructive process that combines aspects of memories of prior episodes along with other schemas and adjusts them into a coherent whole which, through expectations associated with diverse episodes and schemas, may yield the linkage of episodes that constitutes a dream or a narrative. The result is IBSEN, a conceptual model of Imagination in Brain Systems for Episodes and Navigation. The essay closes by analyzing other papers in this Special Issue to assess to what extent their results relate to the research proposed here. [ABSTRACT FROM AUTHOR]

In this paper a model of neural network underlying arithmetic prblem-solving is described. Memory models of procedural memory, semantic memory, and working memory, which are necessary to represent the process of the problem-solving, are constructed within a framework of a model of associative processor, HASP, proposed by one of the authors (Hirai 1983). Performance of the model has been simulated on a digital computer. By memorizing primitive knowledge of addition of two digits such as 6+8=14 in the semantic memory and procedural knowledge for the control of the process of adding in the procedural memory, the model can perform addition of multiple numbers with multiple digits. By making explicit serial associations between consecutive procedural steps, the performance of the model can be impooved, because a current procedural step primes the next one. In addition, if a preceding procedural step is a subset of the next one, merging between the two steps occurs. The performance can be improved about 20% by these priming and merging. By memorizing incorrect procedures, the model can generate four kinds of bugs of addition which were observed in children's performance. [ABSTRACT FROM AUTHOR]

The paper describes a new method of analytical description of single muscle fibre action potential suitable for computer simulation. The description introduced is a product of quadratic and gaussian functions. The coefficients of the function are determined on the basis of dependences of SFAP parameters on electrode-to-fibre distance combined from the transformed results of electrophysiological experiments and modelling. The description is being used for computer simulation of motor unit action potential which results will be described in forthcoming papers. [ABSTRACT FROM AUTHOR]

In this paper, we consider a noisy network of nonlinear systems in the sense that each system is driven by two sources of state-dependent noise: (1) an intrinsic noise that can be generated by the environment or any internal fluctuations and (2) a noisy coupling which is generated by interactions with other systems. Our goal is to understand the effect of noise and coupling on synchronization behaviors of such networks. First, we assume that all the systems are driven by a common noise and show how a common noise can be detrimental or beneficial for network synchronization behavior. Then, we assume that the systems are driven by independent noise and study network approximate synchronization behavior. We numerically illustrate our results using the example of coupled Van der Pol oscillators. [ABSTRACT FROM AUTHOR]

Stochastic oscillations can be characterized by a corresponding point process; this is a common practice in computational neuroscience, where oscillations of the membrane voltage under the influence of noise are often analyzed in terms of the interspike interval statistics, specifically the distribution and correlation of intervals between subsequent threshold-crossing times. More generally, crossing times and the corresponding interval sequences can be introduced for different kinds of stochastic oscillators that have been used to model variability of rhythmic activity in biological systems. In this paper we show that if we use the so-called mean-return-time (MRT) phase isochrons (introduced by Schwabedal and Pikovsky) to count the cycles of a stochastic oscillator with Markovian dynamics, the interphase interval sequence does not show any linear correlations, i.e., the corresponding sequence of passage times forms approximately a renewal point process. We first outline the general mathematical argument for this finding and illustrate it numerically for three models of increasing complexity: (i) the isotropic Guckenheimer–Schwabedal–Pikovsky oscillator that displays positive interspike interval (ISI) correlations if rotations are counted by passing the spoke of a wheel; (ii) the adaptive leaky integrate-and-fire model with white Gaussian noise that shows negative interspike interval correlations when spikes are counted in the usual way by the passage of a voltage threshold; (iii) a Hodgkin–Huxley model with channel noise (in the diffusion approximation represented by Gaussian noise) that exhibits weak but statistically significant interspike interval correlations, again for spikes counted when passing a voltage threshold. For all these models, linear correlations between intervals vanish when we count rotations by the passage of an MRT isochron. We finally discuss that the removal of interval correlations does not change the long-term variability and its effect on information transmission, especially in the neural context. [ABSTRACT FROM AUTHOR]

Motion camouflage is a stealth behaviour by which an insect can appear stationary at a fixed point while approaching or escaping another moving insect. Although several approaches have been proposed to generate motion camouflage in simulated and real agents, the exact mechanisms insects use to perform this complex behaviour are not well understood, especially considering their limited perceptual and computational resources. This paper sheds light on the possible underlying control mechanisms insect might use to generate motion camouflage, by training and analysing a series of motion camouflage controllers using reinforcement learning. We first investigate through simulations the most relevant information available to the insect that can be used to perform motion camouflage and analyse the learnt controllers. The results of this analysis drove us to hypothesise two simpler control mechanisms which, we show, can also generate motion camouflage. The proposed controllers are an extension of proportional navigation, another interception technique found in nature, and therefore, both animal behaviours seem to be connected. Motion camouflage can lead, among others, to novel approaches to closely observe animals in the wild, record sports events or gather information in military operations without being noticed. [ABSTRACT FROM AUTHOR]

Brain–machine interface (BMI) can realize information interaction between the brain and external devices, and yet the control accuracy is limited by the change of electroencephalogram signals. The introduction of auxiliary controller can overcome the above problems, but the performance of different auxiliary controllers is quite different. Hence, in this paper, we comprehensively compare and analyze the performance of different auxiliary controllers to provide a theoretical basis for designing BMI system. The main work includes: (1) designing four kinds of auxiliary controllers based on simultaneous perturbation stochastic approximation-function approximator (SPSA-FA), iterative feedback tuning-PID (IFT-PID), model predictive control (MPC) and model-free control (MFC); (2) based on the model of improved single-joint information transmission, constructing the closed-loop BMI systems with the decoder-based Wiener filter; and (3) comparing their performance in the constructed closed-loop BMI systems for dynamic motion restoration. The results show that the order of tracking accuracy is MPC, IFT-PID, SPSA-FA, MFC, and the order of time consumed is opposite. A good control effectiveness is achieved at the expense of time, so a suitable auxiliary controller should be selected according to the actual requirements. [ABSTRACT FROM AUTHOR]

While the unique hunting behavior of archerfish has received considerable scientific attention, the specific social cues that govern behaviors like intraspecific kleptoparasitism in the species are less understood. This paper asks whether the use of a robotic facsimile representing an archerfish can elicit a social response if it approximates an archerfish's appearance, along with key features of its hunting behavior. We found that the fish respond to the robot when it hunted, as indicated by decreasing distances between the robot and fish (and among the fish) during the robot's hunting behavior sequence, as well as higher net transfer entropy when the robot was hunting. These effects were present even when the robot's "hunt" was unproductive and did not result in food. The temporal pattern of fish approach to the robot and each other indicated that the segment of robot hunting behavior proximal to the robotic facsimile shot elicited fish behavior initially. However, earlier cues in the robot's hunting sequence became important following more experience with a food contingency. This indicates that further studies could use a robotic facsimile to conduct a detailed stimulus analysis, changing aspects of the robot's appearance and behavior to uncover the basic mechanisms of information transfer among individuals in a social hunting scenario. [ABSTRACT FROM AUTHOR]

In several papers published in Biological Cybernetics in the 1980s and 1990s, Kawato and colleagues proposed computational models explaining how internal models are acquired in the cerebellum. These models were later supported by neurophysiological experiments using monkeys and neuroimaging experiments involving humans. These early studies influenced neuroscience from basic, sensory-motor control to higher cognitive functions. One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials. Consciousness and metacognition—the ability to monitor one's own thoughts, may be part of the solution to this enigma. Based on literature reviews of the past 20 years, here we propose a computational neuroscience model of metacognition. The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. In the prefrontal cortex, a distributed executive network called the "cognitive reality monitoring network" (CRMN) orchestrates conscious involvement of generative-inverse model pairs in perception and action. Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a "responsibility signal" that gates selection and learning of pairs in perception, action, and reinforcement learning. A high responsibility signal is given to the pairs that best capture the external world, that are competent in movements (small mismatch), and that are capable of reinforcement learning (small reward-prediction error). CRMN selects pairs with higher responsibility signals as objects of metacognition, and consciousness is determined by the entropy of responsibility signals across all pairs. This model could lead to new-generation AI, which exhibits metacognition, consciousness, dimension reduction, selection of modules and corresponding representations, and learning from small samples. It may also lead to the development of a new scientific paradigm that enables the causal study of consciousness by combining CRMN and decoded neurofeedback. [ABSTRACT FROM AUTHOR]

More and more, the neurosciences and the sciences concerned with mind and cognition are burying fundamental questions under layers of professional methodology. I therefore welcome Biological Cybernetics' invitation to comment on two of my papers, (von der Malsburg 1973) and (von der Malsburg and Schneider 1986) (henceforth referred to as (I) and (II)) as an opportunity to address two fundamental questions about brain and mind: How is the brain's structure generated? and How is mental content expressed by the brain's physical states? Those two questions are deeply entangled with each other and play a kind of gateway role on the way to making progress with the issues of perception, intelligence, creativity and consciousness. [ABSTRACT FROM AUTHOR]

We celebrate the 60th anniversary of Biological Cybernetics. It has also been 30 years since "Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment" was published in Biological Cybernetics (Taga et al. in Biol Cybern 65(3):147–159, 1991). I would like to look back on the creation of this paper and discuss its subsequent development and future perspectives. [ABSTRACT FROM AUTHOR]

Maintaining balance is an essential skill regulated by the central nervous system (CNS) that helps humans to function effectively. Developing a physiologically motivated computational model of a neural controller with good performance is a central component for a large range of potential applications, such as the development of therapeutic and assistive devices, diagnosis of balance disorders, and designing robotic control systems. In this paper, we characterize the biomechanics of postural control system by considering the musculoskeletal dynamics in the sagittal plane, proprioceptive feedback, and a neural controller. The model includes several physiological structures, such as the feedforward and feedback mechanism, sensory noise, and proprioceptive feedback delays. A high-gain observer (HGO)-based feedback linearization controller represents the CNS analog in the modeling paradigm. The HGO gives an estimation of delayed states and the feedback linearization control law generates the feedback torques at joints to execute postural recovery movements. The whole scheme is simulated in MATLAB/Simulink. The simulation results show that our proposed scheme is robust against larger perturbations, sensory noises, feedback delays and retains a strong disturbance rejection and trajectory tracking capability. Overall, these results demonstrate that the nonlinear system dynamics, the feedforward and feedback mechanism, and physiological latencies play a key role in shaping the motor control process. [ABSTRACT FROM AUTHOR]

Model reduction is a central problem in mathematical biology. Reduced order models enable modeling of a biological system at different levels of complexity and the quantitative analysis of its properties, like sensitivity to parameter variations and resilience to exogenous perturbations. However, available model reduction methods often fail to capture a diverse range of nonlinear behaviors observed in biology, such as multistability and limit cycle oscillations. The paper addresses this need using differential analysis. This approach leads to a nonlinear enhancement of classical balanced truncation for biological systems whose behavior is not restricted to the stability of a single equilibrium. Numerical results suggest that the proposed framework may be relevant to the approximation of classical models of biological systems. [ABSTRACT FROM AUTHOR]

Octopus cells in the posteroventral cochlear nucleus exhibit characteristic onset responses to broad band transients but are little investigated in response to more complex sound stimuli. In this paper, we propose a phenomenological, but biophysically motivated, modeling approach that allows to simulate responses of large populations of octopus cells to arbitrary sound pressure waves. The model depends on only few parameters and reproduces basic physiological characteristics like onset firing and phase locking to amplitude modulations. Simulated responses to speech stimuli suggest that octopus cells are particularly sensitive to high-frequency transients in natural sounds and their sustained firing to phonemes provides a population code for sound level. [ABSTRACT FROM AUTHOR]

The widely cited Haken–Kelso–Bunz (HKB) model of motor coordination is used in an enormous range of applications. In this paper, we show analytically that the weakly damped, weakly coupled HKB model of two oscillators depends on only two dimensionless parameters; the ratio of the linear damping coefficient and the linear coupling coefficient and the ratio of the combined nonlinear damping coefficients and the combined nonlinear coupling coefficients. We illustrate our results with a mechanical analogue. We use our analytic results to predict behaviours in arbitrary parameter regimes and show how this led us to explain and extend recent numerical continuation results of the full HKB model. The key finding is that the HKB model contains a significant amount of behaviour in biologically relevant parameter regimes not yet observed in experiments or numerical simulations. This observation has implications for the development of virtual partner interaction and the human dynamic clamp, and potentially for the HKB model itself. [ABSTRACT FROM AUTHOR]

This paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation-exploration dilemma is dissolved by acting to minimise uncertainty (i.e. expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour—driven by novelty and the imperative to reduce uncertainty about the world—contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between ‘place cells’—that fire when a subgoal is reached—and ‘path cells’—that fire until a subgoal is reached. [ABSTRACT FROM AUTHOR]

Our preceding paper presented a relaxation oscillator model generally applicable to the description of the spike generation zone of an afferent nerve fiber. This model was shown to reproduce the measured stimulus-response characteristics of the elasmobranch ampullary electroreceptor. In this paper, our optimized model is shown to resolve input stimulus currents or current shifts as small as 50 fA. The fractional spike generator frequency shift produced by injection of this minimum resolvable current is Δf/f≈2×10. Arguments based upon known properties of both glutamatergic postsynaptic membrane channels and the electroreceptor organ suggest that this resolvability substantially exceeds that required to account for the known sensitivity of elasmobranch fish to marine electric fields. Our estimates of synaptic input current noise indicate that it will limit the minimum resolvable fractional change of synaptic input current to the range 10-10 and will thereby limit the minimum resolvable in vivo spike generator fractional frequency shift to the same range. For our optimized model, increase of the minimum resolvable fractional shift of spike generator frequency into this range can be accomplished by injection of 'white' stimulus current noise of ≈ 1 pA rms, over a bandwidth of 4-200 Hz. These results lead to the conclusion that synaptic input current noise, rather than inherent spike generator stability, limits electroreceptive sensitivity in vivo. This noise limit is also consistent with the Weber-Fechner criterion derived from psychophysical studies, which places the minimum resolvable fractional change of input stimulus in this same range. We suggest that synaptic current noise provides the physiological basis for the Weber-Fechner criterion. The model studies of this and the preceding paper indicate that the remarkable electroreceptive sensitivity exhibited by marine elasmobranches can be accounted for within the framework of well-known physical principles, with no requirement of ad hoc assumptions relating to the structure or function of the electroreceptor organ. [ABSTRACT FROM AUTHOR]

The identification of synchronously active neural assemblies in simultaneous recordings of neuron activities is an important research issue and a difficult algorithmic problem. A gravitational analysis method has been developed to detect and identify groups of neurons that tend to generate action potentials in near-synchrony from among a larger population of simultaneously recorded units. In this paper, an improved algorithm is used for the gravitational clustering method and its performance is characterized. Whereas the original algorithm ran in n time ( n = the number of neurons), the new algorithm runs in n time. Neurons are represented as particles in n-space that 'gravitate' towards one another whenever near-synchronous electrical activity occurs. Ensembles of neurons that tend to fire together then become clustered together. The gravitational technique not only identifies the synchronous groups present but also shows graphically the changing activity patterns and changing synchronies. [ABSTRACT FROM AUTHOR]

The light-growth response of Phycomyces has been studied with Gaussian white-noise test stimuli for a set of 21 double mutants affected in all pairwise combinations of genes madA to madG; these genes are associated with phototropism, the light-growth response, and other behaviors. The input-output relations of the light-growth responses of these mutants are represented by Wiener kernels in the time domain and transfer functions in the frequency domain. The results have been analyzed comparatively with those in the preceding papers on wild-type and single mutant strains. Two of the double night-blind mutants (combinations AB and BC) have especially weak, but still detectable, responses. To evaluate possible dynamic interactions among the seven mad gene products, each double-mutant transfer function was analyzed jointly with those of the parental single mutants and wild-type. Specifically, a hypothesis of dynamic independence was rejected at the 5% significance level for the following combinations: AD, AE, AG, BC, BD, BE, BF, BG, CD, CE, CF, DE, DG, and EF. A formal pictorial scheme summarizes the dynamic interactions among the mad gene products, according to this test. The high degree of interactions between the 'input' gene products ( A, B, and C) and the 'output' gene products ( D, E, F, and G) suggest that most or all of the sensory transduction pathway for the light-growth response (and phototropism) is contained in a multimolecular complex. [ABSTRACT FROM AUTHOR]

In a recent paper, Kunihiko Fukushima described a 'Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position'. The present paper presents a simplified version of the neocognitron. Whereas the latter employs a 16 x 16-element visual field which requires computer simulation, the simplified model uses a 10-element one-dimensional visual field. Two input examples are analyzed: a white sheet which is gradually lowered over a black background, and a white center dot which gradually stretches vertically in both directions until it covers the balck background. The model demonstrates invariance with respect to lateral shift. [ABSTRACT FROM AUTHOR]

The formalism of photoreceptor optics, developed in an earlier paper, is applied to a system consisting of a small receptor, supporting a few waveguide modes, which is excited by light focussed onto it by a lens. The effects of various lens parameters on the angular and absolute sensitivities of this system are described. Receptor variations in the form of caps and tapers are introduced and their effects on the excitation process evaluated. The results obtained are also related to the approach to vision research based on the receptor as a sampling element. Finally, an analysis is presented of the deduction of receptor properties by means of reverse light path observations. The emphasis is on results and graphs are presented so that trends and magnitudes are readily appreciated. [ABSTRACT FROM AUTHOR]

1. Results from the companion paper were incorporated into a physiologically realistic computer model of the three principal cell types (PD/AB, LP, PY) of the pyloric network in the stomatogastric ganglion. Parameters for the model were mostly calculated (sometimes estimated) from experimental data rather than fitting the model to observed output patterns. 2. The initial run was successful in predicting several features of the pyloric pattern: the observed gap between PD and LP bursts, the appropriate sequence of the activity periods (PD, LP, PY), and a substantial PY burst not properly simulated by an earlier model. 3. The major discrepancy between model and observed patterns was the too-early occurrence of the PY burst, which resulted in a much shortened LP burst. Motivated by this discrepancy, additional investigations were made of PY properties. A hyperpolarization-enabled depolorization-activated hyperpolarizing conductance change was discovered which may make an important contribution to the late phase of PY activity in the normal burst cycle. Addition of this effect to the model brought its predictions more in line with observed patterns. 4. Other discrepancies between model and observation were instructive and are discussed. The findings force a substantial revision in previously held ideas on pattern production in the pyloric system. More weight must be given to functional properties of individual neurons and less to properties arising purely from network interactions. This shift in emphasis may be necessary in more complicated systems as well. 5. An example has been provided of the value quantitative modeling can be to network physiology. Only through rigorous quantitative testing can qualitative theories of how the nervous system operates be substantiated. [ABSTRACT FROM AUTHOR]

The optimal linear filters derived in the preceding paper can be thoroughly evaluated using computer simulations, based on the properties of mammalian sensory and motor nerve fibres. Using reasonable values for action potential waveforms, conduction velocity and electrode noise, good separation of motor and sensory signals can be obtained. The performance of the filters is degraded by 1) increasing the electrode noise, 2) introducing dispersion in the conduction velocities, or 3) variation in the waveform of the action potentials from that used in designing the filters. However, the variations needed to seriously degrade performance are quite large compared to those which are likely to be present in mammalian nerves. Use of these filters to distinguish different classes of sensory (or motor) signals based on conduction velocity is discussed. [ABSTRACT FROM AUTHOR]

In this paper the mathematical foundations of threshold models of response latency are specified and their latency distributions are derived. On the basis of existing models some new models are proposed and formalized. The results are described in a uniform way and may be useful in selecting a model to describe latency data. [ABSTRACT FROM AUTHOR]

In a preceding paper, Poggio and Reichardt (1973a), a phenomenological theory describing the visual orientation behaviour of fixed flying flies ( Musca domestica) towards elementary patterns was presented. Some of the problems raised in this first paper are treated here in more detail. The mapping between the position dependent torque distribution - D(ψ) characteristics - associated with a given pattern and the stationary orientation distribution p(ψ), is studied taking into account that the fluctuation process (generated by the fly) is coloured gaussian noise. Under certain critical conditions this may lead to an 'early symmetry breaking' in the mean values of the p(ψ) distribution. The validity of the 'superposition principle' has also been examined. Although shift and superposition give the main qualitative features of the 'attractiveness profile' D(ψ), associated with a 2-stripe pattern, superposition does not hold quantitatively for stripe separations up to about 80°. Evidence is presented suggesting that such an effect is due to inhibitory interactions between input channels of the fly's eye. Implications of this finding with respect to the problem of spontaneous pattern preference are also discussed. [ABSTRACT FROM AUTHOR]

Two architectures of intermittent control are compared and contrasted in the context of the single inverted pendulum model often used for describing standing in humans. The architectures are similar insofar as they use periods of open-loop control punctuated by switching events when crossing a switching surface to keep the system state trajectories close to trajectories leading to equilibrium. The architectures differ in two significant ways. Firstly, in one case, the open-loop control trajectory is generated by a system-matched hold, and in the other case, the open-loop control signal is zero. Secondly, prediction is used in one case but not the other. The former difference is examined in this paper. The zero control alternative leads to periodic oscillations associated with limit cycles; whereas the system-matched control alternative gives trajectories (including homoclinic orbits) which contain the equilibrium point and do not have oscillatory behaviour. Despite this difference in behaviour, it is further shown that behaviour can appear similar when either the system is perturbed by additive noise or the system-matched trajectory generation is perturbed. The purpose of the research is to come to a common approach for understanding the theoretical properties of the two alternatives with the twin aims of choosing which provides the best explanation of current experimental data (which may not, by itself, distinguish beween the two alternatives) and suggesting future experiments to distinguish beween the two alternatives. [ABSTRACT FROM AUTHOR]

There are two issues in balancing a stick pivoting on a finger tip (or mechanically on a moving cart): maintaining the stick angle near to vertical and maintaining the horizontal position within the bounds of reach or cart track. The (linearised) dynamics of the angle are second order (although driven by pivot acceleration), and so, as in human standing, control of the angle is not, by itself very difficult. However, once the angle is under control, the position dynamics are, in general, fourth order. This makes control quite difficult for humans (and even an engineering control system requires careful design). Recently, three of the authors have experimentally demonstrated that humans control the stick angle in a special way: the closed-loop inverted pendulum behaves as a non-inverted pendulum with a virtual pivot somewhere between the stick centre and tip and with increased gravity. Moreover, they suggest that the virtual pivot lies at the radius of gyration (about the mass centre) above the mass centre. This paper gives a continuous-time control-theoretical interpretation of the virtual-pendulum approach. In particular, by using a novel cascade control structure, it is shown that the horizontal control of the virtual pivot becomes a second-order problem which is much easier to solve than the generic fourth-order problem. Hence, the use of the virtual pivot approach allows the control problem to be perceived by the subject as two separate second-order problems rather than a single fourth-order problem, and the control problem is therefore simplified. The theoretical predictions are verified using the data previously presented by three of the authors and analysed using a standard parameter estimation method. The experimental data indicate that although all subjects adopt the virtual pivot approach, the less expert subjects exhibit larger amplitude angular motion and poorly controlled translational motion. It is known that human control systems are delayed and intermittent, and therefore, the continuous-time strategy cannot be correct. However, the model of intermittent control used in this paper is based on the virtual pivot continuous-time control scheme, handles time delays and moreover masquerades as the underlying continuous-time controller. In addition, the event-driven properties of intermittent control can explain experimentally observed variability. [ABSTRACT FROM AUTHOR]

A key problem for biological motor control is to establish a link between an idea of a movement and the generation of a set of muscle-stimulating signals that lead to the movement execution. The number of signals to generate is thereby larger than the body's mechanical degrees of freedom in which the idea of the movement may be easily expressed, as the movement is actually executed in this space. A mathematical formulation that provides a solving link is presented in this paper in the form of a layered, hierarchical control architecture. It is meant to synthesise a wide range of complex three-dimensional muscle-driven movements. The control architecture consists of a 'conceptional layer', where the movement is planned, a 'structural layer', where the muscles are stimulated, and between both an additional 'transformational layer', where the muscle-joint redundancy is resolved. We demonstrate the operativeness by simulating human stance and squatting in a three-dimensional digital human model (DHM). The DHM considers 20 angular DoFs and 36 Hill-type muscle–tendon units (MTUs) and is exposed to gravity, while its feet contact the ground via reversible stick–slip interactions. The control architecture continuously stimulates all MTUs ('structural layer') based on a high-level, torque-based task formulation within its 'conceptional layer'. Desired states of joint angles (postural plan) are fed to two mid-level joint controllers in the 'transformational layer'. The 'transformational layer' communicates with the biophysical structures in the 'structural layer' by providing direct MTU stimulation contributions and further input signals for low-level MTU controllers. Thereby, the redundancy of the MTU stimulations with respect to the joint angles is resolved, i.e. a link between plan and execution is established, by exploiting some properties of the biophysical structures modelled. The resulting joint torques generated by the MTUs via their moment arms are fed back to the conceptional layer, closing the high-level control loop. Within our mathematical formulations of the Jacobian matrix-based layer transformations, we identify the crucial information for the redundancy solution to be the muscle moment arms, the stiffness relations of muscle and tendon tissue within the muscle model, and the length–stimulation relation of the muscle activation dynamics. The present control architecture allows the straightforward feeding of conceptional movement task formulations to MTUs. With this approach, the problem of movement planning is eased, as solely the mechanical system has to be considered in the conceptional plan. [ABSTRACT FROM AUTHOR]

It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation. [ABSTRACT FROM AUTHOR]

This paper proposes a biologically plausible matching method to recognize general shapes based on contour curvature information. The human visual system recognizes general shapes flexibly in real-world scenes through the ventral pathway. The pathway is typically modeled using artificial neural networks. These network models, however, do not construct a shape representation that satisfies the following required constraints: (1) The original shape should be represented by a group of partitioned contours in order to retrieve the whole shape (global information) from the partial contours (local information). (2) Coarse and fine structures of the original shapes should be individually represented in order for the visual system to respond to shapes as quickly as possible based on the least number of their features, and to discriminate between shapes based on detailed information. (3) The shape recognition realized with an artificial visual system should be invariant to geometric transformation such as expansion, rotation, or shear. In this paper, we propose a visual shape representation with geometrically characterized contour partitions described on multiple spatial scales. [ABSTRACT FROM AUTHOR]