PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (66)
 

Clipboard (0)
None

Select a Filter Below

Journals
Year of Publication
more »
Document Types
1.  Integrating Information from Different Senses in the Auditory Cortex 
Biological cybernetics  2012;106(0):617-625.
Multisensory integration was once thought to be the domain of brain areas high in the cortical hierarchy, with early sensory cortical fields devoted to unisensory processing of inputs from their given set of sensory receptors. More recently, a wealth of evidence documenting visual and somatosensory responses in auditory cortex, even as early as the primary fields, has changed this view of cortical processing. These multisensory inputs may serve to enhance responses to sounds that are accompanied by other sensory cues, effectively making them easier to hear, but may also act more selectively to shape the receptive field properties of auditory cortical neurons to the location or identity of these events. We discuss the new, converging evidence that multiplexing of neural signals may play a key role in informatively encoding and integrating signals in auditory cortex across multiple sensory modalities. We highlight some of the many open research questions that exist about the neural mechanisms that give rise to multisensory integration in auditory cortex, which should be addressed in future experimental and theoretical studies.
doi:10.1007/s00422-012-0502-x
PMCID: PMC4340563  PMID: 22798035
Multisensory; Auditory Cortex; Perception; Multiplex; Information; Neural code; Visual; Somatosensory
2.  Developmental time windows for axon growth influence neuronal network topology 
Biological Cybernetics  2015;109(2):275-286.
Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.
Electronic supplementary material
The online version of this article (doi:10.1007/s00422-014-0641-3) contains supplementary material, which is available to authorized users.
doi:10.1007/s00422-014-0641-3
PMCID: PMC4366563  PMID: 25633181
Brain connectivity; Network development; Computational neuroanatomy; Complex networks; Neural networks
3.  STDP in lateral connections creates category-based perceptual cycles for invariance learning with multiple stimuli 
Biological Cybernetics  2014;109(2):215-239.
Learning to recognise objects and faces is an important and challenging problem tackled by the primate ventral visual system. One major difficulty lies in recognising an object despite profound differences in the retinal images it projects, due to changes in view, scale, position and other identity-preserving transformations. Several models of the ventral visual system have been successful in coping with these issues, but have typically been privileged by exposure to only one object at a time. In natural scenes, however, the challenges of object recognition are typically further compounded by the presence of several objects which should be perceived as distinct entities. In the present work, we explore one possible mechanism by which the visual system may overcome these two difficulties simultaneously, through segmenting unseen (artificial) stimuli using information about their category encoded in plastic lateral connections. We demonstrate that these experience-guided lateral interactions robustly organise input representations into perceptual cycles, allowing feed-forward connections trained with spike-timing-dependent plasticity to form independent, translation-invariant output representations. We present these simulations as a functional explanation for the role of plasticity in the lateral connectivity of visual cortex.
doi:10.1007/s00422-014-0637-z
PMCID: PMC4366549  PMID: 25488769
Transformation-invariant representations; Visual object recognition; Scene segmentation; Lateral plasticity ; Spiking neural net; STDP
4.  Sparse sampling: theory, methods and an application in neuroscience 
Biological Cybernetics  2014;109:125-139.
The current methods used to convert analogue signals into discrete-time sequences have been deeply influenced by the classical Shannon–Whittaker–Kotelnikov sampling theorem. This approach restricts the class of signals that can be sampled and perfectly reconstructed to bandlimited signals. During the last few years, a new framework has emerged that overcomes these limitations and extends sampling theory to a broader class of signals named signals with finite rate of innovation (FRI). Instead of characterising a signal by its frequency content, FRI theory describes it in terms of the innovation parameters per unit of time. Bandlimited signals are thus a subset of this more general definition. In this paper, we provide an overview of this new framework and present the tools required to apply this theory in neuroscience. Specifically, we show how to monitor and infer the spiking activity of individual neurons from two-photon imaging of calcium signals. In this scenario, the problem is reduced to reconstructing a stream of decaying exponentials.
doi:10.1007/s00422-014-0639-x
PMCID: PMC4315512  PMID: 25452206
Sampling theory; FRI; Spike train inference; Calcium transient
5.  Inhibitory control of sensory gating in a computer model of the CA3 region of the hippocampus 
Biological cybernetics  2003;88(4):247-264.
A model of the CA3 region of the hippocampus was used to simulate the P50 auditory-evoked potential response to repeated stimuli in order to study the neuronal circuits involved in a sensory-processing deficit associated with schizophrenia. Normal subjects have a reduced P50 auditory-evoked potential amplitude in response to the second of two paired auditory click stimuli spaced 0.5 s apart. However, schizophrenic patients do not gate or reduce their response to the second click. They have equal auditory-evoked response amplitudes to both clicks. When schizophrenic patients were medicated with traditional neuroleptics, the evoked potential amplitude to both clicks increased, but gating of the second response was not restored or improved. Animal studies suggest a role for septohippocampal cholinergic activity in sensory gating. We used a computational model of this system in order to study the relative contributions of local processing and afferent activity in sensory gating. We first compared the effect of information representation as average firing rate to information representation as cell assemblies in order to evaluate the best method to represent the response of hippocampal neurons to the auditory click. We then studied the effects of nicotinic cholinergic input on the response of the network and the effect of GABAB receptor activation on the ability of the local network to suppress the test response. The results of our model showed that nicotinic cholinergic input from the septum to the hippocampus can control the flow of sensory information from the cortex into the hippocampus. In addition, postsynaptic GABAB receptor activation was not sufficient to suppress the test response when the interstimulus interval was 500 ms. However, presynaptic GABAB receptor activity may be responsible for the suppression of the test response at this interstimulus interval.
doi:10.1007/s00422-002-0373-7
PMCID: PMC4170679  PMID: 12690484
6.  Distribution of axon diameters in cortical white matter: an electron-microscopic study on three human brains and a macaque 
Biological Cybernetics  2014;108(5):541-557.
The aim of this study was to obtain information on the axonal diameters of cortico-cortical fibres in the human brain, connecting distant regions of the same hemisphere via the white matter. Samples for electron microscopy were taken from the region of the superior longitudinal fascicle and from the transitional white matter between temporal and frontal lobe where the uncinate and inferior occipitofrontal fascicle merge. We measured the inner diameter of cross sections of myelinated axons. For comparison with data from the literature on the human corpus callosum, we also took samples from that region. For comparison with well-fixed material, we also included samples from corresponding regions of a monkey brain (Macaca mulatta). Fibre diameters in human brains ranged from 0.16 to 9 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu \hbox {m}$$\end{document}. Distributions of diameters were similar in the three systems of cortico-cortical fibres investigated, both in humans and the monkey, with most of the average values below 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu $$\end{document}m diameter and a small population of much thicker fibres. Within individual human brains, the averages were larger in the superior longitudinal fascicle than in the transitional zone between temporal and frontal lobe. An asymmetry between left and right could be found in one of the human brains, as well as in the monkey brain. A correlation was also found between the thickness of the myelin sheath and the inner axon diameter for axons whose calibre was greater than about 0.6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu \hbox {m}$$\end{document}. The results are compared to white matter data in other mammals and are discussed with respect to conduction velocity, brain size, cognition, as well as diffusion weighted imaging studies.
doi:10.1007/s00422-014-0626-2
PMCID: PMC4228120  PMID: 25142940
Axon calibre; White matter; Conduction time; Diffusion weighted imaging; Electron microscopy; Myelin
7.  Active inference, eye movements and oculomotor delays 
Biological Cybernetics  2014;108(6):777-801.
This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalised coordinates of motion. Representing hidden states in generalised coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the generative model to simulate smooth pursuit eye movements—in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system—like the oculomotor system—tries to control its environment with delayed signals.
doi:10.1007/s00422-014-0620-8
PMCID: PMC4250571  PMID: 25128318
Oculomotor delays; Tracking eye movements; Smooth pursuit eye movements; Generalised coordinates; Variational free energy; Active inference
8.  Modular Inverse Reinforcement Learning for Visuomotor Behavior 
Biological cybernetics  2013;107(4):477-490.
In a large variety of situations one would like to have an expressive and accurate model of observed animal or human behavior. While general purpose mathematical models may capture successfully properties of observed behavior, it is desirable to root models in biological facts. Because of ample empirical evidence for reward-based learning in visuomotor tasks we use a computational model based on the assumption that the observed agent is balancing the costs and benefits of its behavior to meet its goals. This leads to using the framework of Reinforcement Learning, which additionally provides well-established algorithms for learning of visuomotor task solutions. To quantify the agent’s goals as rewards implicit in the observed behavior we propose to use inverse reinforcement learning, which quantifies the agent’s goals as rewards implicit in the observed behavior. Based on the assumption of a modular cognitive architecture, we introduce a modular inverse reinforcement learning algorithm that estimates the relative reward contributions of the component tasks in navigation, consisting of following a path while avoiding obstacles and approaching targets. It is shown how to recover the component reward weights for individual tasks and that variability in observed trajectories can be explained succinctly through behavioral goals. It is demonstrated through simulations that good estimates can be obtained already with modest amounts of observation data, which in turn allows the prediction of behavior in novel configurations.
doi:10.1007/s00422-013-0562-6
PMCID: PMC3773182  PMID: 23832417
Inverse reinforcement learning; Visuomotor behavior; Spatial navigation; Task priorities
9.  Thinking in circuits: toward neurobiological explanation in cognitive neuroscience 
Biological Cybernetics  2014;108(5):573-593.
Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.
doi:10.1007/s00422-014-0603-9
PMCID: PMC4228116  PMID: 24939580
Action perception circuit; Cell assembly; Concept; Mirror neuron; Memory cell; Meaning; Semantic category; Semantics
10.  Spike width and frequency alter stability of phase-locking in electrically coupled neurons 
Biological cybernetics  2013;107(3):367-383.
The stability of phase-locked states of electrically coupled type-1 phase response curve neurons is studied using piecewise linear formulations for their voltage profile and phase response curves. We find that at low frequency and/or small spike width, synchrony is stable, and anti-synchrony unstable. At high frequency and/or large spike width, these phase-locked states switch their stability. Increasing the ratio of spike width to spike height causes the antisynchronous state to transition into a stable synchronous state. We compute the interaction function and the boundaries of stability of both these phase-locked states, and present analytical expressions for them. We also study the effect of phase response curve skewness on the boundaries of synchrony and antisynchrony.
doi:10.1007/s00422-013-0556-4
PMCID: PMC3738216  PMID: 23592015
11.  Intermittent control models of human standing: similarities and differences 
Biological Cybernetics  2014;108(2):159-168.
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.
doi:10.1007/s00422-014-0587-5
PMCID: PMC3962584  PMID: 24500616
Intermittent control; Predictive control; Human balancing; Quiet standing
12.  Statistics and geometry of orientation selectivity in primary visual cortex 
Biological Cybernetics  2013;108(5):631-653.
Orientation maps are a prominent feature of the primary visual cortex of higher mammals. In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains. However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion. Although subject of intense investigation for half a century now, it is still not entirely clear how these maps emerge and what function they might serve. Here, we suggest a new model of orientation selectivity that combines the geometry and statistics of clustered thalamocortical afferents to explain the emergence of orientation maps. We show that the model can generate spatial patterns of orientation selectivity closely resembling the maps found in cats or monkeys. Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.
doi:10.1007/s00422-013-0576-0
PMCID: PMC4228171  PMID: 24248916
Orientation selectivity; Orientation map; Columnar structure; Random connectivity;  Primary visual cortex
13.  Hill-type muscle model parameters determined from experiments on single muscles show large animal-to-animal variation 
Biological cybernetics  2012;106(10):559-571.
Models built using mean data can represent only a very small percentage, or none, of the population being modeled, and produce different activity than any member of it. Overcoming this ‘averaging’ pitfall requires measuring, in single individuals in single experiments, all of the system’s defining characteristics. We have developed protocols that allow all the parameters in the curves used in typical Hill-type models (passive and active force-length, series elasticity, force-activation, force-velocity) to be determined from experiments on individual stick insect muscles (Blümel et al. 2011a). A requirement for means to not well represent the population is that the population shows large variation in its defining characteristics. We therefore used these protocols to measure extensor muscle defining parameters in multiple animals. Across-animal variability in these parameters can be very large, ranging from 1.3 to 17-fold. This large variation is consistent with earlier data in which extensor muscle responses to identical motor neuron driving showed large animal-to-animal variability (Hooper et al. 2006), and suggests accurate modeling of extensor muscles requires modeling individual-by-individual. These complete characterizations of individual muscles also allowed us to test for parameter correlations. Two parameter pairs significantly co-varied, suggesting that a simpler model could as well reproduce muscle response.
doi:10.1007/s00422-012-0530-6
PMCID: PMC3501687  PMID: 23132430
Carausius morosus; stick insect; invertebrate
14.  Determining all parameters necessary to build Hill-type muscle models from experiments on single muscles 
Biological cybernetics  2012;106(10):543-558.
Characterizing muscle requires measuring such properties as force–length, force–activation, and force–velocity curves. These characterizations require large numbers of data points because both what type of function (e.g., linear, exponential, hyperbolic) best represents each property, and the values of the parameters in the relevant equations, need to be determined. Only a few properties are therefore generally measured in experiments on any one muscle, and complete characterizations are obtained by averaging data across a large number of muscles. Such averaging approaches can work well for muscles that are similar across individuals. However, considerable evidence indicates that large inter-individual variation exists, at least for some muscles. This variation poses difficulties for across-animal averaging approaches. Methods to fully describe all muscle’s characteristics in experiments on individual muscles would therefore be useful. Prior work in stick insect extensor muscle has identified what functions describe each of this muscle’s properties and shown that these equations apply across animals. Characterizing these muscles on an individual-by-individual basis therefore requires determining only the values of the parameters in these equations, not equation form. We present here techniques that allow determining all these parameter values in experiments on single muscles. This technique will allow us to compare parameter variation across individuals and to model muscles individually. Similar experiments can likely be performed on single muscles in other systems. This approach may thus provide a widely applicable method for characterizing and modeling muscles from single experiments.
doi:10.1007/s00422-012-0531-5
PMCID: PMC3505888  PMID: 23132431
Carausius morosus; Stick insect; Invertebrate
15.  A computational theory of visual receptive fields 
Biological Cybernetics  2013;107(6):589-635.
A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of visual processing that reflect symmetry properties of the surrounding world. These symmetry properties include (i) covariance properties under scale changes, affine image deformations, and Galilean transformations of space–time as occur for real-world image data as well as specific requirements of (ii) temporal causality implying that the future cannot be accessed and (iii) a time-recursive updating mechanism of a limited temporal buffer of the past as is necessary for a genuine real-time system. Fundamental structural requirements are also imposed to ensure (iv) mutual consistency and a proper handling of internal representations at different spatial and temporal scales. It is shown how a set of families of idealized receptive field profiles can be derived by necessity regarding spatial, spatio-chromatic, and spatio-temporal receptive fields in terms of Gaussian kernels, Gaussian derivatives, or closely related operators. Such image filters have been successfully used as a basis for expressing a large number of visual operations in computer vision, regarding feature detection, feature classification, motion estimation, object recognition, spatio-temporal recognition, and shape estimation. Hence, the associated so-called scale-space theory constitutes a both theoretically well-founded and general framework for expressing visual operations. There are very close similarities between receptive field profiles predicted from this scale-space theory and receptive field profiles found by cell recordings in biological vision. Among the family of receptive field profiles derived by necessity from the assumptions, idealized models with very good qualitative agreement are obtained for (i) spatial on-center/off-surround and off-center/on-surround receptive fields in the fovea and the LGN, (ii) simple cells with spatial directional preference in V1, (iii) spatio-chromatic double-opponent neurons in V1, (iv) space–time separable spatio-temporal receptive fields in the LGN and V1, and (v) non-separable space–time tilted receptive fields in V1, all within the same unified theory. In addition, the paper presents a more general framework for relating and interpreting these receptive fields conceptually and possibly predicting new receptive field profiles as well as for pre-wiring covariance under scaling, affine, and Galilean transformations into the representations of visual stimuli. This paper describes the basic structure of the necessity results concerning receptive field profiles regarding the mathematical foundation of the theory and outlines how the proposed theory could be used in further studies and modelling of biological vision. It is also shown how receptive field responses can be interpreted physically, as the superposition of relative variations of surface structure and illumination variations, given a logarithmic brightness scale, and how receptive field measurements will be invariant under multiplicative illumination variations and exposure control mechanisms.
doi:10.1007/s00422-013-0569-z
PMCID: PMC3840297  PMID: 24197240
Receptive field; Scale space; Gaussian derivative; Scale covariance ; Affine covariance; Galilean covariance; Illumination invariance; LGN; Primary visual cortex; Visual area V1; Functional model; Simple cell; Double-opponent cell; Complex cell; Vision; Theoretical neuroscience; Theoretical biology
16.  Revealing instances of coordination among multiple cortical areas 
Biological Cybernetics  2013;108(5):665-675.
Cognitive functions must involve interactions between several (perhaps many) cortical regions. The instances of such interactions may not be tightly time locked to any external cue. Thus averaging over repeated trials of brain activity or its spectrograms may miss these instances. Here, coordinated activity among multiple cortical locations is revealed in ongoing activity with millisecond accuracy without the need for averaging over time or frequencies. This is based on reconstructions of the cortical current dipole amplitudes at multiple points from MEG recordings. In these current dipole traces, instances of brief activity undulations (BAUs) are automatically detected and used to reveal where and when cortical points interact. The article shows that these BAUs truly represent the reorganization of activity at the cortex and are strongly connected to behavior.
doi:10.1007/s00422-013-0574-2
PMCID: PMC4228107  PMID: 24178848
MEG; Binding; Cortical current dipoles; Higher brain functions
17.  Mechanism of suppression of sustained neuronal spiking under high-frequency stimulation 
Biological Cybernetics  2013;107(6):669-684.
Using Hodgkin–Huxley and isolated subthalamic nucleus (STN) model neurons as examples, we show that electrical high-frequency stimulation (HFS) suppresses sustained neuronal spiking. The mechanism of suppression is explained on the basis of averaged equations derived from the original neuron equations in the limit of high frequencies. We show that for frequencies considerably greater than the reciprocal of the neuron’s characteristic time scale, the result of action of HFS is defined by the ratio between the amplitude and the frequency of the stimulating signal. The effect of suppression emerges due to a stabilization of the neuron’s resting state or due to a stabilization of a low-amplitude subthreshold oscillation of its membrane potential. Intriguingly, although we neglect synaptic dynamics, neural circuity as well as contribution of glial cells, the results obtained with the isolated high-frequency stimulated STN model neuron resemble the clinically observed relations between stimulation amplitude and stimulation frequency required to suppress Parkinsonian tremor.
doi:10.1007/s00422-013-0567-1
PMCID: PMC3840296  PMID: 24146294
High-frequency deep brain stimulation; Method of averaging; Parkinson’s disease;  Hodgkin–Huxley model; Subthalamic nucleus model neuron
18.  Dynamic Primitives of Motor Behavior 
Biological cybernetics  2012;106(0):727-739.
We present in outline a theory of sensorimotor control based on dynamic primitives, which we define as attractors. To account for the broad class of human interactive behaviors—especially tool use—we propose three distinct primitives: submovements, oscillations and mechanical impedances, the latter necessary for interaction with objects. Due to fundamental features of the neuromuscular system, most notably its slow response, we argue that encoding in terms of parameterized primitives may be an essential simplification required for learning, performance, and retention of complex skills. Primitives may simultaneously and sequentially be combined to produce observable forces and motions. This may be achieved by defining a virtual trajectory composed of submovements and/or oscillations interacting with impedances. Identifying primitives requires care: in principle, overlapping submovements would be sufficient to compose all observed movements but biological evidence shows that oscillations are a distinct primitive. Conversely, we suggest that kinematic synergies, frequently discussed as primitives of complex actions, may be an emergent consequence of neuromuscular impedance. To illustrate how these dynamic primitives may account for complex actions, we briefly review three types of interactive behaviors: constrained motion, impact tasks, and manipulation of dynamic objects.
doi:10.1007/s00422-012-0527-1
PMCID: PMC3735361  PMID: 23124919
Discrete; submovement; rhythmic; oscillation; impedance; primitive
19.  Walknet, a bio-inspired controller for hexapod walking 
Biological Cybernetics  2013;107(4):397-419.
Walknet comprises an artificial neural network that allows for the simulation of a considerable amount of behavioral data obtained from walking and standing stick insects. It has been tested by kinematic and dynamic simulations as well as on a number of six-legged robots. Over the years, various different expansions of this network have been provided leading to different versions of Walknet. This review summarizes the most important biological findings described by Walknet and how they can be simulated. Walknet shows how a number of properties observed in insects may emerge from a decentralized architecture. Examples are the continuum of so-called “gaits,” coordination of up to 18 leg joints during stance when walking forward or backward over uneven surfaces and negotiation of curves, dealing with leg loss, as well as being able following motion trajectories without explicit precalculation. The different Walknet versions are compared to other approaches describing insect-inspired hexapod walking. Finally, we briefly address the ability of this decentralized reactive controller to form the basis for the simulation of higher-level cognitive faculties exceeding the capabilities of insects.
doi:10.1007/s00422-013-0563-5
PMCID: PMC3755227  PMID: 23824506
Insect locomotion; Motor control;  Decentralized architecture
20.  Modelling human visual navigation using multi-view scene reconstruction 
Biological Cybernetics  2013;107(4):449-464.
It is often assumed that humans generate a 3D reconstruction of the environment, either in egocentric or world-based coordinates, but the steps involved are unknown. Here, we propose two reconstruction-based models, evaluated using data from two tasks in immersive virtual reality. We model the observer’s prediction of landmark location based on standard photogrammetric methods and then combine location predictions to compute likelihood maps of navigation behaviour. In one model, each scene point is treated independently in the reconstruction; in the other, the pertinent variable is the spatial relationship between pairs of points. Participants viewed a simple environment from one location, were transported (virtually) to another part of the scene and were asked to navigate back. Error distributions varied substantially with changes in scene layout; we compared these directly with the likelihood maps to quantify the success of the models. We also measured error distributions when participants manipulated the location of a landmark to match the preceding interval, providing a direct test of the landmark-location stage of the navigation models. Models such as this, which start with scenes and end with a probabilistic prediction of behaviour, are likely to be increasingly useful for understanding 3D vision.
doi:10.1007/s00422-013-0558-2
PMCID: PMC3755223  PMID: 23778937
Navigation; 3D perception; Virtual reality; Stereopsis; Motion parallax; Computational modelling
21.  Subthreshold outward currents enhance temporal integration in auditory neurons 
Biological cybernetics  2003;89(5):333-340.
Many auditory neurons possess low-threshold potassium currents (IKLT ) that enhance their responsiveness to rapid and coincident inputs. We present recordings from gerbil medial superior olivary (MSO) neurons in vitro and modeling results that illustrate how IKLT improves the detection of brief signals, of weak signals in noise, and of the coincidence of signals (as needed for sound localization). We quantify the enhancing effect of IKLT on temporal processing with several measures: signal-to-noise ratio (SNR), reverse correlation or spike-triggered averaging of input currents, and inter-aural time difference (ITD) tuning curves. To characterize how IKLT, which activates below spike threshold, influences a neuron’s voltage rise toward threshold, i.e., how it filters the inputs, we focus first on the response to weak and noisy signals. Cells and models were stimulated with a computer-generated steady barrage of random inputs, mimicking weak synaptic conductance transients (the “noise”), together with a larger but still subthreshold postsynaptic conductance, EPSG (the “signal”). Reduction of IKLT decreased the SNR, mainly due to an increase in spontaneous firing (more “false positive”). The spike-triggered reverse correlation indicated that IKLT shortened the integration time for spike generation. IKLT also heightened the model’s timing selectivity for coincidence detection of simulated binaural inputs. Further, ITD tuning is shifted in favor of a slope code rather than a place code by precise and rapid inhibition onto MSO cells (Brand et al. 2002). In several ways, low-threshold outward currents are seen to shape integration of weak and strong signals in auditory neurons.
doi:10.1007/s00422-003-0438-2
PMCID: PMC3677199  PMID: 14669013
22.  A novel method for the identification of synchronization effects in multichannel ECoG with an application to epilepsy 
Biological Cybernetics  2013;107(3):321-335.
In this paper, we present a novel method for the identification of synchronization effects in multichannel electrocorticograms (ECoG). Based on autoregressive modeling, we define a dependency measure termed extrinsic-to-intrinsic power ratio (EIPR) which quantifies directed coupling effects in the time domain. Hereby, a dynamic input channel selection algorithm assures the estimation of the model parameters despite the strong spatial correlation among the high number of involved ECoG channels. We compare EIPR to the partial directed coherence, show its ability to indicate Granger causality and successfully validate a signal model. Applying EIPR to ictal ECoG data of patients suffering from temporal lobe epilepsy allows us to identify the electrodes of the seizure onset zone. The results obtained by the proposed method are in good accordance with the clinical findings.
doi:10.1007/s00422-013-0552-8
PMCID: PMC3661083  PMID: 23435583
Epilepsy; ECoG; Partial directed coherence; Synchronization; Dynamic input channel selection
23.  HEBBIAN MECHANISMS HELP EXPLAIN DEVELOPMENT OF MULTISENSORY INTEGRATION IN THE SUPERIOR COLLICULUS: A NEURAL NETWORK MODEL 
Biological cybernetics  2012;106(11-12):691-713.
The superior colliculus (SC) integrates relevant sensory information (visual, auditory, somatosensory) from several cortical and subcortical structures, to program orientation responses to external events. However, this capacity is not present at birth, and it is acquired only through interactions with cross-modal events during maturation. Mathematical models provide a quantitative framework, valuable in helping to clarify the specific neural mechanisms underlying the maturation of the multisensory integration in the SC. We extended a neural network model of the adult SC (Cuppini et al. 2010) to describe the development of this phenomenon starting from an immature state, based on known or suspected anatomy and physiology, in which: 1) AES afferents are present but weak, 2) Responses are driven from non-AES afferents, and 3) The visual inputs have a marginal spatial tuning. Sensory experience was modelled by repeatedly presenting modality-specific and cross-modal stimuli. Synapses in the network were modified by simple Hebbian learning rules. As a consequence of this exposure, 1) Receptive fields shrink and come into spatial register, and 2) SC neurons gained the adult characteristic integrative properties: enhancement, depression, and inverse effectiveness. Importantly, the unique architecture of the model guided the development so that integration became dependent on the relationship between the cortical input and the SC. Manipulations of the statistics of the experience during the development changed the integrative profiles of the neurons, and results matched well with the results of physiological studies.
doi:10.1007/s00422-012-0511-9
PMCID: PMC3552306  PMID: 23011260
Visual-acoustic neurons; Anterior ectosylvian sulcus; Enhancement; Hebb rule Learning mechanisms; Inverse effectiveness principle; Neural network modeling
24.  Action understanding and active inference 
Biological cybernetics  2011;104(1-2):137-160.
We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action–observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference.
doi:10.1007/s00422-011-0424-z
PMCID: PMC3491875  PMID: 21327826
Action–observation; Mirror-neuron system; Inference; Precision; Free-energy; Perception; Generative models; Predictive coding
25.  A computational model for the modulation of the prepulse inhibition of the acoustic startle reflex 
Biological cybernetics  2012;106(3):169-176.
The acoustic startle reflex (ASR), a defensive response, is a contraction of the skeletal and facial muscles in response to an abrupt, intense (>80 db) auditory stimulus, which has been extensively studied in rats and humans. Prepulse inhibition (PPI) of ASR is the normal suppression of the startle reflex when an intense stimulus is preceded by a weak non-starting pre-stimulus. PPI, a measure of sensory motor gating, is impaired in various neuropsychiatric disorders, including schizophrenia, and is modulated by cognitive and emotional contexts such as fear and attention. We have modeled the fear modulation of PPI of ASR based on its anatomical substrates and taking into account data from behaving rats and humans. The model replicates the principal features of both phenomena and predicts underlying neural mechanisms. In addition, the model yields testable predictions.
doi:10.1007/s00422-012-0485-7
PMCID: PMC3349350  PMID: 22526356
Acoustic startle reflex; Prepulse inhibition; Fear modulation; PPI computational model

Results 1-25 (66)