Örjan Ekeberg

Neural networks that co-ordinate locomotion and body orientation in lamprey

The networks of the brainstem and spinal cord that co-ordinate locomotion and body orientation in lamprey are described. The cycle-to-cycle pattern generation of these networks is produced by interacting glutamatergic and glycinergic neurones, with NMDA receptor-channels playing an important role at lower rates of locomotion. The fine tuning of the networks produced by 5-HT, dopamine and GABA systems involves a modulation of Ca2+-dependent K+ channels, high- and low-threshold voltage-activated Ca2+ channels and presynaptic inhibitory mechanisms. Mathematical modelling has been used to explore the capacity of these biological networks. The vestibular control of the body orientation during swimming is exerted via reticulospinal neurones located in different reticular nuclei. These neurones become activated maximally at different angles of tilt.

A combined neuronal and mechanical model of fish swimming

A simulated neural network has been connected to a simulated mechanical environment. The network is based on a model of the spinal central pattern generator producing rhythmic swimming movements in the lamprey and the model is similar to that used in earlier simulations of fictive swimming. Here, the network has been extended with a model of how motoneuron activity is transformed via the muscles to mechanical forces. Further, these forces are used in a two-dimensional mechanical model including interaction with the surrounding water, giving the movements of the different parts of the body. Finally, these movements are fed back through stretch receptors interacting with the central pattern generator. The combined model provides a platform for various simulation experiments relating the currently known neural properties and connectivity to the behavior of the animal in vivo. By varying a small set of parameters, corresponding to brainstem input to the spinal network, a variety of basic locomotor behaviors, like swimming at different speeds and turning can be produced. This paper describes the combined model and its basic properties.

Intrinsic function of a neuronal network — a vertebrate central pattern generator

The cellular bases of vertebrate locomotor behaviour is reviewed using the lamprey as a model system. Forebrain and brainstem cell populations initiate locomotor activity via reticulospinal fibers activating a spinal network comprised of glutamatergic and glycinergic interneurons. The role of different subtypes of Ca2+ channels, Ca2+ dependent K+ channels and voltage dependent NMDA channels at the neuronal and network level is in focus as well as the effects of different metabotropic, aminergic and peptidergic modulators that target these ion channels. This is one of the few vertebrate networks that is understood at a cellular level.

A computer based model for realistic simulations of neural networks

The use of computer simulations as a neurophysiological tool creates new possibilities to understand complex systems and to test whether a given model can explain experimental findings. Simulations, however, require a detailed specification of the model, including the nerve cell action potential and synaptic transmission. We describe a neuron model of intermediate complexity, with a small number of compartments representing the soma and the dendritic tree, and equipped with Na+, K+, Ca2+, and Ca2+ dependent K+ channels. Conductance changes in the different compartments are used to model conventional excitatory and inhibitory synaptic interactions. Voltage dependent NMDA-receptor channels are also included, and influence both the electrical conductance and the inflow of Ca2+ ions. This neuron model has been designed for the analysis of neural networks and specifically for the simulation of the network generating locomotion in a simple vertebrate, the lamprey. By assigning experimentally established properties to the simulated cells and their synapses, it has been possible to verify the sufficiency of these properties to account for a number of experimental findings of the network in operation. The model is, however, sufficiently general to be useful for realistic simulation also of other neural systems.

Assessing sensory function in locomotor systems using neuro-mechanical simulations

Computer simulations are being used increasingly to gain an understanding of the complex interactions between the neuronal, sensory, muscular and mechanical components of locomotor systems. Recent neuro-mechanical simulations of walking in humans, cats and insects, and of swimming in lampreys, have provided new information on the functional role of specific groups of sensory receptors in regulating locomotion. As we discuss in this review, these studies also make it clear that a full understanding of the neural and mechanical mechanisms that underlie locomotion can be achieved only by using simulations in parallel with physiological investigations. The widespread implementation of this approach would be enhanced by the development of freely available and easy-to-use software tools.

Large-Scale Modeling – a Tool for Conquering the Complexity of the Brain

Is there any hope of achieving a thorough understanding of higher functions such as perception, memory, thought and emotion or is the stunning complexity of the brain a barrier which will limit such efforts for the foreseeable future? In this perspective we discuss methods to handle complexity, approaches to model building, and point to detailed large-scale models as a new contribution to the toolbox of the computational neuroscientist. We elucidate some aspects which distinguishes large-scale models and some of the technological challenges which they entail.

The Brain in Its Body: Motor Control and Sensing in a Biomechanical Context

Although it is widely recognized that adaptive behavior emerges from the ongoing interactions among the nervous system, the body, and the environment, it has only become possible in recent years to experimentally study and to simulate these interacting systems. We briefly review work on molluscan feeding, maintenance of postural control in cats and humans, simulations of locomotion in lamprey, insect, cat and salamander, and active vibrissal sensing in rats to illustrate the insights that can be derived from studies of neural control and sensing within a biomechanical context. These studies illustrate that control may be shared between the nervous system and the periphery, that neural activity organizes degrees of freedom into biomechanically meaningful subsets, that mechanics alone may play crucial roles in enforcing gait patterns, and that mechanics of sensors is crucial for their function.

Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer

Biologically detailed large-scale models of the brain can now be simulated thanks to increasingly powerful massively parallel supercomputers. We present an overview, for the general technical reader, of a neuronal network model of layers II/III of the neocortex built with biophysical model neurons. These simulations, carried out on an IBM Blue Gene/L™ supercomputer, comprise up to 22 million neurons and 11 billion synapses, which makes them the largest simulations of this type ever performed. Such model sizes correspond to the cortex of a small mammal. The SPLIT library, used for these simulations, runs on single-processor as well as massively parallel machines. Performance measurements show good scaling behavior on the Blue Gene/L supercomputer up to 8,192 processors. Several key phenomena seen in the living brain appear as emergent phenomena in the simulations. We discuss the role of this kind of model in neuroscience and note that full-scale models may be necessary to preserve natural dynamics. We also discuss the need for software tools for the specification of models as well as for analysis and visualization of output data. Combining models that range from abstract connectionist type to biophysically detailed will help us unravel the basic principles underlying neocortical function.

A ONE-LAYER FEEDBACK ARTIFICIAL NEURAL NETWORK WITH A BAYESIAN LEARNING RULE

A probabilistic artificial neural network is presented. It is of a one-layer, feedback-coupled type with graded units. The learning rule is derived from Bayess rule. Learning is regarded as collecting statistics and recall as a statistical inference process. Units correspond to events and connections come out as compatibility coefficients in a logarithmic combination rule. The input to a unit via connections from other active units affects the a posteriori belief in the event in question. The new model is compared to an earlier binary model with respect to storage capacity, noise tolerance, etc. in a content addressable memory (CAM) task. The new model is a real time network and some results on the reaction time for associative recall are given. The scaling of learning and relaxation operations is considered together with issues related to representation of information in one-layer artificial neural networks. An extension with complex units is discussed.

Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework

MUSIC is a standard API allowing large scale neuron simulators to exchange data within a parallel computer during runtime. A pilot implementation of this API has been released as open source. We provide experiences from the implementation of MUSIC interfaces for two neuronal network simulators of different kinds, NEST and MOOSE. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. Benchmarks show that the MUSIC pilot implementation provides efficient data transfer in a cluster computer with good scaling. We conclude that MUSIC fulfills the design goal that it should be simple to adapt existing simulators to use MUSIC. In addition, since the MUSIC API enforces independence of the applications, the multi-simulation could be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.