256 research outputs found
PyNEST: A Convenient Interface to the NEST Simulator
The neural simulation tool NEST (http://www.nest-initiative.org) is a simulator for heterogeneous networks of point neurons or neurons with a small number of compartments. It aims at simulations of large neural systems with more than 104 neurons and 107 to 109 synapses. NEST is implemented in C++ and can be used on a large range of architectures from single-core laptops over multi-core desktop computers to super-computers with thousands of processor cores. Python (http://www.python.org) is a modern programming language that has recently received considerable attention in Computational Neuroscience. Python is easy to learn and has many extension modules for scientific computing (e.g. http://www.scipy.org). In this contribution we describe PyNEST, the new user interface to NEST. PyNEST combines NEST's efficient simulation kernel with the simplicity and flexibility of Python. Compared to NEST's native simulation language SLI, PyNEST makes it easier to set up simulations, generate stimuli, and analyze simulation results. We describe how PyNEST connects NEST and Python and how it is implemented. With a number of examples, we illustrate how it is used
Breaking Synchrony by Heterogeneity in Complex Networks
For networks of pulse-coupled oscillators with complex connectivity, we
demonstrate that in the presence of coupling heterogeneity precisely timed
periodic firing patterns replace the state of global synchrony that exists in
homogenous networks only. With increasing disorder, these patterns persist
until they reach a critical temporal extent that is of the order of the
interaction delay. For stronger disorder these patterns cease to exist and only
asynchronous, aperiodic states are observed. We derive self-consistency
equations to predict the precise temporal structure of a pattern from the
network heterogeneity. Moreover, we show how to design heterogenous coupling
architectures to create an arbitrary prescribed pattern.Comment: 4 pages, 3 figure
The Scientific Case for Brain Simulators
A key element of the European Union’s Human Brain Project (HBP) and other large-scale brain research projects is the simulation of large-scale model networks of neurons. Here, we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and why a set of brain simulators based on neuron models at different levels of biological detail should therefore be developed. To allow for systematic refinement of candidate network models by comparison with experiments, the simulations should be multimodal in the sense that they should predict not only action potentials, but also electric, magnetic, and optical signals measured at the population and system levels
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Theory of Interaction of Memory Patterns in Layered Associative Networks
A synfire chain is a network that can generate repeated spike patterns with
millisecond precision. Although synfire chains with only one activity
propagation mode have been intensively analyzed with several neuron models,
those with several stable propagation modes have not been thoroughly
investigated. By using the leaky integrate-and-fire neuron model, we
constructed a layered associative network embedded with memory patterns. We
analyzed the network dynamics with the Fokker-Planck equation. First, we
addressed the stability of one memory pattern as a propagating spike volley. We
showed that memory patterns propagate as pulse packets. Second, we investigated
the activity when we activated two different memory patterns. Simultaneous
activation of two memory patterns with the same strength led the propagating
pattern to a mixed state. In contrast, when the activations had different
strengths, the pulse packet converged to a two-peak state. Finally, we studied
the effect of the preceding pulse packet on the following pulse packet. The
following pulse packet was modified from its original activated memory pattern,
and it converged to a two-peak state, mixed state or non-spike state depending
on the time interval
Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses
In this paper, we systematically investigate both the synfire propagation and
firing rate propagation in feedforward neuronal network coupled in an
all-to-all fashion. In contrast to most earlier work, where only reliable
synaptic connections are considered, we mainly examine the effects of
unreliable synapses on both types of neural activity propagation in this work.
We first study networks composed of purely excitatory neurons. Our results show
that both the successful transmission probability and excitatory synaptic
strength largely influence the propagation of these two types of neural
activities, and better tuning of these synaptic parameters makes the considered
network support stable signal propagation. It is also found that noise has
significant but different impacts on these two types of propagation. The
additive Gaussian white noise has the tendency to reduce the precision of the
synfire activity, whereas noise with appropriate intensity can enhance the
performance of firing rate propagation. Further simulations indicate that the
propagation dynamics of the considered neuronal network is not simply
determined by the average amount of received neurotransmitter for each neuron
in a time instant, but also largely influenced by the stochastic effect of
neurotransmitter release. Second, we compare our results with those obtained in
corresponding feedforward neuronal networks connected with reliable synapses
but in a random coupling fashion. We confirm that some differences can be
observed in these two different feedforward neuronal network models. Finally,
we study the signal propagation in feedforward neuronal networks consisting of
both excitatory and inhibitory neurons, and demonstrate that inhibition also
plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience
(published
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