1,475 research outputs found
Analysis of data systems requirements for global crop production forecasting in the 1985 time frame
Data systems concepts that would be needed to implement the objective of the global crop production forecasting in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data system requirements, and the influence of these requirements on the formulation of a conceptual data system was analyzed. Any potential problem areas in meeting these data system requirements were identified in an iterative process
Nonnormal amplification in random balanced neuronal networks
In dynamical models of cortical networks, the recurrent connectivity can
amplify the input given to the network in two distinct ways. One is induced by
the presence of near-critical eigenvalues in the connectivity matrix W,
producing large but slow activity fluctuations along the corresponding
eigenvectors (dynamical slowing). The other relies on W being nonnormal, which
allows the network activity to make large but fast excursions along specific
directions. Here we investigate the tradeoff between nonnormal amplification
and dynamical slowing in the spontaneous activity of large random neuronal
networks composed of excitatory and inhibitory neurons. We use a Schur
decomposition of W to separate the two amplification mechanisms. Assuming
linear stochastic dynamics, we derive an exact expression for the expected
amount of purely nonnormal amplification. We find that amplification is very
limited if dynamical slowing must be kept weak. We conclude that, to achieve
strong transient amplification with little slowing, the connectivity must be
structured. We show that unidirectional connections between neurons of the same
type together with reciprocal connections between neurons of different types,
allow for amplification already in the fast dynamical regime. Finally, our
results also shed light on the differences between balanced networks in which
inhibition exactly cancels excitation, and those where inhibition dominates.Comment: 13 pages, 7 figure
Extracting non-linear integrate-and-fire models from experimental data using dynamic IâV curves
The dynamic IâV curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional currentâvoltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron modelsâof the refractory exponential integrate-and-fire typeâprovide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons
SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo
The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work
Desynchronization in diluted neural networks
The dynamical behaviour of a weakly diluted fully-inhibitory network of
pulse-coupled spiking neurons is investigated. Upon increasing the coupling
strength, a transition from regular to stochastic-like regime is observed. In
the weak-coupling phase, a periodic dynamics is rapidly approached, with all
neurons firing with the same rate and mutually phase-locked. The
strong-coupling phase is characterized by an irregular pattern, even though the
maximum Lyapunov exponent is negative. The paradox is solved by drawing an
analogy with the phenomenon of ``stable chaos'', i.e. by observing that the
stochastic-like behaviour is "limited" to a an exponentially long (with the
system size) transient. Remarkably, the transient dynamics turns out to be
stationary.Comment: 11 pages, 13 figures, submitted to Phys. Rev.
Stability of Negative Image Equilibria in Spike-Timing Dependent Plasticity
We investigate the stability of negative image equilibria in mean synaptic
weight dynamics governed by spike-timing dependent plasticity (STDP). The
neural architecture of the model is based on the electrosensory lateral line
lobe (ELL) of mormyrid electric fish, which forms a negative image of the
reafferent signal from the fish's own electric discharge to optimize detection
of external electric fields. We derive a necessary and sufficient condition for
stability, for arbitrary postsynaptic potential functions and arbitrary
learning rules. We then apply the general result to several examples of
biological interest.Comment: 13 pages, revtex4; uses packages: graphicx, subfigure; 9 figures, 16
subfigure
Population dynamics of spiking neurons: fast transients, asynchronous states and locking
An integral equation describing the time evolution of the population activity in a homogeneous pool of spiking neurons of the integrate-and-fire type is discussed. It is analytically shown that transients from a state of incoherent firing can be immediate. The stability of incoherent firing is analyzed in terms of the noise level and transmission delay, and a bifurcation diagram is derived. The response of a population of noisy integrate-and-fire neurons to an input current of small amplitude is calculated and characterized by a linear filter L. The stability of perfectly synchronized âlockedâ solutions is analyzed
Coding properties of spiking neurons: reverse- and cross-correlations
What is the `meaning' of a single spike? Spike-triggered averaging (`reverse correlations') yields the typical input just before a spike. Similarly, cross-correlations describe the probability of firing an output spike given (one additional) presynaptic input spike. In this paper we analytically calculate reverse and cross-correlations for a spiking neuron model with escape noise. The influence of neuronal parameters (such as the membrane time constant, the noise level, and the mean firing rate) on the form of the correlation function is illustrated. The calculation is done in the framework of a population theory that is reviewed. The relation of the population activity equations to population density methods is discussed. Finally we indicate the role of cross-correlations in spike-time dependent Hebbian plasticity
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