211 research outputs found
Spike-Timing Dependent Plasticity and Feed-Forward Input Oscillations Produce Precise and Invariant Spike Phase-Locking
In the hippocampus and the neocortex, the coupling between local field potential (LFP) oscillations and the spiking of single neurons can be highly precise, across neuronal populations and cell types. Spike phase (i.e., the spike time with respect to a reference oscillation) is known to carry reliable information, both with phase-locking behavior and with more complex phase relationships, such as phase precession. How this precision is achieved by neuronal populations, whose membrane properties and total input may be quite heterogeneous, is nevertheless unknown. In this note, we investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks – the reliable, periodic modulation of presynaptic firing rates during oscillations, coupled with spike-timing dependent plasticity. When oscillations are within the biological range (2–150 Hz), firing rates of the inputs change on a timescale highly relevant to spike-timing dependent plasticity (STDP). Through analytic and computational methods, we find points of stable phase-locking for a neuron with plastic input synapses. These points correspond to precise phase-locking behavior in the feed-forward network. The location of these points depends on the oscillation frequency of the inputs, the STDP time constants, and the balance of potentiation and de-potentiation in the STDP rule. For a given input oscillation, the balance of potentiation and de-potentiation in the STDP rule is the critical parameter that determines the phase at which an output neuron will learn to spike. These findings are robust to changes in intrinsic post-synaptic properties. Finally, we discuss implications of this mechanism for stable learning of spike-timing in the hippocampus
On a link between Dirichlet kernels and central multinomial coefficients
AbstractThe central coefficients of powers of certain polynomials with arbitrary degree in x form an important family of integer sequences. Although various recursive equations addressing these coefficients do exist, no explicit analytic representation has yet been proposed. In this article, we present an explicit form of the integer sequences of central multinomial coefficients of polynomials of even degree in terms of finite sums over Dirichlet kernels, hence linking these sequences to discrete nth-degree Fourier series expansions. The approach utilizes the diagonalization of circulant Boolean matrices, and is generalizable to all multinomial coefficients of certain polynomials with even degree, thus forming the base for a new family of combinatorial identities
Multi-area organization of saccade-evoked traveling waves
In this project, we will employ new, large-scale electrophysiological recording techniques to sample widely across the visual system. It will allow us to test our hypothesis that neural traveling waves coordinated across multiple areas contribute to perceptual stability during eye movements. Using our newly developed signal processing technique to track traveling waves moment-by-moment in noisy multichannel data, we will detect and quantify them across multiple visual areas.https://ir.lib.uwo.ca/brainscanprojectsummaries/1040/thumbnail.jp
Theology, News and Notes - Vol. 41, No. 03
Theology News & Notes was a theological journal published by Fuller Theological Seminary from 1954 through 2014.https://digitalcommons.fuller.edu/tnn/1121/thumbnail.jp
Theory of transient chimeras in finite Sakaguchi-Kuramoto networks
Chimera states are a phenomenon in which order and disorder can co-exist
within a network that is fully homogeneous. Precisely how transient chimeras
emerge in finite networks of Kuramoto oscillators with phase-lag remains
unclear. Utilizing an operator-based framework to study nonlinear oscillator
networks at finite scale, we reveal the spatiotemporal impact of the adjacency
matrix eigenvectors on the Sakaguchi-Kuramoto dynamics. We identify a specific
condition for the emergence of transient chimeras in these finite networks: the
eigenvectors of the network adjacency matrix create a combination of a zero
phase-offset mode and low spatial frequency waves traveling in opposite
directions. This combination of eigenvectors leads directly to the coherent and
incoherent clusters in the chimera. This approach provides two specific
analytical predictions: (1) a precise formula predicting the combination of
connectivity and phase-lag that creates transient chimeras, (2) a mathematical
procedure for rewiring arbitrary networks to produce transient chimeras
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Advancing the size and complexity of neural network models leads to an ever
increasing demand for computational resources for their simulation.
Neuromorphic devices offer a number of advantages over conventional computing
architectures, such as high emulation speed or low power consumption, but this
usually comes at the price of reduced configurability and precision. In this
article, we investigate the consequences of several such factors that are
common to neuromorphic devices, more specifically limited hardware resources,
limited parameter configurability and parameter variations. Our final aim is to
provide an array of methods for coping with such inevitable distortion
mechanisms. As a platform for testing our proposed strategies, we use an
executable system specification (ESS) of the BrainScaleS neuromorphic system,
which has been designed as a universal emulation back-end for neuroscientific
modeling. We address the most essential limitations of this device in detail
and study their effects on three prototypical benchmark network models within a
well-defined, systematic workflow. For each network model, we start by defining
quantifiable functionality measures by which we then assess the effects of
typical hardware-specific distortion mechanisms, both in idealized software
simulations and on the ESS. For those effects that cause unacceptable
deviations from the original network dynamics, we suggest generic compensation
mechanisms and demonstrate their effectiveness. Both the suggested workflow and
the investigated compensation mechanisms are largely back-end independent and
do not require additional hardware configurability beyond the one required to
emulate the benchmark networks in the first place. We hereby provide a generic
methodological environment for configurable neuromorphic devices that are
targeted at emulating large-scale, functional neural networks
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