Andreas Knoblauch
Andreas Knoblauch
Neural associative networks are a promising computational paradigm for both modeling neural circuits of the brain and implementing associative memory and Hebbian cell assemblies in parallel VLSI or nanoscale hardware. Previous work has exte...
Anoop Korattikara,Yutian Chen,Max Welling
Anoop Korattikara
We argue that when faced with big data sets, learning and inference algorithms should compute updates using only subsets of data items. We introduce algorithms that use sequential hypothesis tests to adaptively select such a subset of data ...
Global Exponential Stability of Cohen-Grossberg Neural Networks with Piecewise Constant Argument of Generalized Type and Impulses [0.03%]
广义分段常数论证和脉冲的科恩-格罗斯伯格神经网络的整体指数稳定性
Qiang Xi
Qiang Xi
In this letter, we consider a model of Cohen-Grossberg neural networks with piecewise constant argument of generalized type and impulses. Sufficient conditions ensuring the existence and uniqueness of solutions are obtained. Based on constr...
Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling [0.03%]
周期强迫抑制稳定网络：非线性共振和相幅耦合
Romain Veltz,Terrence J Sejnowski
Romain Veltz
Inhibition-stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from inhibitory interneurons, a circuit element found in the hippocampus and the prima...
A Sparse Reformulation of the Green's Function Formalism Allows Efficient Simulations of Morphological Neuron Models [0.03%]
格林函数形式的稀疏重构可实现形态神经元模型的有效模拟
Willem A M Wybo,Daniele Boccalini,Benjamin Torben-Nielsen et al.
Willem A M Wybo et al.
We prove that when a class of partial differential equations, generalized from the cable equation, is defined on tree graphs and the inputs are restricted to a spatially discrete, well chosen set of points, the Green's function (GF) formali...
Rhythmic Inhibition Allows Neural Networks to Search for Maximally Consistent States [0.03%]
节奏性抑制使神经网络能够搜寻一致性最大化的状态
Hesham Mostafa,Lorenz K Müller,Giacomo Indiveri
Hesham Mostafa
Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits, yet its computational role remains elusive. We show that a model of gamma-band rhythmic inhibition allows networks of coupled cortical circuit motifs to search fo...
Symbolic Computation Using Cellular Automata-Based Hyperdimensional Computing [0.03%]
基于元胞自动机的超维度计算的符号计算方法研究
Ozgur Yilmaz
Ozgur Yilmaz
This letter introduces a novel framework of reservoir computing that is capable of both connectionist machine intelligence and symbolic computation. A cellular automaton is used as the reservoir of dynamical systems. Input is randomly proje...
Positive Neural Networks in Discrete Time Implement Monotone-Regular Behaviors [0.03%]
离散时间正神经网络实现单调有规律的行为
Tom J Ameloot,Jan Van den Bussche
Tom J Ameloot
We study the expressive power of positive neural networks. The model uses positive connection weights and multiple input neurons. Different behaviors can be expressed by varying the connection weights. We show that in discrete time and in t...
Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks [0.03%]
结构化脉冲神经网络中学习时空编码的模式变换
Brian Gardner,Ioana Sporea,André Grüning
Brian Gardner
Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question...
STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony [0.03%]
基于神经元的通用计算框架STICK：脉冲时间间隔计算核及延迟与同步作用
Xavier Lagorce,Ryad Benosman
Xavier Lagorce
There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to p...