Abstract:
:A study of a general central pattern generator (CPG) is carried out by means of a measure of the gain of information between the number of available topology configurations and the output rhythmic activity. The neurons of the CPG are chaotic Hindmarsh-Rose models that cooperate dynamically to generate either chaotic or regular spatiotemporal patterns. These model neurons are implemented by computer simulations and electronic circuits. Out of a random pool of input configurations, a small subset of them maximizes the gain of information. Two important characteristics of this subset are emphasized: (1) the most regular output activities are chosen, and (2) none of the selected input configurations are networks with open topology. These two principles are observed in living CPGs as well as in model CPGs that are the most efficient in controlling mechanical tasks, and they are evidence that the information-theoretical analysis can be an invaluable tool in searching for general properties of CPGs.
journal_name
Neural Computjournal_title
Neural computationauthors
Stiesberg GR,Reyes MB,Varona P,Pinto RD,Huerta Rdoi
10.1162/neco.2007.19.4.974subject
Has Abstractpub_date
2007-04-01 00:00:00pages
974-93issue
4eissn
0899-7667issn
1530-888Xjournal_volume
19pub_type
杂志文章abstract::We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00829
更新日期:2016-05-01 00:00:00
abstract::The statistical dependencies that independent component analysis (ICA) cannot remove often provide rich information beyond the linear independent components. It would thus be very useful to estimate the dependency structure from data. While such models have been proposed, they have usually concentrated on higher-order...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01006
更新日期:2017-11-01 00:00:00
abstract::Complexity of one-hidden-layer networks is studied using tools from nonlinear approximation and integration theory. For functions with suitable integral representations in the form of networks with infinitely many hidden units, upper bounds are derived on the speed of decrease of approximation error as the number of n...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.04-08-745
更新日期:2009-10-01 00:00:00
abstract::We investigate the approximation ability of a multilayer perceptron (MLP) network when it is extended to the complex domain. The main challenge for processing complex data with neural networks has been the lack of bounded and analytic complex nonlinear activation functions in the complex domain, as stated by Liouville...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321891846
更新日期:2003-07-01 00:00:00
abstract::We considered a gamma distribution of interspike intervals as a statistical model for neuronal spike generation. A gamma distribution is a natural extension of the Poisson process taking the effect of a refractory period into account. The model is specified by two parameters: a time-dependent firing rate and a shape p...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2006.18.10.2359
更新日期:2006-10-01 00:00:00
abstract::Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by spiking neurons, where the spiking rules are usually used in a sequential way (an applicable rule is applied one time at a step) or an exhaustive way (an applicable rule is applied as many times as possible at a s...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00665
更新日期:2014-12-01 00:00:00
abstract::Modeling stereo transparency with physiologically plausible mechanisms is challenging because in such frameworks, large receptive fields mix up overlapping disparities, whereas small receptive fields can reliably compute only small disparities. It seems necessary to combine information across scales. A coarse-to-fine ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00722
更新日期:2015-05-01 00:00:00
abstract::A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a s...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01021
更新日期:2017-12-01 00:00:00
abstract::Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attracto...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1996.8.6.1135
更新日期:1996-08-15 00:00:00
abstract::We consider the effect of the effective timing of a delayed feedback on the excitatory neuron in a recurrent inhibitory loop, when biological realities of firing and absolute refractory period are incorporated into a phenomenological spiking linear or quadratic integrate-and-fire neuron model. We show that such models...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.8.2124
更新日期:2007-08-01 00:00:00
abstract::Disparity tuning of visual cells in the brain depends on the structure of their binocular receptive fields (RFs). Freeman and coworkers have found that binocular RFs of a typical simple cell can be quantitatively described by two Gabor functions with the same gaussian envelope but different phase parameters in the sin...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1996.8.8.1611
更新日期:1996-11-15 00:00:00
abstract::Recent experiments indicate that the calcium store (e.g., endoplasmic reticulum) is involved in electrical bursting and [Ca2+]i oscillation in bursting neuronal cells. In this paper, we formulate a mathematical model for bursting neurons, which includes Ca2+ in the intracellular Ca2+ stores and a voltage-independent c...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1996.8.5.951
更新日期:1996-07-01 00:00:00
abstract::The pyloric network of the stomatogastric ganglion in crustacea is a central pattern generator that can produce the same basic rhythm over a wide frequency range. Three electrically coupled neurons, the anterior burster (AB) neuron and two pyloric dilator (PD) neurons, act as a pacemaker unit for the pyloric network. ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1991.3.4.487
更新日期:1991-01-01 00:00:00
abstract::Recently there has been great interest in sparse representations of signals under the assumption that signals (data sets) can be well approximated by a linear combination of few elements of a known basis (dictionary). Many algorithms have been developed to find such representations for one-dimensional signals (vectors...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00385
更新日期:2013-01-01 00:00:00
abstract::We show how a Hopfield network with modifiable recurrent connections undergoing slow Hebbian learning can extract the underlying geometry of an input space. First, we use a slow and fast analysis to derive an averaged system whose dynamics derives from an energy function and therefore always converges to equilibrium p...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00322
更新日期:2012-09-01 00:00:00
abstract::We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing stra...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01060
更新日期:2018-04-01 00:00:00
abstract::Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.05-07-525
更新日期:2008-09-01 00:00:00
abstract::Generalized discriminant analysis (GDA) is an extension of the classical linear discriminant analysis (LDA) from linear domain to a nonlinear domain via the kernel trick. However, in the previous algorithm of GDA, the solutions may suffer from the degenerate eigenvalue problem (i.e., several eigenvectors with the same...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976604773717612
更新日期:2004-06-01 00:00:00
abstract::The richness and complexity of recurrent cortical circuits is an inexhaustible source of inspiration for thinking about high-level biological computation. In past theoretical studies, constraints on the synaptic connection patterns of threshold-linear networks were found that guaranteed bounded network dynamics, conve...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321192103
更新日期:2003-03-01 00:00:00
abstract::Experimental studies of reasoning and planned behavior have provided evidence that nervous systems use internal models to perform predictive motor control, imagery, inference, and planning. Classical (model-free) reinforcement learning approaches omit such a model; standard sensorimotor models account for forward and ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976606776240995
更新日期:2006-05-01 00:00:00
abstract::The hypothesis of invariant maximization of interaction (IMI) is formulated within the setting of random fields. According to this hypothesis, learning processes maximize the stochastic interaction of the neurons subject to constraints. We consider the extrinsic constraint in terms of a fixed input distribution on the...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602760805368
更新日期:2002-12-01 00:00:00
abstract::Nondeclarative memory and novelty processing in the brain is an actively studied field of neuroscience, and reducing neural activity with repetition of a stimulus (repetition suppression) is a commonly observed phenomenon. Recent findings of an opposite trend-specifically, rising activity for unfamiliar stimuli-questi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00569
更新日期:2014-04-01 00:00:00
abstract::Pharmacologically isolated GABAergic irregular spiking and stuttering interneurons in the mouse visual cortex display highly irregular spike times, with high coefficients of variation approximately 0.9-3, in response to a depolarizing, constant current input. This is in marked contrast to cortical pyramidal cells, whi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.20.1.44
更新日期:2008-01-01 00:00:00
abstract::A mathematical theory of interacting hypercolumns in primary visual cortex (V1) is presented that incorporates details concerning the anisotropic nature of long-range lateral connections. Each hypercolumn is modeled as a ring of interacting excitatory and inhibitory neural populations with orientation preferences over...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602317250870
更新日期:2002-03-01 00:00:00
abstract::For any memoryless communication channel with a binary-valued input and a one-dimensional real-valued output, we introduce a probabilistic lower bound on the mutual information given empirical observations on the channel. The bound is built on the Dvoretzky-Kiefer-Wolfowitz inequality and is distribution free. A quadr...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00144
更新日期:2011-07-01 00:00:00
abstract::Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testin...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01129
更新日期:2018-11-01 00:00:00
abstract::Changes in GABA modulation may underlie experimentally observed changes in the strength of synaptic transmission at different phases of the theta rhythm (Wyble, Linster, & Hasselmo, 1997). Analysis demonstrates that these changes improve sequence disambiguation by a neural network model of CA3. We show that in the fra...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976698300017539
更新日期:1998-05-15 00:00:00
abstract::Synchronized firings in the networks of class 1 excitable neurons with excitatory and inhibitory connections are investigated, and their dependences on the forms of interactions are analyzed. As the forms of interactions, we treat the double exponential coupling and the interactions derived from it: pulse coupling, ex...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766053630387
更新日期:2005-06-01 00:00:00
abstract::We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is giv...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2010.05-09-1010
更新日期:2010-07-01 00:00:00
abstract::We address the problem of detecting the presence of a recurring stimulus by monitoring the voltage on a multiunit electrode located in a brain region densely populated by stimulus reactive neurons. Published experimental results suggest that under these conditions, when a stimulus is present, the measurements are gaus...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00257
更新日期:2012-04-01 00:00:00