Abstract:
:Although the commonly used quadratic Hebbian-anti-Hebbian rules lead to successful models of plasticity and learning, they are inconsistent with neurophysiology. Other rules, more physiologically plausible, fail to specify the biological mechanism of bidirectionality and the biological mechanism that prevents synapses from changing from excitatory to inhibitory, and vice versa. We developed a synaptic bidirectional Hebbian rule that does not suffer from these problems. This rule was compared with physiological homosynaptic conditions in the hippocampus, with the results indicating the consistency of this rule with long-term potentiation (LTP) and long-term depression (LTD) phenomenologies. The phenomenologies considered included the reversible dynamics of LTP and LTD and the effects of N-methyl-D-aspartate blockers and phosphatase inhibitors.
journal_name
Neural Computjournal_title
Neural computationauthors
Grzywacz NM,Burgi PYdoi
10.1162/089976698300017629subject
Has Abstractpub_date
1998-04-01 00:00:00pages
499-520issue
3eissn
0899-7667issn
1530-888Xjournal_volume
10pub_type
杂志文章abstract::Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specifica...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00198
更新日期:2011-11-01 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::In this work, we propose a two-layered descriptive model for motion processing from retina to the cortex, with an event-based input from the asynchronous time-based image sensor (ATIS) camera. Spatial and spatiotemporal filtering of visual scenes by motion energy detectors has been implemented in two steps in a simple...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01191
更新日期:2019-06-01 00:00:00
abstract::Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00917
更新日期:2017-02-01 00:00:00
abstract::In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660260293292
更新日期:2002-10-01 00:00:00
abstract::Natural gradient learning is known to be efficient in escaping plateau, which is a main cause of the slow learning speed of neural networks. The adaptive natural gradient learning method for practical implementation also has been developed, and its advantage in real-world problems has been confirmed. In this letter, w...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976604322742065
更新日期:2004-02-01 00:00:00
abstract::GABAergic synapse reversal potential is controlled by the concentration of chloride. This concentration can change significantly during development and as a function of neuronal activity. Thus, GABA inhibition can be hyperpolarizing, shunting, or partially depolarizing. Previous results pinpointed the conditions under...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.3.706
更新日期:2007-03-01 00:00:00
abstract::Cortical neurons of behaving animals generate irregular spike sequences. Recently, there has been a heated discussion about the origin of this irregularity. Softky and Koch (1993) pointed out the inability of standard single-neuron models to reproduce the irregularity of the observed spike sequences when the model par...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016511
更新日期:1999-05-15 00:00:00
abstract::We present an integrative formalism of mutual information expansion, the general Poisson exact breakdown, which explicitly evaluates the informational contribution of correlations in the spike counts both between and within neurons. The formalism was validated on simulated data and applied to real neurons recorded fro...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2010.04-09-989
更新日期:2010-06-01 00:00:00
abstract::We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01286
更新日期:2020-07-01 00:00:00
abstract::The Nyström method is a well-known sampling-based technique for approximating the eigensystem of large kernel matrices. However, the chosen samples in the Nyström method are all assumed to be of equal importance, which deviates from the integral equation that defines the kernel eigenfunctions. Motivated by this observ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.11-07-651
更新日期:2009-01-01 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::We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behav...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.10.2739
更新日期:2007-10-01 00:00:00
abstract::In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The re...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016287
更新日期:1999-08-15 00:00:00
abstract::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 the absence of noise, the class of positive neural networks captures the so-call...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00789
更新日期:2015-12-01 00:00:00
abstract::Event-driven simulation strategies were proposed recently to simulate integrate-and-fire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating large-scale networks of neurons; most important, such approaches are more precise than traditional clock-driven numerical ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2006.18.9.2146
更新日期:2006-09-01 00:00:00
abstract::Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concen...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00906
更新日期:2017-01-01 00:00:00
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 o...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.4.974
更新日期:2007-04-01 00:00:00
abstract::The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Pr...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco_a_01229
更新日期:2019-11-01 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::The mutual information between a set of stimuli and the elicited neural responses is compared to the corresponding decoded information. The decoding procedure is presented as an artificial distortion of the joint probabilities between stimuli and responses. The information loss is quantified. Whenever the probabilitie...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602317318947
更新日期:2002-04-01 00:00:00
abstract::The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow fea...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00214
更新日期:2011-12-01 00:00:00
abstract::The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for model...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco_a_01275
更新日期:2020-05-01 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::The ability to achieve high swimming speed and efficiency is very important to both the real lamprey and its robotic implementation. In previous studies, we used evolutionary algorithms to evolve biologically plausible connectionist swimming controllers for a simulated lamprey. This letter investigates the robustness ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.6.1568
更新日期:2007-06-01 00:00:00
abstract::Integrate-and-express models of synaptic plasticity propose that synapses integrate plasticity induction signals before expressing synaptic plasticity. By discerning trends in their induction signals, synapses can control destabilizing fluctuations in synaptic strength. In a feedforward perceptron framework with binar...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00889
更新日期:2016-11-01 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
abstract::We present a reduction of a Hodgkin-Huxley (HH)--style bursting model to a hybridized integrate-and-fire (IF) formalism based on a thorough bifurcation analysis of the neuron's dynamics. The model incorporates HH--style equations to evolve the subthreshold currents and includes IF mechanisms to characterize spike even...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603322518768
更新日期:2003-12-01 00:00:00
abstract::Neural networks are often employed as tools in classification tasks. The use of large networks increases the likelihood of the task's being learned, although it may also lead to increased complexity. Pruning is an effective way of reducing the complexity of large networks. We present discriminant components pruning (D...
journal_title:Neural computation
pub_type: 杂志文章,评审
doi:10.1162/089976699300016665
更新日期:1999-04-01 00:00:00
abstract::In this letter, we investigate the impact of choosing different loss functions from the viewpoint of statistical learning theory. We introduce a convexity assumption, which is met by all loss functions commonly used in the literature, and study how the bound on the estimation error changes with the loss. We also deriv...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976604773135104
更新日期:2004-05-01 00:00:00