Author name cluster

K. Wong

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

NeurIPS Conference 2012 Conference Paper

Delay Compensation with Dynamical Synapses

Chi Fung
K. Wong
Si Wu

Time delay is pervasive in neural information processing. To achieve real-time tracking, it is critical to compensate the transmission and processing delays in a neural system. In the present study we show that dynamical synapses with short-term depression can enhance the mobility of a continuous attractor network to the extent that the system tracks time-varying stimuli in a timely manner. The state of the network can either track the instantaneous position of a moving stimulus perfectly (with zero-lag) or lead it with an effectively constant time, in agreement with experiments on the head-direction systems in rodents. The parameter regions for delayed, perfect and anticipative tracking correspond to network states that are static, ready-to-move and spontaneously moving, respectively, demonstrating the strong correlation between tracking performance and the intrinsic dynamics of the network. We also find that when the speed of the stimulus coincides with the natural speed of the network state, the delay becomes effectively independent of the stimulus amplitude.

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NeurIPS Conference 2010 Conference Paper

Attractor Dynamics with Synaptic Depression

K. Wong
He Wang
Si Wu
Chi Fung

Neuronal connection weights exhibit short-term depression (STD). The present study investigates the impact of STD on the dynamics of a continuous attractor neural network (CANN) and its potential roles in neural information processing. We find that the network with STD can generate both static and traveling bumps, and STD enhances the performance of the network in tracking external inputs. In particular, we find that STD endows the network with slow-decaying plateau behaviors, namely, the network being initially stimulated to an active state will decay to silence very slowly in the time scale of STD rather than that of neural signaling. We argue that this provides a mechanism for neural systems to hold short-term memory easily and shut off persistent activities naturally.

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JAIR Journal 2008 Journal Article

Sound and Complete Inference Rules for SE-Consequence

K. Wong

The notion of strong equivalence on logic programs with answer set semantics gives rise to a consequence relation on logic program rules, called SE-consequence. We present a sound and complete set of inference rules for SE-consequence on disjunctive logic programs.

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NeurIPS Conference 2008 Conference Paper

Tracking Changing Stimuli in Continuous Attractor Neural Networks

K. Wong
Si Wu
Chi Fung

Continuous attractor neural networks (CANNs) are emerging as promising models for describing the encoding of continuous stimuli in neural systems. Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of neutrally stable states. In this study, we systematically explore how neutral stability of a CANN facilitates its tracking performance, a capacity believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.

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NeurIPS Conference 2004 Conference Paper

Multi-agent Cooperation in Diverse Population Games

K. Wong
S. Lim
Z. Gao

We consider multi-agent systems whose agents compete for resources by striving to be in the minority group. The agents adapt to the environment by reinforcement learning of the preferences of the policies they hold. Diversity of preferences of policies is introduced by adding random bi- ases to the initial cumulative payoffs of their policies. We explain and provide evidence that agent cooperation becomes increasingly important when diversity increases. Analyses of these mechanisms yield excellent agreement with simulations over nine decades of data.

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NeurIPS Conference 2002 Conference Paper

Mean Field Approach to a Probabilistic Model in Information Retrieval

Bin Wu
K. Wong
David Bodoff

We study an explicit parametric model of documents, queries, and rel- evancy assessment for Information Retrieval (IR). Mean-ﬁeld methods are applied to analyze the model and derive efﬁcient practical algorithms to estimate the parameters in the problem. The hyperparameters are es- timated by a fast approximate leave-one-out cross-validation procedure based on the cavity method. The algorithm is further evaluated on several benchmark databases by comparing with standard algorithms in IR.

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NeurIPS Conference 2001 Conference Paper

Fast Parameter Estimation Using Green's Functions

K. Wong
F. Li

We propose a method for the fast estimation of hyperparameters in large networks, based on the linear response relation in the cav(cid: 173) ity method, and an empirical measurement of the Green's func(cid: 173) tion. Simulation results show that it is efficient and precise, when compared with cross-validation and other techniques which require matrix inversion.

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