Il Memming Park

Stony Brook University
0000-0002-4255-7750
http://catniplab.github.io/
Publications43
h-index13
Citations892
Highly Influential Citations67
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  • Publications
  • Influence
BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisy… Expand
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Encoding and decoding in parietal cortex during sensorimotor decision-making
It has been suggested that the lateral intraparietal area (LIP) of macaques plays a fundamental role in sensorimotor decision-making. We examined the neural code in LIP at the level of individual… Expand
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A comparison of binless spike train measures
TLDR
We present a systematic comparison of these measures in three simulated paradigms designed to address specific situations of interest in spike train analysis where the relevant feature may be in the form of firing rate, firing rate modulations, and/or synchrony. Expand
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An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters
TLDR
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters that can drastically reduce the time and space complexity without harming the performance. Expand
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Functional dissection of signal and noise in MT and LIP during decision-making
During perceptual decision-making, responses in the middle temporal (MT) and lateral intraparietal (LIP) areas appear to map onto theoretically defined quantities, with MT representing instantaneous… Expand
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Bayesian Spike-Triggered Covariance Analysis
TLDR
We describe an explicit model-based interpretation of traditional estimators for a neuron's multi-dimensional feature space, which allows for several important generalizations and extensions. Expand
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A Unified Framework for Quadratic Measures of Independence
TLDR
This paper proposes a unified framework for several available measures of independence by generalizing the concept of information theoretic learning and applying it in the context of linear independent component analysis (ICA). Expand
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Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences
TLDR
We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. Expand
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Intermittency Coding in the Primary Olfactory System: A Neural Substrate for Olfactory Scene Analysis
The spatial and temporal characteristics of the visual and acoustic sensory input are indispensable attributes for animals to perform scene analysis. In contrast, research in olfaction has focused… Expand
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Bayesian Efficient Coding
TLDR
We propose a general Bayesian theory of efficient coding that unites the efficient coding hypothesis and the Bayesian brain hypothesis and propose a framework for unifying them. Expand
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