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Which measure should we use for unsupervised spike train learning?
"... In certain experimental paradigms, the dynamics of a neural system may not be fully determined by external stimuli because the neural activity depends on internal states from a wide range of possible causes. For example, bistable dynamics of a single neuron has been observed in vitro via frozen nois ..."
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In certain experimental paradigms, the dynamics of a neural system may not be fully determined by external stimuli because the neural activity depends on internal states from a wide range of possible causes. For example, bistable dynamics of a single neuron has been observed in vitro via frozen noise injection [1], local field potentials and EEG phase often correlates with response strength, and top-down control such as attention are known to affect responses. In other words, in these cases, it is nearly impossible to control or observe all the internal variables. Still, we would like to infer these internal states by analyzing the observation variability. The solution we propose is to use unsupervised learning methods, such as PCA and clustering [2, 3], to discover the internal states. Spike train measures Consider two spike trains si, sj ∈ S(T) defined in the interval T = [0, T]. We compare three inner product measures: • The memoryless cross-intensity (mCI) inner product is defined as I(si, sj) = T λsi
Summary
"... One of the fundamental difficulties in neural assembly studies is the lack of an effective, high resolution measure of the spatio-temporal structure in spike trains obtained from a single realization. In this chapter a systematic approach to estimate the cross-correlation (CC) of spike trains, over ..."
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One of the fundamental difficulties in neural assembly studies is the lack of an effective, high resolution measure of the spatio-temporal structure in spike trains obtained from a single realization. In this chapter a systematic approach to estimate the cross-correlation (CC) of spike trains, over time and in only one realization, is proposed. The solution lies in an alternate definition of cross-correlation which suggests that, rather than time averaging as is current practice, we should use ensemble averaging. This observation suggests a natural instantaneous CC estimator as required for high temporal resolution and real-time ensemble analysis and decoding. Results are shown in simulated datasets and neural activity of rat motor cortical neurons during a behavioral task. 1
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"... 1Kernel methods on spike train space for neuroscience: a tutorial ..."
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SPIKE TRAIN KERNELS FOR MULTIPLE NEURON RECORDINGS
, 2014
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
