Аннотация
A paradigm for constructing and analyzing non-Poisson stimulus-response
models of neural spike train activity is presented. Inhomogeneous
gamma (IG) and inverse Gaussian (IIG) probability models are constructed
by generalizing the derivation of the inhomogeneous Poisson (IP)
model from the exponential probability density. The resultant spike
train models have Markov dependence. Quantile-quantile (Q-Q) plots
and Kolmogorov-Smirnov (K-S) plots are developed based on the rate-rescaling
theorem to assess model goodness-of-fit. The analysis also expresses
the spike rate function of the neuron directly in terms of its interspike
interval (ISI) distribution. The methods are illustrated with an
analysis of 34 spike trains from rat CA1 hippocampal pyramidal neurons
recorded while the animal executed a behavioral task. The stimulus
in these experiments is the animal's position in its environment
and the response is the neural spiking activity. For all 34 pyramidal
cells, the IG and IIG models gave better fits to the spike trains
than the IP. The IG model more accurately described the frequency
of longer ISIs, whereas the IIG model gave the best description of
the burst frequency, i.e. ISIs < or = 20 ms. The findings suggest
that bursts are a significant component of place cell spiking activity
even when position and the background variable, theta phase, are
taken into account. Unlike the Poisson model, the spatial and temporal
rate maps of the IG and IIG models depend directly on the spiking
history of the neurons. These rate maps are more physiologically
plausible since the interaction between space and time determines
local spiking propensity. While this statistical paradigm is being
developed to study information encoding by rat hippocampal neurons,
the framework should be applicable to stimulus-response experiments
performed in other neural systems.
- 11166363
- action
- animals,
- behavior,
- cells,
- chains,
- computer-assisted,
- distribution,
- exploratory
- factors,
- hippocampus,
- ical,
- long-evans,
- markov
- models,
- neurolog,
- nonparametric,
- normal
- perception,
- poisson
- potentials,
- processing,
- pyramidal
- rats,
- reaction
- signal
- space
- statistics,
- time
- time,
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