Package 'pro'

Title: Point-Process Response Model for Optogenetics
Description: Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments. This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process. Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
Authors: Xi (Rossi) LUO with contributions from Dylan Small and Vikaas Sohal
Maintainer: Xi (Rossi) LUO <[email protected]>
License: GPL-2
Version: 0.1.1
Built: 2024-11-11 03:43:20 UTC
Source: https://github.com/rluo/pro

Help Index

Model matrix for point-process responses

Description

Constructs a data.frame to be fitted using pro. Reference: X Luo, S Gee, V Sohal, D Small (In Press). A Point-process Response Model for Optogenetics Experiments on Neural Circuits. _Statistics in Medicine_.

Usage

model.pro(spike, flash, fixed = NULL, kv = F)

Arguments

spike

A binary vector represents spiking (1) or no spiking (0).
flash

A binary vector of the same length of spike, 1 for flashing and 0 for non-flashing.
fixed

Whether a fixed time window of spike/flash history should be used. If it is NULL, a varying time window of history will be used as described in the reference. If it is a integer j, a fixed window from index t-j to t will be used.
kv

Whether the history dependence model in Kass and Ventura (2001) (A Spike-Train Probability Model, Neural Computation 13, 1713-1720) should be employed. This differs from the history dependence model in the reference.

Value

a data.frame of the three response functions (PF, CF, SF) and other intermediate functions (for future modeling use).

Examples

n <- 500
set.seed(100)
re <- sim.lif(n, rbinom(n, 1, 0.14), 7, 3)
d <- model.pro(re$sbin, re$I)
d[1:10, ]

Fit the PRO model

Description

Fit the PRO model to data. Reference: X Luo, S Gee, V Sohal, D Small (In Press). A Point-process Response Model for Optogenetics Experiments on Neural Circuits. _Statistics in Medicine_.

Usage

pro(spike, flash, ...)

Arguments

spike

A binary vector represents spiking (1) or no spiking (0).
flash

A binary vector of the same length of spike, 1 for flashing and 0 for non-flashing.
...

Additional parameters, see model.pro.

Value

a glm object of the fitted PRO coefficients.

Examples

n <- 500
set.seed(100)
re <- sim.lif(n, rbinom(n, 1, 0.14), 7, 3)
fit.pro <- pro(re$sbin, re$I)
summary(fit.pro)

Simulate optogenetic stimulation on a leaky-integrate-fire neuron

Description

Simulate various kinds of neural measures (e.g. membrane potentials and spikes) from a LIF neuron.

Usage

sim.lif(n, I, C, R, Vth = 1, V0 = 0, bin = 5, dt = 0.05)

Arguments

n

Number of time bins. The total time is n times bin.
I

Input stimulus vector of length n.
C

Membrane capacitance of the simulated neuron.
R

Membrane resistance of the simulated neuron.
Vth

Membrane potential threshold for spiking.
V0

Membrane potential reset value after spiking.
bin

Time length for each time bin. Default 5 millisecond.
dt

Time length for each simulation step. Default 0.05 millisecond.

Value

a list of simulated neural spikes, optogenetic light flashes, and simulation parameters.

Examples

n<- 500
set.seed(100)
re <- sim.lif(n, rbinom(n, 1, 0.14), 7, 3)