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Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models
Alison I Weber1, Jonathan W Pillow2
1Graduate Program in Neuroscience, University of Washington, Seattle, WA 98195, U.S.A. aiweber@uw.edu.
Neural Computation
|September 29, 2017
Summary
Generalized linear models (GLMs) in computational neuroscience can effectively mimic diverse neural firing patterns. These flexible Poisson GLMs capture complex behaviors, proving valuable for studying neurons.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Computational Biology
Background:
- A significant challenge in computational neuroscience is developing simplified models that accurately represent real neuron responses.
- Poisson generalized linear models (GLMs) are widely used for analyzing electrophysiological recordings due to their statistical properties.
- The dynamic capabilities of GLMs have not been thoroughly compared to the complex behaviors of biological neurons.
Purpose of the Study:
- To systematically evaluate the capacity of recurrent point process models, specifically Poisson GLMs, to replicate canonical neural response behaviors.
- To determine if GLMs can capture the nuanced dynamics and variability observed in real neuronal activity.
Main Methods:
- Utilized Poisson generalized linear models (GLMs) defined by linear filters and a point nonlinearity.
- Assessed the ability of these models to reproduce a range of known neural response patterns, including spiking dynamics, adaptation, and bistability.
- Analyzed stimulus-dependent changes in spike timing precision and reliability, as well as response stochasticity.
Main Results:
- Poisson GLMs successfully replicated a comprehensive suite of canonical neural behaviors: tonic/phasic spiking, bursting, adaptation, type I/II excitation, and bistability.
- GLMs captured stimulus-dependent variations in spike timing precision and reliability, mirroring real neuronal observations.
- The models demonstrated a spectrum of stochasticity, from near-deterministic to greater-than-Poisson variability.
Conclusions:
- Poisson GLMs exhibit a broad range of dynamic spiking behaviors characteristic of real neurons.
- These models are suitable for both quantitative statistical analysis and qualitative dynamical studies of neuronal responses.
- GLMs provide a flexible and tractable framework for understanding single-neuron and neural population dynamics.
