Flexible information routing in neural populations through stochastic comodulation
Authors: Caroline Haimerl, Cristina Savin, Eero Simoncelli
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We simulate an encoding population of spiking neurons whose rates are modulated by a shared stochastic signal, and show that a linear decoder with readout weights estimated from neuron-specific modulation strength can achieve near-optimal accuracy. We test our hypothesis, we simulate encoding in a population of stimulus-selective, noise-modulated Poisson neurons [13] and compare statistically optimal ideal observer decoders, that have full knowledge of the stimulus-selectivity and modulatory structure of the encoding population, with biologically plausible decoders, that must operate with limited knowledge of the encoding population. |
| Researcher Affiliation | Academia | Caroline Haimerl Center for Neural Science New York University ch2880@nyu.edu Cristina Savin Center for Neural Science Center for Data Science New York University csavin@nyu.edu Eero P. Simoncelli Center for Neural Science, and Howard Hughes Medical Institute New York University eero.simoncelli@nyu.edu |
| Pseudocode | No | The paper describes models and equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to code repositories. |
| Open Datasets | No | The paper uses simulated data based on an encoding model rather than a publicly available dataset. It describes the simulation parameters but does not provide access information for a public dataset. |
| Dataset Splits | No | The paper describes simulations and uses 'training trials' for learning decoder weights (Fig. 2A), but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts for data reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the simulations or experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers (e.g., programming languages, libraries, frameworks) used for the simulations. |
| Experiment Setup | Yes | To test our hypothesis, we simulate encoding in a population of stimulus-selective, noise-modulated Poisson neurons [13]... We assume a one-dimensional modulator and introduce neuron-specific modulation weights, wn, that are proportional to the nth neuron s ability to discriminate the two stimuli. Overall modulation strength in the population is determined by the modulator variance (var(mtwn) = σ2 mw2 n see also [18]). knt(s, mt) Poiss (λn(s) exp(wnmt)) ... we assume i.i.d. zero-mean Gaussian noise and variance σ2 m for mt... We simulated 5000 cells in total, of which 50 were active cells and of those 12 (24% of active) were informative cells. Baseline firing rates were set similar for all active neurons. |