NIM statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

NIM specifications

Information


Unique identifier OMICS_31918
Name NIM
Alternative name Nonlinear Input Model
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Maintained Yes

Download


download.png
github.png

Versioning


No version available

Maintainers


  • person_outline Daniel Butts
  • person_outline Qing Shi

Additional information


Two versions are available: (1) the code released with the 2013 paper, combined with tutorials exploring the different examples considered in the paper and (2) the current working version of the code being used by the lab, available on Github. The original source code is downloadable at http://neurotheory.umd.edu/biology/ntlab/NIM/NIM_files/NIMtoolbox.zip.

Publications for Nonlinear Input Model

NIM citations

 (8)
library_books

Nonlinear decoding of a complex movie from the mammalian retina

2018
PLoS Comput Biol
PMCID: 5944913
PMID: 29746463
DOI: 10.1371/journal.pcbi.1006057

[…] = 1. The model neuron is stimulated with real data and the intensity trace at the central site of its receptive field is the stimulus considered for decoding. The model has been implemented using the Nonlinear Input Model toolbox []. […]

library_books

Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons

2018
PLoS Comput Biol
PMCID: 5825175
PMID: 29432411
DOI: 10.1371/journal.pcbi.1005997

[…] istribution, usually the first and last). From this, a two-dimensional smoothed surface was fit in MATLAB. A lowess fit with span of 0.05 was chosen for the surface. Third, we compared the GQM to the nonlinear-input model [NIM, ]. The NIM models the synaptic inputs from excitatory and suppressive subunits using half-wave rectifying-type nonlinearities. This model is a more generalized version of t […]

library_books

Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli

2018
PMCID: 5773573
PMID: 29349664
DOI: 10.1186/s13408-017-0057-1

[…] s using both algorithms. The number of spikes used are indicated at the top of each column. Fig. 16We also evaluated the identification performance of the generalized quadratic model (GQM) [] and the nonlinear input model (NIM) [] with quadratic upstream filters to the same example. The results (not shown) were similar to those obtained with the STC algorithm.We note that whereas the low-rank func […]

library_books

Inference of neuronal functional circuitry with spike triggered non negative matrix factorization

2017
Nat Commun
PMCID: 5529558
PMID: 28747662
DOI: 10.1038/s41467-017-00156-9

[…] actual circuit elements. Yet, to check how the effect of including subunits relates to current state-of-the-art approaches in analyzing nonlinear stimulus integration, we compared our results to the Nonlinear Input Model (NIM), which extends the Generalized Linear Model framework to include subunit-like nonlinear stimulus integration. In order to allow for a direct comparison with our subunit mod […]

library_books

Models of Neuronal Stimulus Response Functions: Elaboration, Estimation, and Evaluation

2017
Front Syst Neurosci
PMCID: 5226961
PMID: 28127278
DOI: 10.3389/fnsys.2016.00109

[…] puts are combined using weights wn before a final nonlinear transformation f. Such a model has also been called a generalized nonlinear model (GNM) (Butts et al., , ; Schinkel-Bielefeld et al., ), or nonlinear input model (NIM) (McFarland et al., ) and model parameters may be estimated by maximizing the spike-train likelihood of an inhomogeneous Poisson model with rate given by Equation (17)—often […]

library_books

Network Receptive Field Modeling Reveals Extensive Integration and Multi feature Selectivity in Auditory Cortical Neurons

2016
PLoS Comput Biol
PMCID: 5105998
PMID: 27835647
DOI: 10.1371/journal.pcbi.1005113

[…] lso been extended by McFarland and colleagues to include the sum of more than two input units with monotonically-increasing nonlinearities []. Of the previously described models, this cascaded LN-LN ‘Nonlinear Input Model (NIM)’ model bears perhaps the greatest similarity with our NRF model. Just like our NRF, it comprises a collection of nonlinear units feeding into a nonlinear unit. The main dif […]

Citations

Looking to check out a full list of citations?

NIM institution(s)
Department of Biology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
NIM funding source(s)
Supported by the Department of Biology at University of Maryland, College Park, NIH EY021372, and NSF IIS-135099.

NIM reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review NIM