som neural net tool matlab 2017b  (MathWorks Inc)

Journal: Frontiers in Neuroscience

Article Title: Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods

doi: 10.3389/fnins.2023.1175629

Figure Lengend Snippet: The discriminability of responses estimated using a SOM neural net. The spatio-temporally patterned array of inputs is weighted based on unsupervised learning to cluster the sets of inputs according to the variability and the patterns present in the dataset. Large intrinsic differences in patterns between responses to 2 stimuli thus lead to reliable clustering. We compare this SOM decoder where each timed point and each neuron are weighted independently (magenta) to a decoder based on Euclidean distance with time points and neurons kept as separate dimensions or a van Rossum metric where responses of different neurons are averaged together before the comparison. (A) PC neuron responses to electrosensory chirps. (B) PN response of the moth antennal lobe to odors. (C) LIF model responses to frozen white noise stimuli of different shapes. Curves show averages (± s.d.) across all pairs of stimuli (number of stimuli: electrosensory = 3 different chirps, 3 pairs; olfactory = 7 odors, 21 pairs; LIF = 10 noise patterns, 45 pairs).

Article Snippet: We used the SOM neural net tool built into Matlab 2017b (Mathworks, Natick, MA) and detailed documentation can be obtained from the provider.

Techniques:

Journal: Frontiers in Neuroscience

Article Title: Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods

doi: 10.3389/fnins.2023.1175629

Figure Lengend Snippet: A modified Euclidean distance, where each dimension is weighted, allows accurate discrimination with similar -or better- performance than SOM neural nets. In the “WED” (Weighted Euclidean Distance) analysis, each dimension in Euclidean space is weighted based on the Kullback–Leibler divergence of the response distribution in that dimension. Each dimension (neuron/time bin) can be weighted independently (‘independent W’), or a single weight can be set for a given neuron across time bins (‘fixed W’). Although using independent weights maximizes the information extracted about the difference in stimuli, using a fixed weight emulates a biologically more realistic decoding network. The best method varies across systems: (A) Electrosensory; (B) Olfactory; (C) LIF model. Curves show averages (± s.d.) across all pairs of stimuli.

Article Snippet: We used the SOM neural net tool built into Matlab 2017b (Mathworks, Natick, MA) and detailed documentation can be obtained from the provider.

Techniques: Modification