The effect of trained parameters in Bayesian neural encoding models for the auditory system
Bibliographic record
Abstract
Bayesian neural decoding models aim at estimating an extrinsic stimulus, such as speech, from a neural population. They have been used to study several physiological systems such as the motor or auditory systems [1]. Such decoding models require the use of an encoding model, i.e. which aim at estimating the neural spikes from the stimulus [2]. One encoding model that has been used extensively in the past models the instantaneous spiking rate of a neuron using a generalized linear model (GLM). When applied to the auditory system, this GLM has three parameters that account respectively for 1) the spontaneous firing rate of the decoded neuron, 2) the spectro-temporal receptive field of the neuron and 3) the intrinsic dynamics of the neuron, such as refractory periods, bursting and network dynamics [3]. In a decoding framework, these parameters are estimated by fitting the encoding model to spike trains measured when presenting a specific stimulus (the training set) and used afterwards to perform the decoding of spike trains obtained when presenting a different stimulus (the test set). While it could be assumed that the parameters associated with the spontaneous rate and receptive fields won’t change significantly for different stimuli for a given neuron, the intrinsic dynamics will most likely change. Here we wish to investigate how trained GLM parameters, i.e. fitted for some specific stimulus, of a neural encoding model for the auditory system can accurately represent the spikes measured for a different stimulus. To do so we use a goodness of fit metric, the normalized Kolmogorov-Smirnov (KS) statistic; a normalized KS < 1 indicating an excellent fit. Table Table11 presents the normalized KS statistic obtained when evaluating the goodness of fit of the encoding model using trained parameters on measured spikes from the test set with 2-fold cross-validation. The recordings are from 54 auditory nerve neurons (20 trials each) in anesthetized cats. We observe that the normalized KS values are < 1 for only 13% (7/54) of the neurons suggesting an extremely poor fit of the encoding model. Using such encoding model parameters in a decoding framework will therefore introduce a strong bias in the decoded stimulus that is not currently taken into account in neural decoding models. Table 1 Number of neurons for which the maximum normalized KS statistics using 2-fold cross validation is within a given range for both training and test sets. A KS statistic < 1 indicates an accurate fit of the model. (n=54, 20 trials each, recordings ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".