Search

Sensory systems, such as the visual or auditory system, are highly non linear. It is therefore not easy to predict the effect of additive inputs on the spiking activity of related brain structures. Here, we propose two metrics to study the effect of additive covariates on the spiking activity of neurons. These metrics are directly obtained from a generalized linear model. We apply these metrics to the study of the effect of additive input audio noise on the spiking activity of neurons in the auditory system. To do so, we combine clean vocalisations with natural stationary or non-stationary noises and record activity in the auditory system while presenting the noisy vocalisations. We found that non-stationary noise has a greater effect on the neural activity than stationary noise. We observe that the results, obtained using the proposed metrics, is more consistent with current knowledge in auditory neuroscience than the results obtained when using a common metric from the literature, the extraction index. more...

We study the impact of different encoding models and spectro-temporal representations on the accuracy of Bayesian decoding of neural activity recorded from the central auditory system. Two encoding models, a generalized linear model (GLM) and a generalized bilinear model (GBM), are compared along with three different spectro-temporal representations of the input stimuli: a spectrogram and two bio-inspired representations, i.e. a gammatone filter bank (GFB) and a spikegram. Signal to noise ratios between the reconstructed and original representations are used to evaluate the decoding, or reconstruction accuracy. We experimentally show that the reconstruction accuracy is best with the spikegram representation and worst with the spectrogram representation and, furthermore, that using a GBM instead of a GLM significantly increases the reconstruction accuracy. In fact, our results show that the spikegram reconstruction accuracy with a GBM fitting yields an SNR that is 3.3 dB better than when using the standard decoding approach of reconstructing a spectrogram with GLM fitting. more...

We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron's intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron's baseline spike rate, its intrinsic dynamics-spiking history-and the stimulus effect which in this case is the analog of the spectrotemporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron's intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF. more...