Responses of midbrain auditory neurons to two different environmental sounds-A new approach on cross-sound modeling.

When modeling auditory responses to environmental sounds, results are satisfactory if both training and testing are restricted to datasets of one type of sound. To predict 'cross-sound' responses (i.e., to predict the response to one type of sound e.g., rat Eating sound, after training with another type of sound e.g., rat Drinking sound), performance is typically poor. Here we implemented a novel approach to improve such cross-sound modeling (single unit datasets were collected at the auditory midbrain of anesthetized rats). The method had two key features: (a) population responses (e.g., average of 32 units) instead of responses of individual units were analyzed; and (b) the long sound segment was first divided into short segments (single sound-bouts), their similarity was then computed over a new metric involving the response (called Stimulus Response Model map or SRM map), and finally similar sound-bouts (regardless of sound type) and their associated responses (peri-stimulus time histograms, PSTHs) were modelled. Specifically, a committee machine model (artificial neural networks with 20 stratified spectral inputs) was trained with datasets from one sound type before predicting PSTH responses to another sound type. Model performance was markedly improved up to 92%. Results also suggested the involvement of different neural mechanisms in generating the early and late responses to amplitude transients in the broad-band environmental sounds. We concluded that it is possible to perform rather satisfactory cross-sound modeling on datasets grouped together based on their similarities in terms of the new metric of SRM map.

Coding of communication calls in the subcortical and cortical structures of the auditory system.

The processing of species-specific communication signals in the auditory system represents an important aspect of animal behavior and is crucial for its social interactions, reproduction, and survival. In this article the neuronal mechanisms underlying the processing of communication signals in the higher centers of the auditory system--inferior colliculus (IC), medial geniculate body (MGB) and auditory cortex (AC)--are reviewed, with particular attention to the guinea pig. The selectivity of neuronal responses for individual calls in these auditory centers in the guinea pig is usually low--most neurons respond to calls as well as to artificial sounds; the coding of complex sounds in the central auditory nuclei is apparently based on the representation of temporal and spectral features of acoustical stimuli in neural networks. Neuronal response patterns in the IC reliably match the sound envelope for calls characterized by one or more short impulses, but do not exactly fit the envelope for long calls. Also, the main spectral peaks are represented by neuronal firing rates in the IC. In comparison to the IC, response patterns in the MGB and AC demonstrate a less precise representation of the sound envelope, especially in the case of longer calls. The spectral representation is worse in the case of low-frequency calls, but not in the case of broad-band calls. The emotional content of the call may influence neuronal responses in the auditory pathway, which can be demonstrated by stimulation with time-reversed calls or by measurements performed under different levels of anesthesia. The investigation of the principles of the neural coding of species-specific vocalizations offers some keys for understanding the neural mechanisms underlying human speech perception.

Temperature dependence of blood surface tension.

Whole blood surface tension of 15 healthy subjects recorded by the ring method was investigated in the temperature range from 20 to 40 degrees C. The surface tension omega as a function of temperature t ( degrees C) is described by an equation of linear regression as omega(t) = (-0.473 t + 70.105) x 10(-3) N/m. Blood serum surface tension in the range from 20 to 40 degrees C is described by linear regression equation omega(t) = (-0.368 t + 66.072) x 10(-3) N/m and linear regression function of blood sediment surface tension is omega(t) = (-0.423 t + 67.223) x10(-3) N/m.

Neuronal connections in the medial geniculate body of the guinea-pig.

The spontaneous and evoked activities of individual pairs of single units were recorded simultaneously with the same microelectrode in the medial geniculate body (MGB) of ketamine-xylazine-anaesthetised guinea-pigs. Cross-correlograms (CCGs) of spike train pairs were computed and divided on the basis of correlation peak shape into four classes [a unilateral narrow (UN) peak, a centrally positioned wide (CW) peak, a complex peak and no significant peak] interpreted in terms of the functional connection between neighbouring neurones. The shift predictor procedure was applied with the aim of removing the effect of the stimulus on the final CCG shape. The occurrence of correlation peak types and the distribution of correlation coefficients were found to be similar for the spontaneous activity during silent periods following acoustical stimulation and for the long-lasting recording of spontaneous activity. CCGs in 38% of pairs computed during silent interstimulus intervals contained a UN peak, suggesting a monosynaptic excitatory connection. Almost 20% of all pairs expressed a CCG shape typical for a common input, i.e. a CW peak. In 5% of cases multiple, so-called complex peaks, were found. About 20% of the CCGs contained no significant correlation peak in the interstimulus period, which is typical for a very weak or absent functional connection between recorded neurones. No inhibitory interaction (groove in the CCGs) between recorded pairs was observed. The distribution of correlation peak shapes was similar when calculated during acoustical stimulation and during silent interstimulus intervals. CCGs computed during presentation of four acoustical stimuli (pure tone bursts, noise bursts, natural call whistle and artificially inverted whistle) showed most frequently a UN peak (28-37%) followed by CCGs with no significant peak (18-28%) and with a UN/CW peak (14-23%). On average, the occurrence of UN peaks tended to be less frequent during stimulus presentation than in silent conditions, but the difference was not statistically significant. The most frequent occurrence of clear UN peaks was found in the medial part of the MGB (from 52-64% of pairs depending on the type of acoustical stimulus), while the least was observed in the ventral part of the MGB (12-22%). In contrast, CW peaks were most frequently expressed in pairs located in the ventral part of the MGB (18-33%), while neuronal pairs in the medial part revealed a very low occurrence of CW peaks (0-7%). The occurrence of independently firing neurones was lowest in the medial part of the MGB (8-20% of pairs) in comparison with the ventral (31-39%) and dorsal (12-41%) parts. In 20% of pairs acoustical stimulation produced a change in the type of correlation peak present during spontaneous activity. Most frequently, a CW peak (shared input) changed to a flat CCG, which represents independently firing neurones. In some pairs higher connection strengths (as expressed by the value of the correlation coefficient) were found for silent interstimulus intervals than for acoustical stimulation. This suggests that in the MGB the stimulus may desynchronise the spontaneous activity of simultaneously firing units in neuronal pairs.

Computation of first passage time moments for stochastic diffusion processes modelling nerve membrane depolarization.

For further understanding of neural coding, stochastic variability of interspike intervals has been investigated by both experimental and theoretical neuroscientists. In stochastic neuronal models, the interspike interval corresponds to the time period during which the process imitating the membrane potential reaches a threshold for the first time from a reset depolarization. For neurons belonging to complex networks in the brain, stochastic diffusion processes are often used to approximate the time course of the membrane potential. The interspike interval is then viewed as the first passage time for the employed diffusion process. Due to a lack of analytical solution for the related first passage time problem for most diffusion neuronal models, a numerical integration method, which serves to compute first passage time moments on the basis of the Siegert recursive formula, is presented in this paper. For their neurobiological plausibility, the method here is associated with diffusion processes whose state spaces are restricted to finite intervals, but it can also be applied to other diffusion processes and in other (non-neuronal) contexts. The capability of the method is demonstrated in numerical examples and the relation between the integration step, accuracy of calculation and amount of computing time required is discussed.