Biomedical subjects

Alessandro E P Villa

Publications and source records attributed to Alessandro E P Villa.

6 recordsLinked to original sources

PubMed2006

Learning of auditory equivalence classes for vowels by rats.

Four male Long-Evans rats were trained to discriminate between synthetic vowel sounds using a GO/NOGO response choice task. The vowels were characterized by an increase in fundamental frequency correlated with an upward shift in formant frequencies. In an initial phase we trained the subjects to discriminate between two vowel categories using two exemplars from each category. In a subsequent phase the ability of the rats to generalize the discrimination between the two categories was tested. To test whether rats might exploit the fact that attributes of training stimuli covaried, we used non-standard stimuli with a reversed relation between fundamental frequency and formants. The overall results demonstrate that rats are able to generalize the discrimination to new instances of the same vowels. We present evidence that the performance of the subjects depended on the relation between fundamental and formant frequencies that they had previously been exposed to. Simple simulation results with artificial neural networks could reproduce most of the behavioral results and support the hypothesis that equivalence classes for vowels are associated with an experience-driven process based on general properties of peripheral auditory coding mixed with elementary learning mechanisms. These results suggest that rats use spectral and temporal cues similarly to humans despite differences in basic auditory capabilities.

Animals↗

PubMed2004

Detection of syntonies between multiple spike trains using a coarse-grain binarization of spike count distributions.

At very short timescales neuronal spike trains may be compared to binary streams where each neuron gives at most one spike per bin and therefore its state can be described by a binary variable. Time-averaged activity like the mean firing rate can be generally used on longer timescales to describe the dynamics; nevertheless, enlarging the space of the possible states up to the continuum may seriously bias the true statistics if the sampling is not accurate. We propose a simple transformation on binary variables which allows us to fix the dimensionality of the space to sample and to vary the temporal resolution of the analysis. For each time length interactions among simultaneously recorded neurons are evaluated using log-linear models. We illustrate how to use this method by analysing two different sets of data, recorded respectively in the temporal cortex of freely moving rats and in the inferotemporal cortex of behaving monkeys engaged in a visual fixation task. A detailed study of the interactions is provided for both samples. In both datasets we find that some assemblies share robust interactions, invariant at different time lengths, while others cooperate only at delimited time resolutions, yet the size of the samples is too small to allow an unbiased estimate of all possible interactions. We conclude that an extensive application of our method to larger samples of data, together with the development of techniques to correct the bias in the estimate of the coefficients, would provide significant information about the structure of the interactions in populations of neurons.

Action Potentials↗

PubMed2003

An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animals.

The present study introduces an approach to automatic classification of extracellularly recorded action potentials of neurons. The classification of spike waveform is considered a pattern recognition problem of special segments of signal that correspond to the appearance of spikes. The spikes generated by one neuron should be recognized as members of the same class. The spike waveforms are described by the nonlinear oscillating model as an ordinary differential equation with perturbation, thus characterizing the signal distortions in both amplitude and phase. It is shown that the use of local variables reduces the problem of spike recognition to the separation of a mixture of normal distributions in the transformed feature space. We have developed an unsupervised iteration-learning algorithm that estimates the number of classes and their centers according to the distance between spike trajectories in phase space. This algorithm scans the learning set to evaluate spike trajectories with maximal probability density in their neighborhood. Following the learning, the procedure of minimal distance is used to perform spike recognition. Estimation of trajectories in phase space requires calculation of the first- and second-order derivatives, and integral operators with piecewise polynomial kernels were used. This provided the computational efficiency of the developed approach for real-time application as required by recordings in behaving animals and in human neurosurgical operations. The new method of spike sorting was tested on simulated and real data and performed better than other approaches currently used in neurophysiology.

Action Potentials↗

PubMedDate not supplied

Dynamics of pruning in simulated large-scale spiking neural networks.

Massive synaptic pruning following over-growth is a general feature of mammalian brain maturation. This article studies the synaptic pruning that occurs in large networks of simulated spiking neurons in the absence of specific input patterns of activity. The evolution of connections between neurons were governed by an original bioinspired spike-timing-dependent synaptic plasticity (STDP) modification rule which included a slow decay term. The network reached a steady state with a bimodal distribution of the synaptic weights that were either incremented to the maximum value or decremented to the lowest value. After 1x10(6) time steps the final number of synapses that remained active was below 10% of the number of initially active synapses independently of network size. The synaptic modification rule did not introduce spurious biases in the geometrical distribution of the remaining active projections. The results show that, under certain conditions, the model is capable of generating spontaneously emergent cell assemblies.

Action Potentials↗

PubMedDate not supplied

Event-related potentials in an auditory oddball situation in the rat.

Evoked potentials were recorded from the auditory cortex of both freely moving and anesthetized rats when deviant sounds were presented in a homogenous series of standard sounds (oddball condition). A component of the evoked response to deviant sounds, the mismatch negativity (MMN), may underlie the ability to discriminate acoustic differences, a fundamental aspect of auditory perception. Whereas most MMN studies in animals have been done using simple sounds, this study involved a more complex set of sounds (synthesized vowels). The freely moving rats had previously undergone behavioral training in which they learned to respond differentially to these sounds. Although we found little evidence in this preparation for the typical, epidurally recorded, MMN response, a significant difference between deviant and standard evoked potentials was noted for the freely moving animals in the 100-200 ms range following stimulus onset. No such difference was found in the anesthetized animals.

Animals↗

PubMedDate not supplied

Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients.

The present study demonstrates the application of the Unsupervised Spike Sorting algorithm (USS) to separation of multi-unit recordings and investigation of neuronal activity patterns in the subthalamic nucleus (STN). This nucleus is the main target for deep brain stimulation (DBS) in Parkinsonian patients. The USS comprises a fast unsupervised learning procedure and allows sorting of multiple single units, if any, out of a bioelectric signal. The algorithm was tested on a simulated signal with different levels of noise and with application of Time and Spatial Adaptation (TSA) algorithm for denoising. The results of the test showed a good quality of spike separation and allow its application to investigation of neuronal activity patterns in a medical application. One hundred twenty-four single channel multi-unit records from STN of 6 Parkinsonian patients were separated with USS into 492 single unit trains. Auto- and crosscorrellograms for each unit were analyzed in order to reveal oscillatory, bursting and synchronized activity patterns. We analyzed separately two brain hemispheres. For each hemisphere the percentage of units of each activity pattern were calculated. The results were compared for the first and the second operated hemispheres of each patient and in total.

Action Potentials↗