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M A L Nicolelis

Publications and source records attributed to M A L Nicolelis.

2 recordsLinked to original sources

Dopamine levels modulate the updating of tastant values.

To survive, animals must constantly update the internal value of stimuli they encounter; a process referred to as incentive learning. Although there have been many studies investigating whether dopamine is necessary for reward, or for the association between stimuli and actions with rewards, less is known about the role of dopamine in the updating of the internal value of stimuli per se. We used a single-bottle forced-choice task to investigate the role of dopamine in learning the value of tastants. We show that dopamine transporter knock-out mice (DAT-KO), which have constitutively elevated dopamine levels, develop a more positive bias towards a hedonically positive tastant (sucrose 400 mM) than their wild-type littermates. Furthermore, when compared to wild-type littermates, DAT-KO mice develop a less negative bias towards a hedonically negative tastant (quinine HCl 10 mM). Importantly, these effects develop with training, because at the onset of training DAT-KO and wild-type mice display similar biases towards sucrose and quinine. These data suggest that dopamine levels can modulate the updating of tastant values, a finding with implications for understanding sensory-specific motivation and reward seeking.

Animals↗

A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

Action Potentials↗