Biomedical subjects
Publications and source records attributed to Rita Almeida.
We have studied a neurodynamic model of cross-modal and cross-temporal associations. We show that a network of integrate-and-fire neurons can generate spiking activity with realistic dynamics during the delay period of a paired associates task. In particular, the activity of the model resembles reported data from single-cell recordings in the prefrontal cortex.
Recent neurophysiological experimental results suggest that the prefrontal cortex plays an important role in filtering out unattended visual inputs. Here we propose a neurodynamical computational model of a part of the prefrontal cortex to account for the neural mechanisms defining this attentional filtering effect. Similar models have been employed to explain experimental results obtained during the performance of attention and working memory tasks. In this previous work the principle of biased competition was shown to successfully account for the experimental data. To model the attentional filtering effect, the biased competition model was extended to enable cooperation between stimulus selective neurons. We show that, in a biological relevant minimal model, competition and cooperation between the neurons are sufficient conditions for reproducing the attentional effect. Furthermore, a characterization of the parameter regime where the cooperation effect is observed is presented. Finally, we also reveal parameter regimes where the network has different modes of operation: selective working memory, attentional filtering, pure competition and noncompetitive amplification.
Prefrontal cortex (PFC) has been suggested to play an important role in executive cognitive functions, participating in planning and controlling behaviour. The results of several recent electrophysiological studies indicate that PFC might be involved not only in the active maintenance of information but in doing so in a context- or task-dependent manner. In a delayed-match-to-sample paradigm, recordings from neurons in the PFC showed their ability to selectively represent information, which is needed for task completion, suggesting that task-irrelevant information does not access working memory. We present a neurodynamical computational model of a part of the PFC to account for the selective representation of information in working memory. We show that a network of biological realistic integrate-and-fire excitatory and inhibitory neurons, implementing the mechanisms of local or modular biased-competition, which is transmitted through cooperation to different subsets of neuronal pools, can explain the formation of selective context-dependent working memory. The modes of operation of the network are characterized and the corresponding parameter settings revealed. Modular competition and cooperation might constitute general mechanisms for implementing context-dependent formation of working memory.
In positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data sets, the number of variables is larger than the number of observations. This fact makes application of multivariate linear model analysis difficult, except if a reduction of the data matrix dimension is performed prior to the analysis. The reduced data set, however, will in general not be normally distributed and therefore, the usual multivariate tests will not be necessarily applicable. This problem has not been adequately discussed in the literature concerning multivariate linear analysis of brain imaging data. No theoretical foundation has been given to support that the null distributions of the tests are as claimed. Our study addresses this issue by introducing a method of constructing test statistics that follow the same distributions as when the data matrix is normally distributed. The method is based on the invariance of certain tests over a large class of distributions of the data matrix. This implies that the method is very general and can be applied for different reductions of the data matrix. As an illustration we apply a test statistic constructed by the method now presented to test a multivariate hypothesis on a PET data set. The test rejects the null hypothesis of no significant differences in measured brain activity between two conditions. The effect responsible for the rejection of the hypothesis is characterized using canonical variate analysis (CVA) and compared with the result obtained by using univariate regression analysis for each voxel and statistical inference based on size of activations. The results obtained from CVA and the univariate method are similar.
Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) measurements reflect changes in the hemodynamics which are thought to be related to local synaptic input to neuron populations. The local neuronal spiking activity, which is believed to form the basis of neuronal coding and communication, is not directly reflected in fMRI/PET measurements. We used a mean-field neuronal model of recurrently coupled excitatory and inhibitory neuronal populations to characterize the relationship between the synaptic activity (reflected in the PET and fMRI measurements) and the neuronal spike rates, averaged over brain areas. We analyzed this relation for a number of cases. For a single brain area and in the absence of external input to its inhibitory neurons, the relation between average spike rates and synaptic activity is linear. However, departures from linearity are found when: (i) the local synaptic strengths vary, (ii) the external inputs vary, in the presence of external input to the inhibitory population, or (iii) the synchronization between oscillations of the average spike rates of two areas changes. We further show that an increase in the imaging signal can reflect a decrease in average spiking activity, in the presence of external input to the inhibitory population. Synaptic activity can also be associated with silent neuronal populations, when input to the excitatory population does not reach the activation threshold or for certain synchronizations between oscillations of two areas. In conclusion, caution should be used when interpreting neuroimaging results in terms of mean spike rates.