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We propose an invasive brain-machine interface (BMI) that decodes the orientation of a visual grating from spike train recordings made with a 96 microelectrodes array chronically implanted into the prelunate gyrus (area V4) of a rhesus monkey. The orientation is decoded irrespective of the grating's spatial frequency. Since pyramidal cells are less prominent in visual areas, compared to (pre)motor areas, the recordings contain spikes with smaller amplitudes, compared to the noise level. Hence, rather than performing spike decoding, feature selection algorithms are applied to extract the required information for the decoder. Two types of feature selection procedures are compared, filter and wrapper. The wrapper is combined with a linear discriminant analysis classifier, and the filter is followed by a radial-basis function support vector machine classifier. In addition, since we have a multiclass classification problen, different methods for combining pairwise classifiers are compared.

During neocortical seizures in patients with epilepsy, microelectrode array recordings from the ictal core show a strong correlation between the fast, cellular spiking activities and the low-frequency component of the potential field, reflected in the electrocorticogram (ECoG). Here, we model the relationship between the cellular spike activity and this low-frequency component as the input and output signals of a linear time invariant system. Our approach is based on the observation that this relationship can be characterized by a so-called sinc function, the unit impulse response of an ideal (brick-wall) filter. Accordingly, using a brick-wall filter, we are able to convert ictal cellular spike inputs into an output that significantly correlates with the observed seizure activity in the ECoG $(r=0.40-0.56,p<0.01)$, while ECoG recordings of subsequent seizures within patients also show significant, but lower, correlations $(r=0.10-0.30,p<0.01)$. Furthermore, we can produce seizure-like output signals using synthetic spike trains with ictal properties. We propose a possible physiological mechanism to explain the observed properties associated with an ideal filter, and discuss the potential use of our approach for the evaluation of anticonvulsant strategies.

In order to gain a better theoretical understanding of the interaction between voltage and calcium influx, we present the simulation results for saltatory transmission in a sparsely excitable model of a continuous cylindrical segment of nerve fiber, where calcium diffuses internally and various ion channels are distributed as hotspots along the cable. A standard set of ion channel descriptions is used to illustrate how different numbers and distributions of ion channel hotspots affect the propagation and transmission of a single action potential and/or a spike train and how such hotspots affect calcium influx and diffusion within continuous cylindrical segment of nerve fiber.