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Independent component analysis (ICA) has been proven useful for suppression of artifacts in EEG recordings. It involves separation of measured signals into statistically independent components or sources, followed by rejection of those deemed artificial. We show that a "leak" of cerebral activity of interest into components marked as artificial means that(More)
The spontaneous activity of working neurons yields synaptic currents that mix up in the volume conductor. This activity is picked up by intracerebral recording electrodes as local field potentials (LFPs), but their separation into original informative sources is an unresolved problem. Assuming that synaptic currents have stationary placing we implemented(More)
To study how the visual areas of the 2 hemispheres interact in processing visual stimuli we have recorded local field potentials in the callosally connected parts of areas 17 and 18 of the ferret during the presentation of 3 kinds of stimuli: 2.5 degrees squares flashed for 50 ms randomly in the visual field (S1), 4 full-field gratings differing in(More)
In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network obtained by our method includes explicit mathematical models of each of the spiking neurons and a(More)
We study how individual memory items are stored assuming that situations given in the environment can be represented in the form of synaptic-like couplings in recurrent neural networks. Previous numerical investigations have shown that specific architectures based on suppression or max units can successfully learn static or dynamic stimuli (situations).(More)
Sorting of the extracellularly recorded spikes is a basic prerequisite for analysis of the cooperative neural behavior and neural code. Fundamentally the sorting performance is defined by the quality of discriminative features extracted from spike waveforms. Here we discuss two features extraction approaches: principal component analysis (PCA), and wavelet(More)
Local field potentials (LFPs) capture the electrical activity produced by principal cells during integration of converging synaptic inputs from multiple neuronal populations. However, since synaptic currents mix in the extracellular volume, LFPs have complex spatiotemporal structure, making them hard to exploit. Here we propose a biophysical framework to(More)
Animals for survival in complex, time-evolving environments can estimate in a "single parallel run" the fitness of different alternatives. Understanding of how the brain makes an effective compact internal representation (CIR) of such dynamic situations is a challenging problem. We propose an artificial neural network capable of creating CIRs of dynamic(More)
Information processing and exchange between brain nuclei are made through spike series sent by individual neurons in highly irregular temporal patterns. Synchronization in cell assemblies, proposed as a network language for internal neural representations, still has little experimental support. We use a novel technique to extract pathway-specific local(More)