Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data representation; images are encoded by the network and processed in a similar manner as the primary layers in visual cortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple object classifier. Results show that the model can successfully learn and classify black and white images with added noise or partially obscured samples with up to ×20 computing speed-up at an equivalent classification ratio when compared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network topology, neuron membrane model and STDP learning.
Oscillatory coupling between distributed areas can constitute a mechanism for neuronal integration. Theta oscillations provide temporal windows for hippocampal processing and only appear during certain active states of animals. Since previous studies have demonstrated that nucleus incertus (NI) contributes to the generation of hippocampal theta activity, in this paper, we evaluated the oscillatory coupling between both structures. We compared hippocampal and NI field potentials that were simultaneously recorded in urethane-anesthetized rats. Electrical and cholinergic stimulations of the reticularis pontis oralis nucleus have been used as hippocampal theta generation models. The spectral analyses reveal that electrical stimulation induced an increase in theta oscillations in both channels, whose frequencies depended on the intensity of stimulation. The intensity range used simultaneously increased the normalized spectral energy in the fast theta band (6-12 Hz) in HPC and NI. Frequencies within the theta range were found to be very similar in both channels. In order to validate coupling, spectral coherence was inspected. The data reveal that coherence in the high theta band also increased while stimuli were applied. Cholinergic activation progressively increased the main frequency in both structures to reach an asymptotic period with stable peak frequency in the low theta range (3-6 Hz), which could be first observed in NI and lasted about 1,500 s. Coherence in this band reached values close to 1. Taken together, these results support an electrophysiological and functional coupling between the hippocampus and the reticular formation, suggesting NI to be part of a distributed network working at theta frequencies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.