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Pulsed near infrared laser stimulates the rat visual cortex in vivo

Mou Z. X., Hou W. S., Zheng X. L., Yao J. P., Shang G. B., Yin Z. Q.

Biocybernetics and Biomedical Engineering

2015

Vol. 35, no. 4

247--254

Pulsed infrared irradiation is an alternative neural stimulation with the advantages of being non-contact, spatially precise and artifact-free. Although infrared neural stimulation (INS) is well characterized in the peripheral nervous system, research has been limited in the central nervous system (CNS), especially the near infrared with wavelength around 800 nm. To establish feasibility of INS in the CNS, pulsed near infrared laser (λ = 808 nm, pulse duration = 300–1000 μs, radiant exposure = 0.73–2.45 J/cm2, fiber size = 105 μm, repetition rate = 2 Hz) was used to stimulate the primary visual cortex (V1) of anesthetized Long Evans (LE) rats and the near-infrared-evoked neural activities in V1 was recorded. The impact of the duration of infrared pulse on the intensity and the latency of evoked potentials was assessed. We found that V1 was activated by 808 nm laser and the optical evoked potential (OEP) included a descending wave (D1) and an ascending wave (A1) after optical stimuli. Furthermore, with the increase of the stimulation pulse duration, both the amplitude of D1 and the latency of A1 were increased. The results from this paper will facilitate the applications of near infrared neural stimulation on central nervous system.

A proposal of biologically inspired hierarchical approach to object recognition

Kozik R.

Journal of Medical Informatics & Technologies

2013

Vol. 22

169--176

In this article a biologically-inspired algorithm for object recognition is presented. The approach is based on a hierarchical HMAX cortex model that was initially proposed by Riesenhuber and Poggio [12] and later extended by Serre et al [13]. The results show that despite the modification that were undertaken to simplify the HMAX model (in order to make it feasible for a real-time solutions) it is possible to achieve high effectiveness for a one-class detection problems. Moreover, it is also demonstrated how the proposed algorithm can be successfully deployed on a low-cost Android smartphone.