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Digital Character Image Reconstruction on the Basis of Local Field Potential Signals of Pigeons |
Chen Shuli1, Jiao Xingyang2, Wang Zhizhong1*, Wang Songwei1 |
1(School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China) 2(Industrial Technology Research Institute of Zhengzhou University, Zhengzhou 450001, China) |
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Abstract The digital character image was reconstructed by the pigeon optic tectum neurons using local field potential signal (LFP) generated by visual image stimulation. The microelectrode array was used to record the LFP signal of the neuron under the stimulus of the digital image scanning screen. The Fourier transform was performed to extract the amplitude and phase characteristics. Then the reconstruction model was constructed by using the inverse filter algorithm, and the digital image was reconstructed. The cross-correlation coefficient was used to evaluate the reconstruct digital image. This study found out that with the optimal channel combination, according to the single-factor reconstruction test, the response delay time of neurons to visual stimuli under the reconstructed model was 0.01 s, the response duration was 0.55 s, and the frequency band range was 1 Hz< f1<30 Hz、140 Hz< f2<240 Hz. Under the optimal conditions of each single factor, the cross-correlation coefficient of the ten-digital characters images (0~9) reconstructed from 8 sets of data of 4 pigeons by reconstruction model exceed 0.90 compared with the original image, and the overall cross-correlation coefficient was 0.935±0.013.In short, the neuron response induced by the scanning visual stimulation pattern of digital images can reconstruct the visual stimulation image by means of information accumulation. It also showed that the amplitude and phase characteristics of the LFP signal represented the visual stimulation image better.
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Received: 05 May 2018
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[1] Goddard CA, Sridharan D, Huguenard JR, et al. Gamma oscillations are generated locally in an attention-related midbrain network.[J]. Neuron, 2012, 73(3):567-580. [2] Luksch H. Cytoarchitecture of the avian optic tectum: Neuronal substrate for cellular computation [J]. Rev Neurosci, 2011, 14 (1-2):85-106. [3] Bene FD, Wyart C, Robles E, et al. Filtering of visual information in the tectum by an identified neural circuit [J]. Science, 2010, 330(6004):669-673. [4] Wylie DRW, Gutierrez-Ibanez C, Pakan JMP, et al. The optic tectum of birds: Mapping our way to understanding visual processing [J]. Canadian Journal of Experimental Psychology, 2009, 63(4):328-338. [5] Mysore SP, Knudsen EI. The role of a midbrain network in competitive stimulus selection[J]. Current Opinion in Neurobiology, 2011, 21(4):653-660. [6] Dan Yang, Atick JJ, Reid RC. Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory [J]. Journal of Neuroscience, 1996, 16(10):3351-3351. [7] Stanley GB, Li Feifei, Dan Yang. Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleu s[J]. The Journal of Neuroscience, 1999, 19(18):8036-8042. [8] Dan Yang, Alonso JM, Usrey WM, et al. Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus[J]. Nature Neuroscience, 1998, 1(6):501-507. [9] Freiwald WA, Stemmann H, Wannig A, et al. Stimulus representation in rat primary visual cortex: multi-electrode recordings with micro-machined silicon probes and estimation theory[J]. Neurocomputing, 2001, 44: 407-416. [10] Busse L, Wade AR, Carandini M. Representation of concurrent stimuli by population activity in visual cortex [J]. Neuron, 2009, 64(6): 931-942. [11] Schwartz G, Macke J, Amodei D, et al. Low error discrimination using a correlated population cod e[J]. Journal of Neurophysiology, 2012, 108(4): 1069-1088. [12] Wang Songwei, Liu Lijun, Wang Zhizhong, et al. Luminance information decoding on the basis of local field potential signals of pigeon optic tectum neurons[J]. Neuroreport, 2017, 28(16):1036-1042. [13] Siegel M, Donner TH, Engel AK. Spectral fingerprints of large-scale neuronal interactions [J]. Nature Reviews Neuroscience, 2012, 13(2):121-134. [14] Montemurro MA, Rasch MJ, Murayama Y, et al. Phase-of-firing coding of natural visual stimuli in primary visual cortex[J]. Current Biology, 2008, 18(5):375-380. [15] Mazzoni A, Panzeri S, Logothetis NK, et al. Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons[J]. PLoS Computational Biology, 2008, 4(12):e1000239. [16] Gollisch T, Meister M. Rapid neural coding in the retina with relative spike latencies[J]. Science, 2008, 319(5866):1108-1111. [17] Carrasco M. Visual attention: The past 25 years [J]. Vision Research, 2011, 51(13):1484-1525. [18] Belitski A, Gretton A, Magri C, et al. Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information[J]. Journal of Neuroscience, 2008, 28(22):5696-5709. [19] Penttonen M, Kamondi A. Gamma frequency oscillation in the hippocampus of the rat: intracellular analysis in vivo[J]. European Journal of Neuroscience, 2010, 10(2):718-728. [20] Poulet JF, Petersen CC. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice [J]. Nature, 2008, 454(7206):881-885. [21] Buschman T, Denovellis E, Diogo C, et al. Synchronous oscillatory neural ensembles for rules in the prefrontal cortex[J]. Neuron, 2012, 76(4):838-846. [22] Yargholi E, Hossein-Zadeh GA. Brain decoding-classification of hand written digits from fmri data employing Bayesian networks [J]. Frontiers in Human Neuroscience, 2016, 10(13):351-363. [23] Song Sutao, Ma Xinyue, Zhan Yu, et al. Bayesian reconstruction of multiscale local contrast images from brain activity[J]. Journal of Neuroscience Methods, 2013, 220(1):39-45. [24] Nishimoto S, Vu AT, Naselaris T, et al. Reconstructing visual experiences from brain activity evoked by natural movies[J]. Current Biology, 2011, 21(19):1641-1646. |
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