5 research outputs found

    Distributed coding of sound locations in the auditory cortex

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    Although the auditory cortex plays an important role in sound localization, that role is not well understood. In this paper, we examine the nature of spatial representation within the auditory cortex, focusing on three questions. First, are sound-source locations encoded by individual sharply tuned neurons or by activity distributed across larger neuronal populations? Second, do temporal features of neural responses carry information about sound-source location? Third, are any fields of the auditory cortex specialized for spatial processing? We present a brief review of recent work relevant to these questions along with the results of our investigations of spatial sensitivity in cat auditory cortex. Together, they strongly suggest that space is represented in a distributed manner, that response timing (notably first-spike latency) is a critical information-bearing feature of cortical responses, and that neurons in various cortical fields differ in both their degree of spatial sensitivity and their manner of spatial coding. The posterior auditory field (PAF), in particular, is well suited for the distributed coding of space and encodes sound-source locations partly by modulations of response latency. Studies of neurons recorded simultaneously from PAF and/or A1 reveal that spatial information can be decoded from the relative spike times of pairs of neurons โ€“ particularly when responses are compared between the two fields โ€“ thus partially compensating for the absence of an absolute reference to stimulus onset.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47436/1/422_2003_Article_439.pd

    Unified selective sorting approach to analyse multi-electrode extracellular data

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    Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators

    PDMS๊ธฐ๋ฐ˜์˜ ๊ณ ์›ํ˜• ์‹ ๊ฒฝ ์ ‘์† ์ „๊ทน ๊ตฌ์กฐ ์„ค๊ณ„ ๋ฐ ๊ตฌํ˜„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2016. 2. ์„œ์ข…๋ชจ.๋ณต์žกํ•œ ํ‘œ๋ฉด ๊ตด๊ณก์„ ๊ฐ–๋Š” ์‹ ๊ฒฝ ์กฐ์ง๊ณผ ํšจ๊ณผ์ ์œผ๋กœ ์ธํ„ฐํŽ˜์ด์Šคํ•˜๊ธฐ ์œ„ํ•œ PDMS๊ธฐ๋ฐ˜์˜ ์œ ์—ฐํ•œ ํ‰ํŒํ˜• ๋ฏธ์„ธ์ „๊ทน์„ ๊ฐœ๋ฐœํ•˜๊ณ  ๊ทธ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ธฐ๋ก์ „๊ทน ์ธก๋ฉด์—์„œ, ๊ธฐ์กด์˜ ์š”์ฒ ํ˜• ์ „๊ทน ๊ตฌ์กฐ๋ฅผ ํƒˆํ”ผํ•˜๊ณ  ๊ณ ์›์ง€ํ˜• ๋ชจ์–‘์˜ ์ „๊ทน์„ ํŠน์ง•์œผ๋กœํ•˜๋Š” ํ”Œ๋ผํ† (plateau)์ „๊ทน ์ œ์กฐ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ๋‹ค๋ฅธ ์ƒ์ฒด์ ํ•ฉ์„ฑ ํด๋ฆฌ๋จธ์ธ ํด๋ฆฌ์ด๋ฏธ๋“œ๋‚˜ ํŒจ๋Ÿด๋ฆฐ์œผ๋กœ ์ œ์ž‘๋œ ๋‹ค์ฑ„๋„์ „๊ทน์€ ๋‚ฎ์€ ์ ํ•ฉ์„ฑ(conformability)๊ณผ ์‹ ๊ฒฝ์กฐ์ง๊ณผ ๋น„๊ตํ•˜์—ฌ ๋†’์€ ์˜๋ฅ  ๊ทธ๋ฆฌ๊ณ  ๋‰ด๋Ÿฐ๊ณผ ์ „๊ทน ์‚ฌ์ด์— ๊ณต๊ธฐ ๊ฐ‡ํž˜ ํ˜„์ƒ์ด ๋ฐœ์ƒํ•˜๋Š” ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ๋‹ค. ์ „๊ทน์˜ ์š”์ฒ  ๊ตฌ์กฐ๋Š” ๋‡Œ์™€ ๊ฐ™์ด ๋ถ€๋“œ๋Ÿฌ์šด ์‹ ๊ฒฝ ์กฐ์ง์—์„œ๋Š” ์žฌ๋ถ„ํฌ(repopulation)ํ˜„์ƒ์œผ๋กœ ๋ฌธ์ œ๊ฐ€ ์™„ํ™”๋˜์ง€๋งŒ ์ฒ™์ˆ˜์™€ ๊ฐ™์ด ์‹ ๊ฒฝ ๋‹ค๋ฐœ๋กœ ์ด๋ฃจ์–ด์ง„ ์กฐ์ง์—์„œ๋Š” ์‹ ํ˜ธ ์—ดํ™”์˜ ์ฃผ๋œ ์š”์ธ์ด ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ์‚ฌ์šฉํ•œPDMS๋Š”์ƒ์ฒด์ ํ•ฉ์„ฑํด๋ฆฌ๋จธ์ค‘๊ฐ€์žฅ์‹ ๊ฒฝ์กฐ์ง๊ณผ์˜์˜๋ฅ ์ด์œ ์‚ฌํ•˜๊ณ  ๋›ฐ์–ด๋‚œ ์ ํ•ฉ์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ์–ด ๊ธฐ๋ก์šฉ์ „๊ทน์˜ ์žฌ๋ฃŒ๋กœ ์ ํ•ฉํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋‹ค๋ฅธํด๋ฆฌ๋จธ์™€ ์œ ์‚ฌํ•˜๊ฒŒ PDMS์˜ ์†Œ์ˆ˜์„ฑ ํŠน์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ ์ „๊ทน์˜ ํฌ๊ธฐ๊ฐ€ ์ž‘์•„์งˆ์ˆ˜๋ก ๊ณต๊ธฐ ๊ฐ‡ํž˜ํ˜„์ƒ์ด ๋‘๋“œ๋Ÿฌ์ง€๊ฒŒ ๋ฐœ์ƒํ•˜์˜€๊ณ  ์ด๋กœ์ธํ•˜์—ฌ ์‹คํ—˜์˜ ์‹ ๋ขฐ์„ฑ์— ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ํ”Œ๋ผํ† ์ „๊ทน์€ ์‹ ๊ฒฝ์กฐ์ง๊ณผ์˜ ์ ํ•ฉ์„ฑ ๋ฐ ๊ณต๊ธฐ ๊ฐ‡ํž˜ ํ˜„์ƒ์„ ํšจ๊ณผ์ ์œผ๋กœ ํ•ด๊ฒฐํ•จ์œผ๋กœ์จ ์‹ ๋ขฐ์„ฑ์žˆ๋Š” ๋™๋ฌผ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ž๊ทน์ „๊ทน ์ธก๋ฉด์—์„œ, ๊ธฐ์กด ์ˆ˜์งํ˜• ๋ฒฝ๋ฉด ์ „๊ทน์˜ ์ „๋ฅ˜ ๋ฐ€๋„ ๋ถ„ํฌ๋Š” ์ „๊ทน์˜ ๊ฐ€์žฅ์ž๋ฆฌ์—์„œ ๊ฐ€์žฅ ๊ฐ•ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜ ์กฐ์ง์„ ๊ท ์ผํ•˜๊ฒŒ ์ž๊ทนํ•˜๊ธฐ ์–ด๋ ค์› ๋‹ค. ํ•˜์ง€๋งŒ ์ „๊ทน์˜ ๋ฒฝ๋ฉด ๋ชจ์–‘์— ๋”ฐ๋ผ ์ „๊ทน์˜ ํ‘œ๋ฉด์— ์œ ๊ธฐ๋˜๋Š” ์ „๋ฅ˜ ๋ฐ€๋„ ๋ถ„ํฌ๊ฐ€ ๋ณ€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜๊ณ  PDMS๊ธฐ๋ฐ˜์˜ ๊ฒฝ์‚ฌ๋ฒฝ๋ฉด์„ ๊ฐ–๋Š” ์ „๊ทน ์ œ์ž‘ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์ œ์ž‘ํ•˜์˜€๋‹ค. ๊ฒฝ์‚ฌ๊ฐ์„ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•ด์„œ ์Œ์„ฑ๊ฐ๊ด‘์ œ์˜ ๋…ธ๊ด‘์—๋„ˆ์ง€๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์˜€๋‹ค. ๊ณผ์†Œ๋…ธ๊ด‘์ •๋„์™€ ๋งˆ์Šคํฌ์™€ ๊ฐ๊ด‘์ œ์˜ ๊ฐ„๊ฒฉ์— ๋”ฐ๋ผ ๊ฐ๊ด‘์ œ ๊ธฐ๋‘ฅ์˜ ๊ฒฝ์‚ฌ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์ œ์–ดํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ,์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์ „๊ทน ๋ฒฝ๋ฉด์˜ ๊ฒฝ์‚ฌ๋ฅผ ์ œ์–ดํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ˆ˜์ง ๋ฒฝ๋ฉด ์ „๊ทน, ๊ฒฝ์‚ฌ๋ฒฝ๋ฉด ์ „๊ทน, ํ‘œ๋ฉด ๋ถ€์ฐฉํ˜• ์ „๊ทน๊ตฌ์กฐ์—์„œ ์ „๋ฅ˜ ๋ฐ€๋„ ๋ถ„ํฌ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ COMSOL์„ ์ด์šฉํ•ด ์ „๋ฅ˜ ๋ฐ€๋„ ๋ถ„ํฌ๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜์˜€๋‹ค. ๋‹ค์ธต ๊ธฐํŒ ์ œ์ž‘์— ์žˆ์–ด ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์ธต๊ฐ„์—ฐ๊ฒฐ ๊ธฐ๋ฒ•์„ ๊ธฐ์กด์˜ ์ˆ˜์งํ˜• ์ธต๊ฐ„์—ฐ๊ฒฐ์—์„œ ๊ฒฝ์‚ฌํ˜• ์ธต๊ฐ„์—ฐ๊ฒฐ๋กœ ๋Œ€์ฒดํ•จ์œผ๋กœ์จ ๋„๊ธˆ๊ณต์ •์—†์ด ๋‹ค์ธต๊ธฐํŒ์„ ์ œ์ž‘ํ•˜๋Š” ๊ณต์ • ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ธต๊ฐ„์—ฐ๊ฒฐ ๊ณผ์ •์—์„œ PDMS์™€ ๊ธˆ์„ ์—ฐ๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ํ‹ฐํƒ€๋Š„์„ ๋ฐฐ์ œํ•˜๋Š” ๊ณต์ •์„ ์ œ์•ˆํ•จ์œผ๋กœ์จ ์ธต๊ฐ„์—ฐ๊ฒฐ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ €ํ•ญ์„ ํšจ๊ณผ์ ์„ ๋ฐฉ์ง€ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  PDMS๊ธฐํŒ์˜ ์ธ์žฅ ๋ฐ ๊ตฝํž˜์‹œํ—˜์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ธ์žฅ๋น„์œจ์ด4%์ด๋‚ด์—์„œ๋Š” ๋‹จ์„ ์ด ๋ฐœ์ƒํ•˜์ง€ ์•Š์Œ์„ ํ™•์ธํ•˜์˜€๊ณ  3000ํšŒ์˜๊ตฝํž˜ ์‹คํ—˜ ๋™์•ˆ์— ์„ฑ๋Šฅ์˜ ์—ดํ™”๊ฐ€ ๋ฏธ๋ฏธํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” 8๊ฐœ์˜ ์ธต๊ฐ„์—ฐ๊ฒฐ๊ณผ 5ํšŒ์˜ ๋„์„  ๊ต์ฐจ๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ์–‘๋ฉด ๊ธฐํŒ์„ ์ œ์ž‘ํ•˜์—ฌ ์ œ์•ˆ ๊ธฐ๋ฒ•์˜ ์œ ํšจ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ธฐ์กด ๋ฐฉ์‹์œผ๋กœ ์ œ์ž‘ํ•œ ์˜ค๋ชฉํ•œ(recessed) ์ „๊ทน๊ณผ ์ œ์•ˆํ•œ ํ”Œ๋ผํ†  ์ „๊ทน์„ ์ด์šฉํ•˜์—ฌ ๋™๋ฌผ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์˜ค๋ชฉํ•œ ์ „๊ทน์—์„œ๋Š” ๊ณต๊ธฐ ๊ฐ‡ํž˜ ํ˜„์ƒ์œผ๋กœ ์ธํ•˜์—ฌ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์‹ ํ˜ธ ๊ธฐ๋ก์ด ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜์œผ๋‚˜, ํ”Œ๋ผํ†  ์ „๊ทน์—์„œ๋Š” ๊ณต๊ธฐ ๊ฐ‡ํž˜ ํ˜„์ƒ์ด ์ผ์–ด๋‚˜์ง€ ์•Š์•„ ํšจ๊ณผ์ ์œผ๋กœ ์ƒ์ฒด ์‹ ํ˜ธ๋ฅผ ๊ธฐ๋กํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋™๋ฌผ ์‹คํ—˜์€ ๋‘๊ฐ€์ง€ ํ™˜๊ฒฝ์—์„œ ์ง„ํ–‰ํ•˜์˜€๋Š”๋ฐ, ํ•œ ์‹คํ—˜์€ ์„ค์น˜๋ฅ˜์˜ ํšŒ์Œ๋ถ€๋ฅผ ๋ฌผ๋ฆฌ์ ์œผ๋กœ ์ž๊ทนํ•จ์œผ๋กœ์จ ๋ฐœ์ƒํ•˜๋Š” ์ฒ™์ˆ˜์‹ ๊ฒฝ ์‹ ํ˜ธ๋ฅผ ๊ธฐ๋กํ•จ์œผ ๋กœ์จ ์ž๊ทน์˜ ์œ ํšจ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๊ณ , ๋‹ค๋ฅธํ•˜๋‚˜๋Š” 2๊ฐ€์ง€์˜ ๋ƒ„์ƒˆ๋ฌผ์งˆ์„ ์„ค์น˜๋ฅ˜์˜ ์ฝ”์— ์ฃผ์ž…ํ•œ ํ›„, ๋‡Œ์˜ ํ›„๊ฐ๋ง์šธ์—์„œ ๊ทธ ์‹ ํ˜ธ๋ฅผ ์ธก์ •ํ•จ์œผ๋กœ์จ ์ž๊ทน์˜ ์œ ํšจ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.I. ์„œ ๋ก  1 1.1 ์‹ ๊ฒฝ ์ ‘์† ๊ธฐ์ˆ  1 1.2 ๋‹ค์ฑ„๋„ ์ „๊ทน์—ด ๊ธฐ์ˆ  4 1.2.1 ํด๋ฆฌ๋จธ ๊ธฐ๋ฐ˜์˜ ์œ ์—ฐํ•œ ํ‘œ๋ฉดํ˜• MEA 6 1.2.2 ์‹ค๋ฆฌ์ฝ˜ ๊ณ ๋ฌด 9 1.2.3 PDMS ๊ธฐ๋ฐ˜์˜ ์œ ์—ฐํ•œ ํ‘œ๋ฉดํ˜• MEA 12 1.3 PDMS๊ธฐ๋ฐ˜์˜ ๋ฐ˜๋„์ฒด ๊ณต์ • ๊ธฐ์ˆ  15 1.3.1 ๋ฏธ์„ธ ๊ณต์ • ๋ฌธ์ œ 15 1.3.2 ๊ธฐ๋ก ์ „๊ทน ๋ฌธ์ œ 16 1.3.3 ์ž๊ทน ์ „๊ทน ๋ฌธ์ œ 17 1.3.4 ๋‹ค์ธต ๊ธฐํŒ ๋ฌธ์ œ 18 1.4 ์š”์•ฝ 20 II. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 23 2.1 PDMS๋ฅผ ์ด์šฉํ•œ ๋ฐ˜๋„์ฒด ๊ณต์ • 24 2.1.1 ํ‘œ๋ฉด ์ฒ˜๋ฆฌ ๊ธฐ์ˆ  24 2.1.2 ๊ธˆ์† ๋„์„  ์‹๊ฐ 29 2.1.3 ๊ธฐ์กด์˜ PDMS ํŒจํ„ฐ๋‹ ๊ธฐ๋ฒ• 42 2.1.4 ์ œ์•ˆ ์ฃผ๋ฌผ ๊ธฐ๋ฒ• 44 2.2 ๊ธฐ๋ก์šฉ ํ”Œ๋ผํ† (Plateau) ์ „๊ทน 50 2.2.1 ํ”Œ๋ผํ†  ์ „๊ทน ์ œ์ž‘ ๊ณต์ • 51 2.2.2 ์ „๊ทน ํ”„๋กœํŒŒ์ผ 56 2.2.3 ์ž„ํ”ผ๋˜์Šค ์ธก์ • 58 2.2.4 ์ „๊ทน์˜ ํ‘œ๋ฉด ์ ‘์ด‰ ์„ฑ๋Šฅ 60 2.3 ์ž๊ทน์šฉ ๊ฒฝ์‚ฌ ์ „๊ทน(slated-edge electrode) 60 2.3.1 ๊ธฐ์กด ์ „๊ทน์˜ ์ „๋ฅ˜ ๋ฐ€๋„ ๋ฌธ์ œ 61 2.3.2 ๊ฒฝ์‚ฌ ์ „๊ทน ์ œ์ž‘ ๊ณต์ • 62 2.3.3 ์‹ ๋ขฐ์„ฑ ์‹คํ—˜ 67 2.3.4 ์ „๊ทน ๊ตฌ์กฐ์— ๋”ฐ๋ฅธ ์ „๋ฅ˜ ๋ฐ€๋„ ๋ถ„ํฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 69 2.4 PDMS ๊ธฐ๋ฐ˜์˜ ๋‹ค์ธต ๊ธฐํŒ ์ œ์ž‘์˜ ํ•„์š”์„ฑ 69 2.4.1 MPTMS๋ฅผ ์ด์šฉํ•œ PDMS ํ‘œ๋ฉด ๊ฐœ์งˆ 71 2.4.2 ์—ด์••์ฐฉ๊ธฐ๋ฅผ ์ด์šฉํ•œ PDMS๋ฐ•๋ง‰ ํ‰ํƒ„๋„ ํ–ฅ์ƒ 73 2.4.3 ๋‹ค์ธต ๊ธฐํŒ์„ ์œ„ํ•œ ์—ฐ๊ฒฐํ†ต๋กœ(Via) ๊ตฌ์กฐ 74 2.4.4 ๋‹ค์ธต ๊ธฐํŒ ์ œ์ž‘ ๊ณต์ • ๊ธฐ๋ฒ• 75 2.4.5 ์ •์ „ ์šฉ๋Ÿ‰ ์ธก์ • 79 2.5 ํ”Œ๋ผํ† (plateau) ์ „๊ทน์„ ์ด์šฉํ•œ ๋™๋ฌผ์‹คํ—˜ 79 2.5.1 ์‹ ๊ฒฝ ์‹ ํ˜ธ ๊ธฐ๋ก์„ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค ์ œ์ž‘ 80 2.5.2 ์ฒ™์ˆ˜ ์‹ ๊ฒฝ ์‹ ํ˜ธ ๊ธฐ๋ก ์‹คํ—˜ 82 2.5.3 ํ›„๊ฐ๋ง์šธ ์‹ ๊ฒฝ ์‹ ํ˜ธ ๊ธฐ๋ก ์‹คํ—˜ 84 III. ๊ฒฐ๋ก  86 3.1 PDMS ๊ธฐ๋ฐ˜์˜ ๋ฐ˜๋„์ฒด ๊ณต์ • ๊ฒฐ๊ณผ 86 3.1.1 PDMS ํ‘œ๋ฉด ๊ฐœ์งˆ ๊ฒฐ๊ณผ 86 3.1.2 ๊ธˆ์† ๋ฐ•๋ง‰๊ณผ PDMS ๊ณ„๋ฉด์˜ ์ ‘์ฐฉ๋ ฅ ์‹คํ—˜ ๊ฒฐ๊ณผ 86 3.1.3 ๊ธˆ์† ๋„์„  ์ œ์ž‘ ์‹คํ—˜ ๊ฒฐ๊ณผ 90 3.1.4 ์ธ์žฅ ๋ฐ ๊ตฝํž˜ ์‹คํ—˜ ๊ฒฐ๊ณผ 97 3.1.5 PDMS ํŒจํ„ฐ๋‹ ๊ธฐ๋ฒ• ๊ฒฐ๊ณผ 98 3.2 ๊ธฐ๋ก์šฉ ํ”Œ๋ผํ†  ์ „๊ทน ์ œ์ž‘ ๊ฒฐ๊ณผ 110 3.2.1 ์ „๊ทน 3D ํ”„๋กœํŒŒ์ผ ๊ฒฐ๊ณผ 111 3.2.2 ์ž„ํ”ผ๋˜์Šค ์ธก์ • ๊ฒฐ๊ณผ 114 3.2.3 ๊ณต๊ธฐ ๊ฐ‡ํž˜ ํ˜„์ƒ ๋ฐฉ์ง€ ๊ฒฐ๊ณผ 117 3.3 ์ž๊ทน์šฉ ๊ฒฝ์‚ฌ ์ „๊ทน ์ œ์ž‘ ๊ฒฐ๊ณผ 117 3.3.1 ์‹ ๋ขฐ์„ฑ ์‹คํ—˜ ๊ฒฐ๊ณผ 121 3.3.2 ์ „๋ฅ˜ ๋ฐ€๋„ ๋ถ„ํฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 122 3.4 PDMS๊ธฐ๋ฐ˜์˜ ๋‹ค์ธต ๊ธฐํŒ ์ œ์ž‘ ๊ฒฐ๊ณผ 124 3.4.1 ์ •์ „์šฉ๋Ÿ‰ ์ธก์ •๊ฒฐ๊ณผ 124 3.4.2 ๋ˆ„ํ™”(cross talk) ์žก์Œ 127 3.4.3 ๋ ˆ์ด์ €๋ฅผ ์ด์šฉํ•œ ์™ธํ˜• ์ ˆ๋‹จ ๋ฐ MEA ํšŒ์ˆ˜ 130 3.5 In Vivo ์‹คํ—˜ 132 3.5.1 ์ฒ™์ˆ˜ ์‹ ๊ฒฝ ์‹ ํ˜ธ ๊ธฐ๋ก ์‹คํ—˜ ๊ฒฐ๊ณผ 132 3.5.2 ํ›„๊ฐ๋ง์šธ ์‹ ๊ฒฝ ์‹ ํ˜ธ ๊ธฐ๋ก ์‹คํ—˜ ๊ฒฐ๊ณผ 135 IV. ํ† ์˜ 139 4.1 PDMS ํ‘œ๋ฉด์ฒ˜๋ฆฌ ๊ธฐ์ˆ  139 4.2 ์‚ฌ์ง„๊ณต์ •์„ ์œ„ํ•œ ์Œ์„ฑ ๊ฐ๊ด‘์ œ ์„ ํƒ 140 4.3 ์œ ๊ธฐ ์šฉ์•ก์— ์˜ํ•œ PDMS ๋ณ€ํ˜• 141 4.4 ์ฃผ๋ฌผ ๊ณต์ • ์ œ์•ˆ 141 4.5 ๊ธฐ๋ก์šฉ ํ”Œ๋ผํ†  ์ „๊ทน 142 4.6 ์ž๊ทน์šฉ ์™„๋งŒํ•œ ๊ฒฝ์‚ฌ ์ „๊ทน 142 4.7 ๋‹ค์ธต ๊ธฐํŒ ์ œ์ž‘์„ ์œ„ํ•œ ์—ฐ๊ฒฐํ†ต๋กœ 143 V. ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ 144 5.1 ๊ฒฐ๋ก  144 5.2 ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ 146 ์ฐธ๊ณ  ๋ฌธํ—Œ 148 Abstract 161Docto

    Integration eines Neuro-Sensors in ein Messsystem sowie Untersuchungen zur Unit-Separation

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    An der Universitรคt Rostock wurde im Rahmen einer Dissertation ein neuartiger CMOS (Complementary Metal Oxide Semiconductor) Sensorchip zur extrazellulรคren Analyse elektrisch aktiver biologischer Zellen eingesetzt. Dieser Chip besitzt auรŸer einem MEA (Multielektrodenarray) noch weitere FET (Field Effect Transistor)-basierte Sensoren zur Erfassung unterschiedlicher Zellparameter. Neben der Inbetriebnahme dieses Sensors wurden externe Hardware zur Messwerterfassung und Algorithmen zur Signalaufbereitung entworfen und realisiert. Das Ziel war die Schaffung eines Cell Monitor Systems (CMS) zur teilautomatisierten Nutzung des Silizium-basierten Sensorchips.At the University of Rostock a new hybrid CMOS (Complementary Metal Oxide Semiconductor) sensor chip has been applied to analyse biological electrogenic cells. This chip consists of a MEA (Multi Electrode Array) and several types of FET (Field Effect Transistor) based sensors to monitor substance dependent cell reactions in-vitro. The system consists of the actual sensor chip including a cell culture area, an external hardware platform for data acquisition and digital signal processing algorithms for signal conditioning. Finally a Cell Monitor System (CMS) for the semi-automatic data acquisition was realised to increase the efficiency of the sensor chip usage
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