Laser-deinsulated, printed-circuit electrodes integrated into the floor of culture chambers have been used to monitor the spontaneous activity of mouse spinal monolayer cell cultures. This technique has allowed a multisite analysis of activity over long periods of time in closed chambers. In 63 cultures investigated 3-5 weeks after seeding, 89% included single- or multiunit bursting. Based on a subset of 40 cultures in which all electrodes were sequentially scanned, bursting was found on 41% of the active electrodes (approximately 38% of all units monitored). A total of 35% of the electrodes monitoring spontaneous bursting activity revealed rhythmic sequences that were usually coupled among multiple electrodes. Although most of this coupling was in-phase, three out of 40 cultures exhibited antiphasic bursting. In all cases where coupling was observed, each electrode monitored different burst compositions, demonstrating that the activity was generated by different units. Some rhythmic patterns persisted for over 12 hr and were observed in 400 mm2 monolayer cultures, as well as in much smaller 3 mm2 adhesion islands. The addition of 10 mM MgCl2 consistently blocked both random and patterned (i.e., bursting) spontaneous activity at all recording sites. Strychnine (10(-6) M) typically increased firing frequencies and either disrupted pretest bursting or generated rhythmic activity from random phasic patterns. In certain cases, strychnine also blocked activity on specific electrodes, indicating that glycine is not the only inhibitory transmitter involved. The spontaneous appearance of rhythmic activity in low-density, monolayer cell cultures established from dissociated and randomly seeded spinal tissue can be explained by one or a combination of two hypotheses: an inherent specificity of some interconnections in developing mammalian cultures and the generation of organized activity by random circuits at certain stages of complexity.
Department of Electrical hgineering, II. FEATURE EXTRACT10 Nlhispapcrprcsentstbealgori~andirqplemnta~onofthenal-timefeatllre extraction and pattctn recognition for signals from culturtd living neural-cell network. FkaWc extraction and pattem recognition are achieved by the application of data compression techniques and an artificial neural network (ANN). Iht implmtation cmists of an 80386-based F C and a TMS32Oc30 digital signal processing @SP) card that is inserted into an expansion dot in the PC. Off-line trainfng of the ANN is done in the PC. R c a l -t i m e~o f t h e p u l s e p a m r n s~m t b e~v i n g n e u r a l c c l l s i s~ in tbe DSP sub-system Iht recognition results are sent from the DSP subsystem to the PC in real time for displaying and recording. -A living neural cell fires by sending electrical impulses to its neighbors through synaptic junctions. With advanced VLSI trchnology, neuroscientists areabletomoahoraadstimulatetheactionpotentialsofcultund,livingne~al cells [1].lbstpdythcbchaviorofneurons, itisessentialu,recognizethefiring pat" especially in real time. & " of tbt fyino patterns can be achieved by the application of a speach c c m g d t h algorithm which is basically a template-matching system. Butitrequinsin~~ecalculationtocomparetheincomingpattans withall t b e~l a t c s t o f~t h e leastma. Italsorequireslargeamouatsofmmoryto store all the sampled data before they are converted to linear predictive code o. To be time-and storage-efficient, a different algorithmis introduced in which data compnssion is done in real time and an ANN [23 is applied to This paper describes the application of an ANN and a digital signal processing @SP) system [3] for faing-pattem recognition. Due to the nature of arallel proccsstng of a neural network and the calculating power of the D& chip, real-& recognition of the signals from the living neural cells becomcs realizable.The irnpltmentation includes two phases. The training phase teaches the supervised ANN to recognize sample pattems. The recognition phase uses the well-trained ANN to idenw pulse trains. F e a m extraction which converts, condenses, and normalizes the incoming signals to a predefined format is used to extract the feature of the firing patterns. The feature of the f h g patterns are catalogued into 16 templates which are U@ to train the supervised ANN. "he ANN is a two-layer multiple-input, multiple-output (MIMO) supervised network. It has 32 nodes in b e input layer, 12 nodes in the hidden layer, and 4 nodes in the output layer.Since binary codes are used as the desired values, four nodes in the output layer can be used to represent 1 6 templates. The off-line recursive training is implemented in the PC.After training, the weights of the trained ANN is used by the ANN of the s a m configuration in the recognition system. The recognition system which is implemnted in the DSP subsystem includes real-time feature extraction and ANN recognition. Feature extraction which is used to prepare the template patterns is also applied in the recognit...
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