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Networks of Spiking Neurons: A New Approach to Understanding Mental Retardation in Down Syndrome

Networks of Spiking Neurons: A New Approach to Understanding Mental Retardation in Down Syndrome. Krzysztof (Krys) Cios and Zygmunt Galdzicki Computer Science and Engineering Department UC Denver Krys.Cios@cudenver.edu Department of Anatomy, Physiology and Genetics

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Networks of Spiking Neurons: A New Approach to Understanding Mental Retardation in Down Syndrome

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  1. Networks of Spiking Neurons: A New Approach to Understanding Mental Retardation in Down Syndrome Krzysztof (Krys) Cios and Zygmunt Galdzicki Computer Science and Engineering Department UC Denver Krys.Cios@cudenver.edu Department of Anatomy, Physiology and Genetics USUHS Medical School, Bethesda zgaldzicki@usuhs.mil

  2. Cios & Galdzicki Future Work - Goal First, we propose to model the hippocampal circuit in a rodent model of DS-trisomy 16 mouse (Ts65Dn), using our previous approach with networks of spiking neurons. Second, we will experimentally verify our hypothesis that DS mice have impaired neuronal hippocampal network, by using multi-electrode arrays. Third, we will propose a novel design of pharmacological or microsurgical intervention to restore functional plasticity by administration of neurotrophic factors or neuroactive drugs in an effort to restore normal cognitive function in DS patients through modeling and identification of incorrect or de-optimized neural connections in the mouse model of DS. • Galdzicki Z, Siarey R, Pearce R, Stoll J, Rapoport SI.  2001. On the cause of mental retardation in Down syndrome: extrapolation from full and segmental trisomy 16 mouse models. Brain Research Reviews 35:115-145              • Sala D.M., Cios K.J. and Wall J.T. 1997. A spatio-temporal computer model of dynamic organization properties of the adult primate somatosensory system. In:Proc. of 1997 Int. Conf. On Neural Information Processing and Intelligent Information Systems, Dunedin, New Zealand, November, Springer:153-156 • Sala D.M. and Cios K.J. 1999. Solving graph algorithms with networks of spiking neurons. IEEE Tr. on Neural Networks, 10(4): 953-957

  3. Cios & Galdzicki i j k E i t 0 = i E j t =2 t =0 j j E k t =0 t =7 k k Previous Work - Synaptic Plasticity Temporal Correlation Rule Correlation Coefficient wij - weight between neurons - learning ratey - max value for decaytcorr - correlation time

  4. Cios & Galdzicki Previous Work – Modeling of SI

  5. Cios & Galdzicki Previous Work – Modeling of SI Cortical response – no learning Cortical response – learning using TCR

  6. Cios & Galdzicki 5 8 7 7 2 5 4 6 5 6 2 6 1 10 1 9 3 7 5 5 8 8 3 6 9 8 4 3 4 5 7 2 5 2 6 1 1 9 3 5 3 8 3 4 Previous Work Modeling of SI: two regions stimulated together using temporal correlation rule Pattern grouping Network Result

  7. Cios & Galdzicki Previous Work – LTP in Mice Tetanus 100 Hz for 1 sec Tetanus 1Hz for 16 min

  8. Cios & Galdzicki fn 1 PSP 0.5 fn > fa fa 0 i 0 10 20 30 Normal mice DS mice j k Tn i Tij Tik Tij Tik Tn < Ta j k Ta Future Work

  9. Cios & Galdzicki Future Work - Hypothesis Normal mice DS mice i i t3 t4 j j t2 t2 k k t1 t1 WijWik Wij Wik  Pattern grouping takes place Pattern grouping does not take place

  10. Cios & Galdzicki Future Work – Multi-electrode Array for Verification of Pattern Grouping in DS Mice a) Day one, Hipp. slice of 200µm thick [800µm] b) Day 10 in culture [300µm] c) Day 23 in culture [100 µm] a) Spontaneous spiking activity (top) After stimulus (bottom) b) Cross correlograms c) Hippocampus slice after 23 days in culture From Egert et al., 1998

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