networks of spiking neurons: a new approach to understanding mental retardation in down syndrome
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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 [email protected] Department of Anatomy, Physiology and Genetics - PowerPoint PPT PresentationTRANSCRIPT
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
Department of Anatomy, Physiology and Genetics
USUHS Medical School, Bethesda
Future Work - GoalFirst, 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
Cios & Galdzicki
Previous Work - Synaptic PlasticityPrevious Work - Synaptic Plasticity
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wij - weight between neurons - learning ratey - max value for decaytcorr - correlation time
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Correlation CoefficientTemporal Correlation Rule
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t j =0
t i =0
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t j =2
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Cios & Galdzicki
Previous Work – Modeling of SIPrevious Work – Modeling of SI Cios & Galdzicki
Previous Work – Modeling of SIPrevious Work – Modeling of SI
Cortical response – learning using TCRCortical response – no learning
Cios & Galdzicki
Previous WorkPrevious Work
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Network
Result
Modeling of SI: two regions stimulated together using temporal correlation rule
Pattern grouping
Cios & Galdzicki
Previous Work – LTP in MicePrevious Work – LTP in Mice
Tetanus 100 Hz for 1 sec
Tetanus 1Hz for 16 min
Cios & Galdzicki
Future WorkFuture Work
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Normal mice
DS mice
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Cios & Galdzicki
Future Work - HypothesisFuture Work - HypothesisNormal mice DS mice
Wij Wik Wij Wik Pattern grouping takes place Pattern grouping does not take place
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t2 t2
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Cios & Galdzicki
Future Work – Multi-electrode Array for Future Work – Multi-electrode Array for Verification of Pattern Grouping in DS MiceVerification of Pattern Grouping in DS Mice
From Egert et al., 1998
a) Day one, Hipp. slice of 200µm thick [800µm]
b) Day 10 in culture [300µm]
c) Day 23 in culture [100 µm]
Cios & Galdzicki
a) Spontaneous spiking activity (top)After stimulus (bottom)
b) Cross correlograms
c) Hippocampus slice after 23 days in culture