Aug
12
Mon
2013
Plenary Talk: Realistic modeling-new insight into the functions of the cerebellar network @ Amriteshwari Hall
Aug 12 @ 1:37 pm – 2:24 pm

egidioEgidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy


Realistic modeling: new insight into the functions of the cerebellar network

Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work.  In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.

This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.

Correspondence should be addressed to

Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it

Acknowledgments

This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).

Egidio

Aug
13
Tue
2013
Plenary Talk: Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law @ Sathyam Hall
Aug 13 @ 1:20 pm – 2:00 pm

karmeshuKarmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.


Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law

The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal  ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate  the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory  rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal  ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits  power law.