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Publications of Computation in Neural Circuits

Selected Publications

Paper RD 20170410

Synaptic patterning and the timescales of cortical dynamics.

Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. Duarte RC., Seeholzer A., Zilles K., Morrison A. Current Opinion in Neurobiology 43:156-165 (2017): Synaptic patterning and the timescales of cortical dynamics. …

Paper EH 20161226

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Hagen E., Dahmen D., Stavrinou ML., Lindén H., Tetzlaff T., van Albada SJ., Grün S., Diesmann M., Einevoll GT. Cerebral Cortex 26:4461-4496 (2016): Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks …

Paper PW 20160803

Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS.

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. Weidel P., Djurfeldt M., Duarte RC., Morrison A. Frontiers in Neuroinformatics 10:31 (2016): Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS. …

Paper AM 20160526

Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. Diaz-Pier S., Navaeu M., Butz-Ostendorf M., Morrison A. Frontiers in Neuroanatomic 10:57 (2016): Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity. …

Paper MD 20160518

Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study

High-level brain function, such as memory, classification, or reasoning, can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy-efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. Pfeil T., Jordan J., Tetzlaff T., Grübl A., Schemmel J., Diesmann M., Meier K. Physical Review X 6:021023 (2016): Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study …

Paper PW 20160803

Dynamic stability of sequential stimulus representations in adapting neuronal networks.

The ability to acquire and maintain appropriate representations of time-varying, sequential stimulus events is a fundamental feature of neocortical circuits and a necessary first step toward more specialized information processing. Duarte RC., Morrison A. Frontiers in Neuroscience 8:124 (2014): Dynamic stability of sequential stimulus representations in adapting neuronal networks. …

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