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Neural Information Processing Group

We are concerned with the principles underlying information processing in biological systems. On the one hand we want to understand how the brain computes, on the other hand we want to utilize the strategies employed by biological systems for machine learning applications. Our research interests cover three thematic areas.

Models of Neuronal Systems:

Lupe [1]

In collaboration with neurobiologists and clinicians we study how the visual system processes visual information. Research topics include: cortical dynamics, the representation of visual information, adaptation and plasticity, and the role of feedback. More recently we became interested in how perception is linked to cognitive function, and we began to study computational models of decision making in uncertain environments, and how those processes interact with perception and memory.

Machine Learning and Neural Networks:

Lupe [2]

Here we investigate how machines can learn from examples in order to predict and (more recently) act. Research topics include the learning of proper representations, active and semisupervised learning schemes, and prototype-based methods. Motivated by the model-based analysis of decision making in humans we also became interested in reinforcement learning schemes and how these methods can be extended to cope with multi-objective cost functions. In collaboration with colleagues from the application domains, machine learning methods are applied to different problems ranging from computer vision, information retrieval, to chemoinformatics.

Analysis of Neural Data:

Lupe [3]

Here we are interested to apply machine learning and statistical methods to the analysis of multivariate biomedical data, in particular to data which form the basis of our computational studies of neural systems. Research topics vary and currently include spike-sorting and the analysis of multi-tetrode recordings, confocal microscopy and 3D-reconstruction techniques, and the analysis of imaging data. Recently we became interested in the analysis of multimodal data, for example, correlating anatomical, imaging, and genetic data.

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Selected Publications

Extending Integrate-and-fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite
Citation key Aspart2016
Author Aspart, F. and Ladenbauer, J. and Obermayer, K.
Pages e1005206
Year 2016
DOI DOI 10.1371/ journal.pcbi.1005206
Journal PLOS Computional Biology
Volume 12
Number 11
Abstract The collective dynamics of neuronal populations can be efficiently studied using single-compartment (point) model neurons of the integrate-and-fire (IF) type. Existing point neuron models are intrinsically not able to appropriately reproduce (i) the effects of dendrites on synaptic input integration or (ii) the modulation of neuronal activity due to an electric field, which strongly depends on the dendritic morphology. Weak electric fields, as generated endogenously or through transcranial electrical stimulation, have recently gained increased attention because of their ability to modulate ongoing neuronal activity. However, the underlying mechanisms are not well understood. Here, we extend the popular spiking point neuron model class to accurately reflect input filtering and weak electric field effects as present in a canonical spatially extended “ball-and-stick” (BS) neuron model. We analytically derive additional components for two major types of IF point neuron models to exactly reproduce the subthreshold somatic voltage dynamics of the BS model with arbitrary morphology exposed to an oscillating electric field. Also the spiking dynamics for suprathreshold fluctuating inputs is well reproduced by the extended point models. Through this approach we further show that the presence of a dendritic cable (i) attenuates the somatic subthreshold response to slowly-varying inputs and (ii) mediates spike rate resonance, or equivalently, pronounced spike to field coherence, in the beta and gamma frequency range due to an oscillatory weak electric field. Our point neuron model extension is simple to implement and well suited for studying the dynamics of populations with heterogeneous neuronal morphology and the effects of weak electric fields on population activity.
Bibtex Type of Publication Selected:main selected:structured selected:publications
Link to original publication [5] Download Bibtex entry [6]

Administrative Office
Groiss, Camilla
MAR 5042
Fon: +49 30 314 73442
Fax: +49 30 314 73121
Contact [9]

Consultation hours:
We 9am - 11am

Departmental reseach labs

  • Cognitive Systems [10]
  • Data Analytics & Cloud [11]
  • Future Internet & Media Technology [12]
  • Cyber-Physical Systems [13]

Collaborative projects in research and education

  • Bernstein Center for Computational Neuroscience [14]
  • Research Training Group "Sensory Computation in Neural Systems" [15]
  • Graduate School Mind and Brain [16]
  • International Master-Program in Computational Neuroscience [17]
  • Einstein Center Neuroscience [18]
  • Collaborative Research Center "Control of Self-Organizing Nonlinear Systems" [19]
  • SysMedAlcoholism: Alcohol Addiction: A Systems-Oriented Approach [20]
  • Science of Intelligence (DFG Research Cluster) [21]
  • Collaborative Research Center "Mechanisms and Disturbances in Memory Consolidation" [22]
  • SMARTSTART - training program in computational neuroscience [23]
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