Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | Next revision Both sides next revision | ||
|
research [2012/07/10 09:13] katha |
research [2012/11/02 13:51] group |
||
|---|---|---|---|
| Line 2: | Line 2: | ||
| {{ :gruppenphoto8zu3v2.jpg?nolink|}} | {{ :gruppenphoto8zu3v2.jpg?nolink|}} | ||
| \\ | \\ | ||
| + | ====Subthreshold resonance==== | ||
| + | sdfsdf \\ | ||
| + | \\ | ||
| + | Investigators: Eric Reifenstein \\ | ||
| + | Keywords: [Grid cells] [Spatial memory] \\ | ||
| ====Phase precession==== | ====Phase precession==== | ||
| When a rat explores its environment, so called grid cells in the medial entorhinal cortex show increased activity at specific locations that constitute a regular hexagonal grid. As the rat enters and progresses through one of these "grid fields", spikes occur at successively earlier phases in the LFP's theta rhythm. This phenomenon is called phase precession. We employ data analysis and modeling to investigate the mechanisms and benefits of grid-cell phase precession. \\ | When a rat explores its environment, so called grid cells in the medial entorhinal cortex show increased activity at specific locations that constitute a regular hexagonal grid. As the rat enters and progresses through one of these "grid fields", spikes occur at successively earlier phases in the LFP's theta rhythm. This phenomenon is called phase precession. We employ data analysis and modeling to investigate the mechanisms and benefits of grid-cell phase precession. \\ | ||
| Line 8: | Line 13: | ||
| Keywords: [Grid cells] [Spatial memory] \\ | Keywords: [Grid cells] [Spatial memory] \\ | ||
| ====Auditory computation in the grasshopper==== | ====Auditory computation in the grasshopper==== | ||
| - | Grasshoppers evolved an auditory system for two reasons: predator avoidance and partner selection. Generally speaking, we study how sound information that arrives at a grasshopper's ear is transformed into a neuronal signal, and how this signal in turn is transformed to a sparse, efficient code. \\ | + | Grasshoppers evolved an auditory system for two reasons: predator avoidance and partner selection. We study how sound information that arrives at a grasshopper's ear is transformed into a neuronal signal, and how this signal in turn is transformed into a sparse, efficient code. \\ |
| On an average day, jumping from the shade into the sun can easily change a grasshopper's body temperature by 10°C. Given that every neuron's ion channel dynamics are modulated by temperature, maintenance of neuronal functionality across behaviorally relevant temperatures is a non-trivial task. We investigate how neuronal activity can be made robust against temperature fluctuations, even in the presence of temperature-modulated ion channel dynamics.\\ | On an average day, jumping from the shade into the sun can easily change a grasshopper's body temperature by 10°C. Given that every neuron's ion channel dynamics are modulated by temperature, maintenance of neuronal functionality across behaviorally relevant temperatures is a non-trivial task. We investigate how neuronal activity can be made robust against temperature fluctuations, even in the presence of temperature-modulated ion channel dynamics.\\ | ||
| \\ | \\ | ||
| Investigators: Janina Hesse, Frederic Roemschied \\ | Investigators: Janina Hesse, Frederic Roemschied \\ | ||
| Keywords: [Coding] [Neuronal morphology] [Temperature compensation] [Single-neuron modeling] \\ | Keywords: [Coding] [Neuronal morphology] [Temperature compensation] [Single-neuron modeling] \\ | ||
| + | ====Influence of ion channel cooperativity on neuronal function sdf sdfk sldkfj sdf==== | ||
| + | |||
| + | \\ | ||
| + | Investigators: Ekaterina Zhuchkova, Fabian Santi \\ | ||
| + | Keywords: [Coding] [ion channels] [cooperativity] \\ | ||
| ====Dendritic computation==== | ====Dendritic computation==== | ||
| Virtually all synaptic input to a neuron arrives at the dendritic tree. A great number of experimental studies have shown that dendrites are endowed with a wide variety of voltage-dependent ion channels. These active membrane properties can underlie a variety of dynamical behaviors, such as backpropagation of somatic action potentials, dendritic spike initiation, and membrane potential resonances and oscillations. Using numerical and analytical approaches, we study how dendritic membrane properties, together with their morphology, affect the integration of synaptic input and thereby shape the computations that single neurons can perform.\\ | Virtually all synaptic input to a neuron arrives at the dendritic tree. A great number of experimental studies have shown that dendrites are endowed with a wide variety of voltage-dependent ion channels. These active membrane properties can underlie a variety of dynamical behaviors, such as backpropagation of somatic action potentials, dendritic spike initiation, and membrane potential resonances and oscillations. Using numerical and analytical approaches, we study how dendritic membrane properties, together with their morphology, affect the integration of synaptic input and thereby shape the computations that single neurons can perform.\\ | ||
