Topic: synaptic efficacies

Inhibitory interneurons come in different classes and form intricate circuits. While our knowledge of these circuits has advanced substantially over the last decades, it is not fully understood how the structure of these circuits relates to their function. I will present some of our recent attempts to “understand” the structure of interneuron circuits by means of computational modeling. Surprisingly (at least for us), we found that prominent features of inhibitory circuitry can be accounted for by an optimisation for excitation-inhibition (E/I) balance. In particular, we find that such an opti

Brains are energy-hungry and evolution has driven them to work in an energy-efficient manner. In food restriction experiments, Padamsey et al. [1] have recently observed that individual neurons trade off energy consumption and information transmission. Specifically, neurons appear to reduce energy consumption by changing their integration properties through the adaptation of membrane resistance, resting potential, and synaptic efficacies, which comes at the cost of reduced information transmission. Furthermore, they found that individual neurons assume a broad range of values of these paramete

Perception relies on spatio-temporal activity patterns. Also, within the brain computations plau- sibly generate and exploit temporal structures. It is, however, unknown how neuronal systems develop selectivity for spatio-temporal patterns. Furthermore, learning should preserve already acquired contents while becoming selective to new contents. It is also not known by what mech- anisms synapses could stabilize previously stored memories of spatio-temporal patterns while they remain plastic and contribute to further learning. Here initially, the mechanisms for neurons to learn these patterns ar