Computational Neuroscience Group
Our lab uses mathematical models of synapses, neurons and networks to explain aspects of perception and behaviour. We consider models for the cortical pyramidal neurons and micro-circuitries which are being experimentally investigated in vivo and in vitro at our
Department 1
.
A further interest is the neuronal substrate of learning and memory. One question we are addressing is how state-action sequences can be learned from an ongoing stream of synaptic inputs and a single delayed feedback signal 2
. We also develop models of sensory processing and its interaction with cortical top-down signals. These explain experimental recordings from the visual cortex obtained while solving perceptual or classification tasks 3,
4
.
Our models try to highlight some key mechanisms by which the interaction of synapses and neurons enables our brains to deal with everyday tasks.
Highlights
Nacht der Forschung 2022 - Info
We were delighted to be among the 750 researchers from the University of Bern who presented their research to tens of thousands of people.
![NdFcollage](IMG/News/icons/NdFcollage.png)
Visitors to our stand got an insight into learning in neural networks and neuromorphic engineering by experiencing the dynamics of real biological neurons using our in-house electronic neuron circuit. The public could also compete against an AI by playing the puzzle game Tic Tac Toe, also known as Noughts and Crosses.
eLife Article: how natural-gradient descent enable efficient synaptic plasticity!
![Kreutzer2022](IMG/News/icons/Kreutzer2022.png)
"Natural-gradient learning for spiking neurons".
See Publications
eLife Article: Learning across three different global brain states!
![NDarXivFigbig](IMG/News/icons/NDarXivFigbig.png)
"Learning cortical representations through perturbed and adversarial dreaming".
See Publication
- More on GANs