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.


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.


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!


"Natural-gradient learning for spiking neurons".
See Publications

eLife Article: Learning across three different global brain states!


"Learning cortical representations through perturbed and adversarial dreaming".
See Publication - More on GANs

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