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.

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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!

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

eLife Article: Learning across three different global brain states!

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


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