Prof. Dr. Walter Senn is a computational neuroscientist recognized for his models on cortical computation that connect biological and artificial intelligence. He has a PhD in Mathematics from University of Bern and Freiburg i.Br., and was for research stays at Moscow Lomonossov University (with Prof. Y. Sinai), at the NIH and NYU (with Prof. Rinzel) and at the Hebrew University in Jerusalem (with I. Segev). Since 2006 he is Full Professor for Computational Neuroscience at the Institute of Physiology, University of Bern, where he is Co-Director since 2010.  Sennbuilds mathematical models of cognitive functions such as learning, memory, attention, perception and lately awareness and consciousness. His models capture morphological and biophysical properties of neurons, synapses and networks, characterized by a close qualitative match to the cortical architecture. He was pioneering models of spike-timing-dependent synaptic plasticity, and algorithms for reinforcement learning in populations of spiking neurons with delayed reward. He also introduced a theory of `learning by the dendritic prediction of somatic spiking’, with extensions to reward-based leaning in multi-compartment neurons. He developed ideas on how learning through error-backpropagation could be implemented by cortical microcircuits in the brain, and with dendritic errors enabling local plasticity rule. His recent work is devoted to the neuronal least action principle from which dynamic laws of neuron and synaptic plasticity are derived in a similar way as the law of motion is derived from the least action principle in physics. The theory links cortical microcircuits with behavioral error minimization, links to artificial intelligence and serves as a basis to design neuromorphic hardware. W. Senn is involved in various

Abstract: Online learning in cross-cortical self-attention circuits

“Deep neural networks (DNNs) outperform cortical network models in behavioural performance. DNNs also outperform cortical neuron models in terms of their statistical response properties. Yet, the alignment of DNNs with cortical circuits, cortical structures, and synaptic plasticity is rather limited. I present a functional bottom-up model of cortical pyramidal neurons and cortical circuits that is inspired by self- and cross-attention networks in artificial intelligence (AI). I show how cortical layer 2/3 pyramidal neurons can sustain a key-value memory that is queried by cortical layer 5 neurons. The AI-inspired self-attention is implemented as a gain modulation in cortical pyramidal neurons. Crucially, synapses in the cortical key-value memory are frozen, so that the network is learned purely online, without need of backpropagation through time (BPTT). I show how this online plasticity, in principle,could be implemented via dedicated cortical error neurons. I also show how reward-prediction errors (RPEs), calculated in basal ganglia, help in task-switching. Via gain modulation, the RPEs increase the signal-to-noise ratio in the cortical networks. RPEs further gate the recruitment of hippocampal memories and their inclusion in the cortical key-value memories. Overall, the model aligns well with cortical structures involved in cognition, and offers a mechanistic explanation of inference and learning.”