pyramidal neurons; computational neuroscience; learning theory; synaptic plasticity; dendritic integration and spikes
Llera-Montero Milton, Sacramento João, Costa Rui Ponte (2019), Computational roles of plastic probabilistic synapses, in Current Opinion in Neurobiology
, 54, 90-97.
Wybo Willem A.M., Torben-Nielsen Benjamin, Nevian Thomas, Gewaltig Marc-Oliver (2019), Electrical Compartmentalization in Neurons, in Cell Reports
, 26(7), 1759-1773.
Larkum Matthew E., Petro Lucy S., Sachdev Robert N. S., Muckli Lars (2018), A Perspective on Cortical Layering and Layer-Spanning Neuronal Elements, in Frontiers in Neuroanatomy
, 12, 1-9.
Sacramento Joao, Ponte CostaRui, BengioYoshua, SennWalter (2018), Dendritic cortical microcircuits approximate the backpropagation algorithm, in Advances in Neural Information Processing Systems 31 (NIPS 2018)
Suzuki Mototaka, Larkum Matthew E. (2017), Dendritic calcium spikes are clearly detectable at the cortical surface, in Nature Communications
, 8(1), 276-276.
Micallef Andrew H., Takahashi Naoya, Larkum Matthew E., Palmer Lucy M. (2017), A Reward-Based Behavioral Platform to Measure Neural Activity during Head-Fixed Behavior, in Frontiers in Cellular Neuroscience
, 11, 1-8.
Takahashi Naoya, Oertner Thomas G., Hegemann Peter, Larkum Matthew E. (2016), Active cortical dendrites modulate perception, in Science
, 354(6319), 1587-1590.
Brea Johanni, Gaál Alexisz Tamás, Urbanczik Robert, Senn Walter (2016), Prospective Coding by Spiking Neurons, in PLOS Computational Biology
, 12(6), 1-25.
Schiess Mathieu, Urbanczik Robert, Senn Walter (2016), Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites, in PLOS Computational Biology
, 12(2), 1-18.
Vladimirskiy Boris, Urbanczik Robert, Senn Walter (2015), Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding, in PLOS ONE
, 10(12), 1-19.
This Lead Agency proposal is a continuation of the personal SNF-grant of W. Senn on the theory of dendritic computation. In the running project period a key insight has been achieved by suggesting that learning on the level of a neuron implies the prediction of somatic spiking by the dendritic input (Urbanczik & Senn, Neuron 2014). This hypothesis introduces a paradigm shift in viewing dendritic computation and opens the door for a putative computational understanding of the signal processing in pyramidal neurons. So far, general experimental and theoretical research has tried to prove more and more complex nonlinearities in the dendritic processing of synaptic signals. But the mere description of a neuron as a complex input-output element gives only little insight into what dendrites are actually computing. In contrast, regarding neurons, and in particular pyramidal neurons, as intrinsic prediction elements links single neuron processing to a possible broader computational task.The current proposal extends this single neuron hypothesis by the notion of prospective coding. This notion introduces the idea that the activity of a neuron not only predicts current, but also future synaptic inputs. The proposal takes account of the bipolar dendritic morphology of a pyramidal neuron with a basal and apical dendritic tree. We hypothesize that both the basal and apical tree make independent predictions of the somatic spiking. These predictions are based on within-network projections to the basal tree, and extrinsic or top-down connections to the apical tree. The match between the independent predictions represents a high confidence signal that generates a dendritic calcium spike with a subsequent burst of somatic action potentials. These bursts can then be fed back to the presynaptic neurons that can use them as a teaching signal for their own up-stream synapses. The theory and its experimental verification is divided into 4 subprojects:SP1: Prospective coding (Lead: Senn lab, 1 PhD). Formalize the concept of prospective coding and show that the independent prediction of future input by the basal and dendritic trees is equivalent to a Bayesian cue combination problem.SP2: Backpropagation in time (Lead: Senn lab, 1 postdoc). Show that the matching signal for the prediction of future events can be used to train hidden neurons that contribute to these predictions. Apply the theory to the non-Markovian sequence learning problem and to a simplified ball catching problem.SP3: Novelty coding (Lead: Larkum lab, 1 postdoc - DFG). Test in vivo whether a dendritic calcium spike is representing the match between prediction signals or the match between novelty signals generated by the basal and apical trees. Verify the prediction of the cue combination hypothesis by measuring the neuronal responses to a somato-sensory oddball paradigm with combined auditory and somato-sensory cues.SP4: Error-correcting plasticity (Lead: Nevian lab, 1 PhD). Verify the hypothesis in vitro whether synaptic plasticity both in excitatory and inhibitory plasticity is error-correcting and hence non-Hebbian. Test whether plasticity involving calcium spikes has a longer induction time window as predicted by prospective coding.The first two subprojects are yield the formal framework in which the subsequent two experimental subprojects are embedded. The experiments are formulated such that, ideally, they verify or falsify the theory inspired hypotheses. They will be jointly designed and the results will be described in terms of a mathematical model.