Prospective Coding by Cortical Pyramidal Neurons

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 makes 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 are 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 yielding 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.

Principal Investigators
Larkum, Matthew Prof. Dr. (Details) (Neuronal Plasticity)

Duration of Project
Start date: 02/2015
End date: 05/2018

Research Areas
Cognitive Neuroscience, Systemic Neuroscience, Computational Neuroscience, Behaviour

Last updated on 2021-12-10 at 12:47