Prospective coding by cortical pyramidal neurons

• Applicant: Professor Matthew Larkum
• Subject Area: Cognitive, Systems and Behavioural Neurobiology
• Term: from 2015 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 267823436

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 suggested that learning on the level of a neuron predicts somatic spiking by dendritic inputs. So far, general experimental and theoretical research has tried to prove 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 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 prospectivecoding. This notion implies that the activity of a neuron predicts current, as well as future synaptic inputs. We hypothesize that both the basal and apical dendritic tree of a pyramidal neuron make independent predictions of the somatic spiking, based on within-network synapses to the basal tree and extrinsic synapses to the apical tree. The match between the independent predictions represents a high confidence signal that generates dendritic calcium spikes with a subsequent burst of somatic action potentials, which 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). Formalize the concept of prospectivecoding 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). 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). Test in vivo whether a dendritic calcium spike is representing the match between prediction signals or 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 somatosensory oddball paradigm with combined auditory and somatosensory cues.SP4: Error-correcting plasticity (Lead: Nevian lab). Verify the hypothesis invitro 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 will yield the formal framework in which the subsequent two experimental subprojects are embedded. They will be jointly designed and the results will be described in terms of a mathematical model.

• DFG Programme: Research Grants
• International Connection: Switzerland
• Cooperation Partners: Professor Dr. Thomas Nevian; Professor Walter Martin Senn, Ph.D.