Prospective coding by cortical pyramidal neurons

这个牵头机构的建议是W.关于树状计算理论。在项目运行期间，一个关键的见解表明，神经元水平上的学习可以预测树突输入的躯体尖峰。到目前为止，一般的实验和理论研究都试图证明复杂的非线性突触信号的树突处理。但是，仅仅把神经元描述为一个复杂的输入-输出元件，只能让我们对树突实际上在计算什么知之甚少。相比之下，将神经元视为内在预测元素将单个神经元处理与可能的更广泛的计算任务联系起来。这个概念意味着神经元的活动预测当前以及未来的突触输入。我们假设，锥体神经元的基底和顶端树突树的体细胞尖峰的独立预测，基于内网络突触的基底树和外部突触的顶端树。独立预测之间的匹配代表了一个高置信度的信号，该信号产生树突状钙尖峰，随后爆发体细胞动作电位，然后可以反馈给突触前神经元，突触前神经元可以将其用作自己上游突触的教学信号。该理论及其实验验证分为4个子项目：SP1：前瞻性编码（领导：Senn实验室）。形式化前瞻编码的概念，并证明由基底树和树突树对未来输入的独立预测等价于贝叶斯线索组合问题。SP2：时间上的反向传播（领导：Senn实验室）。证明预测未来事件的匹配信号可以用来训练有助于这些预测的隐藏神经元。将理论应用于非马尔可夫序列学习问题和简化的接球问题。SP3：新奇编码（领导：Larkum实验室）。在体内测试树突状钙峰是否代表预测信号之间的匹配或由基底树和顶端树产生的新奇信号之间的匹配。通过测量神经元对具有听觉和躯体感觉组合线索的躯体感觉古怪范例的反应来验证线索组合假说的预测。SP 4：纠错可塑性（牵头：Nevian实验室）。在体外验证兴奋性和抑制性的突触可塑性是否是纠错的，因此非赫布假说。测试是否有一个更长的诱导时间窗口，包括钙尖峰可塑性预测的前瞻性编码。前两个子项目将产生正式的框架中，随后的两个实验子项目嵌入。它们将被联合设计，结果将以数学模型的形式描述。

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.