Prospective coding by cortical pyramidal neurons

皮质锥体神经元的前瞻性编码

• 批准号: 267823436
• 负责人: Professor Matthew Larkum
• 金额: --
• 依托单位: Institut für Physiologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/267823436?language=en
• 关键词: Prospective; coding; 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 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.

这个领导机构的提案是W. Senn在树突计算理论方面的snf个人资助的延续。在运行项目期间，一个关键的见解表明，在神经元水平上的学习可以预测树突输入的体细胞尖峰。到目前为止，一般的实验和理论研究都试图证明树突处理突触信号的复杂非线性。但是，仅仅把神经元描述成一个复杂的输入输出元素，并不能让我们深入了解树突实际上在计算什么。相反，将神经元视为内在的预测元素，将单个神经元的处理与可能更广泛的计算任务联系起来。目前的建议通过前瞻性编码的概念扩展了这种单神经元假设。这个概念意味着神经元的活动可以预测当前和未来的突触输入。我们假设锥体神经元的基底树突树和顶端树突树都能独立预测体细胞尖峰，基于网络内突触到基底树和外部突触到顶端树。独立预测之间的匹配代表了一个高可信度的信号，该信号产生树突钙峰值，随后产生体细胞动作电位的爆发，然后可以反馈给突触前神经元，突触前神经元可以将它们作为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（牵头：Senn实验室）。形式化了前瞻性编码的概念，并证明了基树和树突状树对未来输入的独立预测等同于贝叶斯线索组合问题。SP2：时间反向传播（Lead: Senn lab）。证明用于预测未来事件的匹配信号可以用于训练有助于这些预测的隐藏神经元。将该理论应用于非马尔可夫序列学习问题和一个简化的接球问题。SP3：新颖性编码（领导：Larkum实验室）。在体内测试树突钙峰是否代表了预测信号之间的匹配，还是代表了基树和顶树产生的新奇信号之间的匹配。通过测量神经元对结合听觉和体感线索的怪异范式的反应，验证线索组合假说的预测。SP4：纠错可塑性（Lead: Nevian lab）。在体外验证兴奋性和抑制性可塑性中的突触可塑性是否具有纠错性，因此是非hebbian。验证钙峰相关的可塑性是否如前瞻编码所预测的具有更长的诱导时间窗。前两个子项目将产生正式的框架，随后的两个实验性子项目将嵌入其中。他们将共同设计，结果将用数学模型来描述。