Modelling the formation of new memories in the human brain

阿尔茨海默病等神经系统疾病突显了学习和记忆在日常生活中的重要性，其早期症状包括忘记最近发生的事件、名字和面孔。最近的实验工作涉及因临床原因而植入深度电极的癫痫患者的单个神经元记录，该工作表明，当形成新的记忆时，人脑中的单个神经元改变其放电以联系联系(Ison，Quiroga&Fry，Neuron 2015)。此外，DeFalco，Ison，Fry&Quiroga(自然通信，2016)最近报告说，内侧颞叶(MTL)的神经元参与物品之间联系的长期表征。这些发现，加上最近对啮齿动物和非人类灵长类动物的研究证据表明，记忆是在稀疏的神经元集合中编码的。然而，传统的记忆模型预测MTL只参与记忆的初始表征(如Norman&O‘Reilly，Souol)。2003年版)。计算模型可以揭示这个难题，从而更好地理解大脑代表记忆的方式，并可能导致关于患有神经系统疾病的患者的记忆如何恶化的新想法。该项目旨在开发具有数据约束规则的神经网络模型，以实现峰时依赖的可塑性。该项目的第一阶段将优先培训候选人在神经科学领域(重点是记忆)、建立日益复杂的神经模型以及熟悉机器学习技术。第二阶段将侧重于模拟模式之间的关联的形成(目标1)。在接下来的阶段，该模型将被扩展到包括巩固阶段，以考虑记忆中的长期联系(目标2)。研究方法，包括将被研究的工程和物理科学中的新知识或技术。网络模型将是一个由兴奋性神经元和抑制性神经元组成的随机连接的递归神经网络。大多数参数，包括短期突触可塑性的突触连接，将从实验数据中提取(参见Pokorny，Ison，Rao，Legenstein，Papadimitriou&Maass，Cer Cortex 2019)。将使用Nest模拟器(Gewaltig&Diesman，2007)或Brian(Swood，Brette&Goodman，2019)在Python语言中进行网络模拟。数据分析将在Python或MatLab中执行。将评估扩大网络规模以模拟的可能性，并可能使用GPU实施。由于其跨学科性质，该项目与EPSRC研究主题(医疗保健技术、数学科学)以及其他研究理事会优先领域(例如BBSRC健康老龄化)都相关。重要的是，它非常符合EPSRC对医疗保健应用的愿景，它围绕着英国在计算和数学科学方面的研究优势建立了临界质量。主要合作者是诺丁汉大学数学学院的Stephen Coombes教授和奥地利TU Graz的理论计算机科学研究所的Wolfgang Maass教授。其他潜在的合作者包括哈佛医学院的加布里埃尔·克里曼教授和加州大学洛杉矶分校医学中心的伊扎克·弗里德教授。

The importance of learning and memory in our everyday lives is highlighted by neurological conditions such as Alzheimer's disease, whose early symptoms include forgetting about recent events, names and faces. Recent experimental work involving single neuron recordings from epilepsy patients implanted with depth electrodes for clinical reasons has shown that individual neurons in the human brain change their firing to link associations when a new memory is formed (Ison, Quiroga & Fried, Neuron 2015). Moreover, DeFalco, Ison, Fried & Quiroga (Nature Communications, 2016) recently reported that neurons in the medial temporal lobe (MTL) are involved in long-term representation of associations between items. These findings, together with recent evidence from work on rodents and non-human primates, suggest that memories are encoded in sparse assemblies of neurons. However, conventional memory models predict that the MTL is only involved in the initial representation of memories (e.g. Norman & O'Reilly, Psychol. Rev. 2003). Computational models can shed light into this conundrum, leading to a better understanding of the way in which the brain represents memories and potentially leading to novel ideas about how memories deteriorate in patients suffering from neurological disorders.The project aims at developing neural network models with data-constrained rules for spike-timing dependent plasticity. The first stage of the project will prioritise the training of the candidate in the area of neuroscience (with emphasis on memory), in building neural models of increasing complexity, and on familiarising with machine learning techniques. The second stage will be focused on simulating the formation of associations between patterns (Objective 1). In a subsequent stage, the model will be extended to incorporate a consolidation phase to give account for long-term associations in memory (Objective 2).The research methodology, including new knowledge or techniques in engineering and physical sciences that will be investigated The network model will be a randomly connected recurrent neural network consisting of excitatory and inhibitory neurons. Most of the parameters, including synaptic connections for short-term synaptic plasticity, will be drawn from experimental data (see e.g. Pokorny, Ison, Rao, Legenstein, Papadimitriou & Maass, Cer Cortex 2019). Network simulations will be conducted in Python, using either the NEST Simulator (Gewaltig & Diesman, 2007) or Brian (Stimber, Brette & Goodman, 2019). Data analyses will be performed in Python or MATLAB. The possibility of extending the size of the networks to simulate will be evaluated and potentially implemented using GPUs.Due to its interdisciplinary nature, the project is relevant both to EPSRC Research Themes (Healthcare Technologies, Mathematical sciences) as well as other Research Council priority areas (e.g. BBSRC Healthy Ageing). Importantly, it fits extremely well with the vision of the EPSRC to healthcare applications by building critical mass around UK research strengths in computational and mathematical sciences.Key collaborators are Prof. Stephen Coombes (School of Maths, University of Nottingham) and Prof. Wolfgang Maass (Institute of Theoretical Computer Science, TU Graz, Austria). Other potential collaborators include Prof. Gabriel Kreiman (Harvard Medical School) and Prof. Itzhak Fried (UCLA Medical Center).