标题： 生物和人工神经网络中神经编码的动力学 显示英文标题

标题： The dynamics of neural codes in biological and artificial neural networks

摘要： 不断加深对大脑如何处理信息的理解仍然是神经科学领域的一个关键挑战。 本论文结合了三种不同的方法来研究神经网络的动力学及其编码表示：一种计算方法，它基于神经元及其网络的基本生物学特征，构建能够模拟其结构和动力学的有效模型；一种机器学习方法，它与大脑网络的功能能力形成类比，使我们能够推断出解决某些输入处理任务所需的动力学和编码特性；以及最后一种理论分析，将带我们进入“临界”大脑的迷人假设，作为可以解释数百万神经元相互作用所产生的涌现集体特性的数学基础。 伴随着物理学，我们进入神经科学领域，以解释 across 脑区的准普遍标度性质的存在，并着手量化其动力学距离临界点的距离。 接下来，我们进入人工智能的领域，在那里，同样的临界现象理论将证明对于解释残差计算机预测能力中生物启发的可塑性规则的效果非常有用。 在我们的旅程中途，我们探讨了外部刺激的神经表示概念，揭示了神经网络的动力学状态与这种表示流形的最佳拓扑特性之间的意外联系。 论文最后探讨了嗅觉皮层在气味编码过程中出现的表征漂移这一独特问题，揭示了可能解释这一最近观察到现象的潜在突触可塑性机制。 显示英文摘要

摘要： Advancing our knowledge of how the brain processes information remains a key challenge in neuroscience. This thesis combines three different approaches to the study of the dynamics of neural networks and their encoding representations: a computational approach, that builds upon basic biological features of neurons and their networks to construct effective models that can simulate their structure and dynamics; a machine-learning approach, which draws a parallel with the functional capabilities of brain networks, allowing us to infer the dynamical and encoding properties required to solve certain input-processing tasks; and a final, theoretical treatment, which will take us into the fascinating hypothesis of the "critical" brain as the mathematical foundation that can explain the emergent collective properties arising from the interactions of millions of neurons. Hand in hand with physics, we venture into the realm of neuroscience to explain the existence of quasi-universal scaling properties across brain regions, setting out to quantify the distance of their dynamics from a critical point. Next, we move into the grounds of artificial intelligence, where the very same theory of critical phenomena will prove very useful for explaining the effects of biologically-inspired plasticity rules in the forecasting ability of Reservoir Computers. Halfway into our journey, we explore the concept of neural representations of external stimuli, unveiling a surprising link between the dynamical regime of neural networks and the optimal topological properties of such representation manifolds. The thesis ends with the singular problem of representational drift in the process of odor encoding carried out by the olfactory cortex, uncovering the potential synaptic plasticity mechanisms that could explain this recently observed phenomenon.