Aitken Kyle, Campagnola Luke, Garrett Marina, Olsen Shawn, Mihalas Stefan
Center for Data-Driven Discovery for Biology, Allen Institute, Seattle, WA 98109, USA.
Allen Institute for Brain Science, Seattle, WA 98109, USA.
bioRxiv. 2024 Apr 27:2023.08.16.553635. doi: 10.1101/2023.08.16.553635.
Since environments are constantly in flux, the brain's ability to identify novel stimuli that fall outside its own internal representation of the world is crucial for an organism's survival. Within the mammalian neocortex, inhibitory microcircuits are proposed to regulate activity in an experience-dependent manner and different inhibitory neuron subtypes exhibit distinct novelty responses. Discerning the function of diverse neural circuits and their modulation by experience can be daunting unless one has a biologically plausible mechanism to detect and learn from novel experiences that is both understandable and flexible. Here we introduce a learning mechanism, (FMSs), through which a network response that encodes novelty emerges from unsupervised multiplicative synaptic modifications depending only on the presynaptic or both the pre- and postsynaptic activity. FMSs stand apart from other familiarity mechanisms in their simplicity: they operate under continual learning, do not require specialized architecture, and can distinguish novelty rapidly without requiring feedback. Implementing FMSs within an experimentally-constrained model of a visual cortical circuit, we demonstrate the generalizability of FMSs by reproducing three distinct novelty effects recently observed in experiments: absolute, contextual (or oddball), and omission novelty. Additionally, our model reproduces functional diversity within cell subpopulations, leading to experimentally testable predictions about connectivity and synaptic dynamics that can produce both population-level novelty responses and heterogeneous individual neuron signals. Altogether, our findings demonstrate how simple plasticity mechanisms within the cortical circuit structure can give rise to qualitatively distinct novelty responses. The flexibility of FMSs opens the door to computationally and theoretically investigating how distinct synapse modulations can lead to a variety of experience-dependent responses in a simple, understandable, and biologically plausible setup.
由于环境不断变化，大脑识别超出其自身内部世界表征的新刺激的能力对于生物体的生存至关重要。在哺乳动物新皮层中，抑制性微电路被认为以经验依赖的方式调节活动，并且不同的抑制性神经元亚型表现出不同的新奇反应。除非有一个生物学上合理的机制来检测新经验并从中学习，且该机制既易于理解又灵活，否则辨别不同神经回路的功能及其受经验的调节可能会令人生畏。在这里，我们引入一种学习机制，即（FMSs），通过这种机制，一种编码新奇性的网络反应从仅依赖于突触前或突触前和突触后活动的无监督乘法突触修饰中出现。FMSs在其简单性方面与其他熟悉机制不同：它们在持续学习下运行，不需要专门的架构，并且可以快速区分新奇性而不需要反馈。在视觉皮层回路的实验约束模型中实现FMSs，我们通过重现最近在实验中观察到的三种不同的新奇效应：绝对新奇、情境（或异常球）新奇和遗漏新奇，证明了FMSs的通用性。此外，我们的模型重现了细胞亚群内的功能多样性，从而产生了关于连接性和突触动力学的可实验测试的预测，这些预测可以产生群体水平的新奇反应和异质的单个神经元信号。总之，我们的研究结果表明皮层回路结构内简单的可塑性机制如何能够产生质上不同的新奇反应。FMSs的灵活性为从计算和理论上研究不同的突触调制如何在一个简单、易懂且生物学上合理的设置中导致各种经验依赖反应打开了大门。
