There is a growing consensus among neuroscientists that many neural circuits critical for survival result from a process of genomic decompression, hence are constructed based on the information contained within the genome. Aligning with this perspective, we introduce SynaptoGen, a novel computational framework designed to bring the advent of synthetic biological intelligence closer, facilitating the development of neural biological agents through the precise control of genetic factors governing synaptogenesis. SynaptoGen represents the first model in the well-established family of Connectome Models (CMs) to offer a possible mechanistic explanation of synaptic multiplicity based on genetic expression and protein interaction probabilities, modeling connectivity with unprecedented granularity. Furthermore, SynaptoGen connects these genetic factors through a differentiable function, effectively working as a neural network in which each synaptic weight is computed as the average number of synapses between neurons, multiplied by its corresponding conductance, and derived from a specific genetic profile. Differentiability is a critical feature of the framework, enabling its integration with gradient-based optimization techniques. This allows SynaptoGen to generate patterns of genetic expression and/or genetic rules capable of producing pre-wired biological agents tailored to specific tasks. The framework is validated in simulated synaptogenesis scenarios with varying degrees of biological plausibility. It successfully produces biological agents capable of solving tasks in four different reinforcement learning benchmarks, consistently outperforming the state-of-the-art and a control baseline designed to represent populations of neurons where synapses form freely, i.e., without guided manipulations.

翻译：神经科学界日益形成共识：许多对生存至关重要的神经回路源于基因组解压缩过程，因此是基于基因组所含信息构建的。基于这一视角，我们提出SynaptoGen——一个旨在加速合成生物智能发展的新型计算框架，通过精确控制调控突触发生的遗传因子来促进神经生物智能体的开发。SynaptoGen是现有连接组模型家族中首个基于基因表达和蛋白质相互作用概率为突触多重性提供机制解释的模型，以前所未有的粒度对神经连接进行建模。该框架通过可微分函数关联这些遗传因子，实质上相当于一个神经网络：其中每个突触权重被计算为神经元间平均突触数量乘以对应电导值，并源自特定遗传谱系。可微分性是该框架的关键特性，使其能够与基于梯度的优化技术相结合。这使得SynaptoGen能够生成可产生针对特定任务的预置型生物智能体的基因表达模式和/或遗传规则。该框架在不同生物可信度的模拟突触发生场景中得到验证，成功构建出能在四个不同强化学习基准测试中解决问题的生物智能体，其表现持续超越最先进技术以及代表自由形成突触（即无引导调控）神经元群体的对照基线。