High-dimensional neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related latent variables. A fundamental goal of neural data analysis consists of identifying such variables and their mapping to neural activity. The canonical approach is to assume the latent variables are behaviour (e.g. the measured location of the animal) and visualise the subsequent tuning curves. However, significant mismatches between behaviour and the encoded variables may still exist---the agent may be thinking of another location, or be uncertain of its own---distorting the tuning curves and decreasing their interpretability. To address this issue a variety of methods have been proposed to learn this latent variable in an unsupervised manner; these techniques are typically expensive to train, come with many hyperparameters, or scale poorly to large datasets complicating their adoption in practice. To solve these issues we propose SIMPL (Scalable Iterative Maximization of Population-coded Latents); an EM-style algorithm which iteratively optimises latent variables and tuning curves. SIMPL is fast, scalable and exploits behaviour as an initial condition to improve convergence and identifiability. SIMPL accurately recovers latent variables in a biologically-inspired spatial navigation task, outperforming a contemporary neural-network based equivalent. When applied to a large rodent hippocampal dataset SIMPL rapidly finds a modified latent space with smaller, more numerous, and more uniformly-sized place fields than those based on behaviour, suggesting the brain encodes space with greater resolution than previously thought.