Exposing parallelism in scientific applications has become a core requirement for efficiently running on modern distributed multicore SIMD compute architectures. The granularity of parallelism that can be attained is a key determinant for the achievable acceleration and time to solution. Motivated by a scientific use case that requires the simulation of long spans of time — the study of plasticity and learning in detailed models of brain tissue — we present a strategy that exposes and exploits multicore and SIMD micro-parallelism from unrolling flow dependencies and concurrent outputs in a large system of coupled ordinary differential equations (ODEs). An implementation of a parallel simulator is presented, running on the HPX runtime system for the ParalleX execution model, providing dynamic task-scheduling and asynchronous execution. The implementation was tested on different architectures using a previously published brain tissue model. Benchmark of single neurons on a single compute node present a speed-up of circa 4-7x when compared with the state of the art Single Instruction Multiple Data (SIMD) implementation and 13-40x over its Single Instruction Single Data (SISD) counterpart. Large scale benchmarks suggest almost ideal strong scaling and a speed-up of 2-8x on a distributed architecture of 128 Cray X6 compute nodes.