"Brain-like" can mean either of two things, the circuit approach or the algorithm approach. 1) Circuits: Its components look like neurons (analog internals, spikes, etc). Algorithmic: 2) It performs the same real-time 3-D tomography/simulation/control which real brains perform. This second approach is unusual in ignoring "neurons" entirely, but has the advantage that 3-D reconstruction from undersampled, quantized inputs is a well-posed computational problem, although it does require a difficult-to-imagine continuous, self-amplifying 3-D representational medium to make it work. My caclulations indicate the second approach is at least 10^8 more efficient than the circuit-based approach at doing what brains actually do (https://arxiv.org/pdf/1409.8275.pdf).
"Brain-like" is not a binary property. A recurrent neural network is more brain-like than a Turing machine. Numenta's Cortical Learning Algorithm is more brain-like than an RNN. Blue Brain simulator is more brain-like than CLA. And so on.
