Kolmogorov complexity might be low, but that's only the amount needed to specify the entity. Predicting the behavior of the entity is a different question entirely. For example, the string given by f(n)= p(A(n,n)) mod 3 where A(m,k) is the Ackermann function and p(m) is the nth prime number has very low Kolmogorov complexity. (The Turing machine to do this is simple enough that one could if one wanted to write out all the states without much effort). But calculating this function beyond n=3 or so is not feasible. The issue is not just the total information but the degree of interaction. Your estimate ignores how much interaction there is between neurons. As with the example with the Ackermann function, the level of interaction may create something which is computationally intractable even if the specification length is very short.

There are other methods of estimating the computational level necessary to model the human brain that do try to calculate estimates based on neuronal interactions, and they are many orders of magnitude larger than your estimate. If computational power continues to increase exponentially this shouldn't make that much of a difference (order of 15 to 30 years or so in terms of computational power), but it doesn't stop your estimate from being far below the correct value.