How can I make this piece of code even faster?

[pablobarhamalzas at gmail.com]

Ok, I'm working on a predator/prey simulation, which evolve using genetic algorithms. At the moment, they use a quite simple feed-forward neural network, which can change size over time. Each brain "tick" is performed by the following function (inside the Brain class):

    def tick(self):
        input_num = self.input_num 
        hidden_num = self.hidden_num
        output_num = self.output_num
         
        hidden = [0]*hidden_num
        output = [0]*output_num
        
        inputs = self.input
        h_weight = self.h_weight
        o_weight = self.o_weight
        
        e = math.e
        
        count = -1
        for x in range(hidden_num):
            temp = 0
            for y in range(input_num):
                count += 1
                temp += inputs[y] * h_weight[count]
            hidden[x] = 1/(1+e**(-temp))  
        
        count = -1      
        for x in range(output_num):
            temp = 0 
            for y in range(hidden_num):
                count += 1 
                temp += hidden[y] * o_weight[count]
            output[x] = 1/(1+e**(-temp))  
             
        self.output = output 

The function is actually quite fast (~0.040 seconds per 200 calls, using 10 input, 20 hidden and 3 output neurons), and used to be much slower untill I fiddled about with it a bit to make it faster. However, it is still somewhat slow for what I need it.
 
My question to you is if you an see any obvious (or not so obvious) way of making this faster. I've heard about numpy and have been reading about it, but I really can't see how it could be implemented here.

Cheers!