Simple neural-network simulation in C++ (Round 4)
get_I_syn() currently always returns 0
I'm not sure if you are aware of this, but because I_syn_bar is a constant 0, this makes get_I_syn() always return 0.  This means you can just get rid of all calls to get_I_syn(), which is where 100% of your time is being spent.
However, assuming that there is some mixup here and I_syn_bar is supposed to be non-zero, I will explain how you can speed your program up by a factor of 50+.
Caching return values of get_f()
If you look at the get_f() function, everything is a constant except for t, which is passed in as t - spike_times[i] from get_I_syn().  If you look closely at what t and spike_times[i] are, they are all multiples of Delta_t.  In fact, t - spike_times[i] can only take on one of 10000 values, which are the first 10000 multiples of Delta_t.
Knowing this, you can precompute the 10000 possible values of get_f().  Then inside get_I_syn(), you can look up the precomputed values instead of doing the expensive computation.
I modified your program to do this and the program sped up by more than 50x.
Modified program
Here is the modified program.  I copied the header contents directly into the c++ file for my own convenience.  As you can see, I increased the number of iterations from 10000 to 300000 because otherwise the program ran too fast.
#include &lt;deque&gt;
#include &lt;math.h&gt;

const double get_T(const double tau, const double g_l, const double I_0,
                   const double theta, const double V_l)
{
  return -tau * log(1 - g_l / I_0 * (theta - V_l));
}


double set_initial_V(const double tau, const double g_l, const double I_0,
                     const double theta, const double V_l, const double c,
                     const double N)
{
  const double T = get_T(tau, g_l, I_0, theta, V_l);
  const double i = 1;
  return V_l + I_0 / g_l * (1 - exp(-c * (i - 1) / N * T / tau));
}


double get_dV_dt(const double g_l, const double V, const double V_l,
                 const double I_syn, const double I_0, const double C)
{
  return (-g_l * (V - V_l) + I_syn + I_0) / C;
}


double get_f(const double tau_1, const double tau_2, const double t)
{
  return 1 / (tau_1 - tau_2) * (exp(-t / tau_1) - exp(-t / tau_2));
}


double get_I_syn(const double I_syn_bar, const double N, const double *f_cache,
                 const int t_index, const int *spike_times, int n_spikes)
{
    double sum_f = 0;
    for (int i = 0; i &lt; n_spikes; i++)
        sum_f += f_cache[t_index - spike_times[i]];
    return I_syn_bar / N * sum_f;
}

#define ITERATIONS        300000

// Declare in global scope to not overflow the stack.
static double f_cache[ITERATIONS];
static int spike_times[ITERATIONS];

int main()
{
    // Parameters.
    const double tau = 10;
    const double g_l = 0.1;
    const double I_0 = 2.3;
    const double theta = -40;
    const double V_l = -60;
    const double c = 0.5;
    const double N = 128;
    const double I_syn_bar = 2;
    const double tau_1 = 3;
    const double tau_2 = 1;
    const double C = 1;
    const double Delta_t = 0.25;
    int    i = 0;
    double t = 0;

    // Simulation.
    int numSpikes = 0;
    double V = set_initial_V(tau, g_l, I_0, theta, V_l, c, N);

    // Precompute all the possible values returned by get_f().
    for (i = 0, t = 0; i &lt; ITERATIONS; i++, t += Delta_t)
        f_cache[i] = get_f(tau_1, tau_2, t);

    for (i = 0; i &lt; ITERATIONS; i++)
    {
        double I_syn = get_I_syn(I_syn_bar, N, f_cache, i, spike_times,
                numSpikes);
        double dV_dt = get_dV_dt(g_l, V, V_l, I_syn, I_0, C);
        V += Delta_t * dV_dt;
        if (V &gt; theta)
        {
            V = V_l;
            spike_times[numSpikes++] = i + 1;
        }
    }
}