#ifndef nmsim_mf_H
#define nmsim_mf_H

/* Types and functions for the mean-field simulation of neuronal nets. */
/* Last edited on 2019-01-11 18:04:44 by jstolfi */

typedef struct nmsim_bundle_parms_t
  { double W_dev; /* Deviation of total rested synapse gain. */
    double W_avg; /* Average total rested synapse gain. */
  } nmsim_bundle_parms_t;
  /* Parameters of a set of synapses from a homogeneous population {A}
    of neurons to another homogeneous population {B} (possibly the
    same as {A}). If the population {B} has {N} neurons, each neuron
    of {A} has a synapse to each neuron of {B} with independent
    probability {K/N}. The strength of that synapse, in its fully
    rested state, is a random variable with log-normal distribution,
    mean {W_avg/K}, and deviation {W_dev/K}. */

typedef struct nmsim_pop_state_t
  { int32_t np;  /* Number of individual cohorts retained. */
    nmsim_cohort_state_t *cs; /* State of neurons in each cohort. */
  } nemansim_pop_state_t;
  /* Stochastic state of a homogeneous population of neurons.
    The neurons with firing age {k} have state {cs[k]},
    for {k} in {0..np-1}.  All neurons with age {np} or greater
    are lumped together and assumed to have state
    {cs[np].}  */

void nmsim_pop_evolve
  ( nmsim_pop_state_t *pso,
    double DV_avg,
    double DV_dev,
    double I_avg,
    double I_dev,
    nmsim_neuron_class_t *parm,
    nmsim_pop_state_t *psn
  );
  /* Simulates the evolution of a homogeneous population of neurons
    with stochastic state {pso} and neuron parameters {parm} from some
    discrete time {t} to time {t+1}.

    It is assumed that the total input that each neuron {i} in the
    population received from all its input synapses betwen time {t}
    and time {t+1} is a normal random variable with mean {DV_avg} and
    deviation {DV_dev}. This potential increment includes the pulses
    {X[j,t]} of each input neuron {j} in the network, modulated by its
    output gain factor {H[j,t]} and by the rested synaptic gain
    {wfix[j,i]}; but not yet modulated by the input gain {G[i,t]} of
    neuron {i}.

    In addition to synaptic inputs, each neuron {i} in the population
    is also assumed to have received, between times {t} and {t+1}, an
    external input signal {I[i,t]}, which is a normally distributed
    variable with mean {I_avg} and deviation {I_dev}.

    ??? Should the input signal be modulated by the input gain factor
    {G[i,t]} too? ??? */

void nmsim_network_evolve
  (
  );
  /* Simulates the evolution of a network with one or more homogeneous
    populations of neurons with stochastic states {net.pso[k]} and
    neuron parameters {parm} from some discrete time {t} to time
    {t+1}.

    In addition to synaptic inputs, each neuron {i} in the 
    population {k} is also assumed to have received, between times
    {t} and {t+1}, an external input signal {I[i,t]},
    which is a normally distributed variable with mean
    and deviation given by {net.exin(k,t,&I_avg,I_dev)}

    ??? Should the input signal be modulated by the input gain
    factor {G[i,t]} too? ???  */

#endif