def summarize_rec_data(data):
"""Return summary stats of recorded data."""
# Warning: not all collectible data has a summary stats implemented below!
# See get_rec_stats() above!
stats = {}
if 'hc_ro' in data:
# Entropy across HC units average over samples.
hc_ro_arr = np.array(list(data['hc_ro'].values()))
stats['H HC ro'] = utils.entropy(hc_ro_arr.T).mean()
if 'vs_state' in data:
# Sum of vS reward estimates change (from first to last sample).
vs_state = data['vs_state']
stats['d vS'] = sum(vs_state[max(vs_state.keys())] - vs_state[0])
if 'co_occs' in data:
# Mean entropy of real location and HC state co-occurance frequencies.
co_occs = data['co_occs'][max(data['co_occs'].keys())]
stats['H HC co'] = np.nanmean(get_hc_co_occ_entropy(co_occs))
stats['H loc co'] = np.nanmean(get_loc_co_occ_entropy(co_occs))
return stats
def record_learning(village, s, GS, HC, hc_ro, dVC_HC, dGS_HC, co_occs, norm):
"""Collect data from a single step of connectivity learning."""
# Animal's real and estimated position and location + uncertainty.
x, y = village.animal_coords()
gs_x, gs_y = analysis.GS_pos_mean(GS.P, GS.xvec, GS.yvec, GS.circular)
gs_h = utils.entropy(GS.P.flatten())
hc_ml = HC.s_names[hc_ro.argmax()]
hc_h = utils.entropy(hc_ro)
# Entropy between real state and HC state during last n steps.
s_real_h = get_loc_co_occ_entropy(co_occs)
s_hc_h = get_hc_co_occ_entropy(co_occs)
# Connectivity stats.
vc_hc_snr = np.mean(np.abs(HC.VC_HC), 1) / np.std(np.abs(HC.VC_HC), 1)
gs_hc_pos = analysis.GS_HC_conn_mean(HC.GS_HC, GS.xvec, GS.yvec,
GS.circular)
# Entropy calculation below takes a lot of time --> approximated by max.
# gs_hc_h = utils.entropy(HC.GS_HC.reshape((len(HC.s_names), -1)).T)
gs_hc_max = HC.GS_HC.max(axis=(1, 2))
# Connectivity change.
dVC_HC_max = analysis.VC_HC_norm(dVC_HC, norm).max()
dGS_HC_max = analysis.GS_HC_norm(dGS_HC, norm).max()
# Compensate for the magnitude difference between VC and GS input (GS is a
# PD, VC is not).
dVC_HC_max /= dVC_HC.shape[1]
res = {'s': s, 'x': x, 'y': y,
'gs_x': gs_x, 'gs_y': gs_y, 'gs_h': gs_h,
'hc_ro': hc_ro, 'hc_ml': hc_ml, 'hc_h': hc_h,
's_real_h': s_real_h, 's_hc_h': s_hc_h,
'vc_hc_snr': vc_hc_snr,
'gs_hc_pos': gs_hc_pos, 'gs_hc_max': gs_hc_max,
'dVC_HC_max': dVC_HC_max, 'dGS_HC_max': dGS_HC_max}
return res
def format_GS_HC_rec(res, s_hc, GS_HC):
"""Format recordings of GS - HC connectivity."""
# GS - HC x-y mean position per HC unit.
gs_hc_pos = pd.concat({i: pd.DataFrame(kv, index=['x', 'y'], columns=s_hc)
for i, kv in res['gs_hc_pos'].items()}).unstack()
# GS - HC entropy per HC unit.
gs_hc_h = pd.Series([utils.entropy(gs_hc.flatten()) for gs_hc in GS_HC],
index=s_hc)
# GS - HC maximum value per HC unit.
gs_hc_max = pd.Series([gs_hc.max() for gs_hc in GS_HC], index=s_hc)
return gs_hc_pos, gs_hc_h, gs_hc_max
def GS_pos_estimate(gs_state, gs_circular):
"""
Return GS position estimate and inverse precision (entropy) over
simulation time.
"""
# Init params and grouping.
g = gs_state.groupby(level=0)
xvec = np.array(gs_state.columns)
yvec = np.sort(np.array(gs_state.index.get_level_values('y').unique()))
# Weighted mean position estimate.
mpos = g.apply(lambda x: GS_pos_mean(x, xvec, yvec, gs_circular))
mpos = pd.concat(
{i: pd.Series(mp, index=['x', 'y'])
for i, mp in mpos.items()}).unstack()
# Inverse precision (entropy) of location estimate.
H = g.apply(lambda x: utils.entropy(x.stack()))
return mpos, H
def curious_exploration(GS, HC, village, u_list, mot_sig):
"""Return action determined by curiousity-drivent exploration."""
# TODO: remove dependence on village and make this part of an internal
# PFC - MC - GS - HC loop.
gs_p = GS.P.copy() # save current GS activity
# Go through each action, roll GS, infer HC, check uncertainty (entropy).
s_uncertain = {}
for u in u_list:
umot, emot, vmot = village.motor_feedback(u, mot_sig)
GS.motor_update(umot)
HC.grid_input(GS.P)
s_uncertain[u] = utils.entropy(HC.s['GS'])
GS.P = gs_p # restore original GS activity
# Select action leading to maximally uncertain state.
u_max_uncert = max(s_uncertain, key=s_uncertain.get)
return u_max_uncert