hill_tononi_Vp_raster.py

#! This tutorial shows you how to implement a simplified version of the
#! Hill-Tononi model of the early visual pathway using the NEST Topology
#! module.  The model is described in the paper
#! 
#!   S. L. Hill and G. Tononi.
#!   Modeling Sleep and Wakefulness in the Thalamocortical System.
#!   J Neurophysiology **93**:1671-1698 (2005).
#!   Freely available via `doi 10.1152/jn.00915.2004
#!   <http://dx.doi.org/10.1152/jn.00915.2004>`_.
#!
#! And is based on the NEST Topology example of the Hill-Tononi model
#! implement by Hans Ekkehard Plesser, which can be found here:
#!
#! https://github.com/nest/nest-simulator/blob/master/topology/examples/hill_tononi_Vp.py
#!
#! Authors: Leonardo Barbosa and Keiko Fujii
#! Date:    21 September 2016


SHOW_FIGURES = True

import pylab
if not SHOW_FIGURES:
    pylab_show = pylab.show
    def nop(s=None): pass
    pylab.show = nop
else:
    pylab.ion()
    
# Leonardo: I need to import readline for some reason, otherwise nest wont work
import readline

#! Load pynest
import nest
import nest.topology as topo
from nest import raster_plot

#! Make sure we start with a clean slate, even if we re-run the script
#! in the same Python session. 
nest.ResetKernel()

# set multi-thread on
nest.SetKernelStatus({"local_num_threads": 16})

#! Other generic imports
import math
import numpy as np
import scipy.io
import pickle

#! --- keiko 9/14/2016
#! Import csv to save V_m data
import csv
f_Vm = open('V_m.csv', 'w')
#writer_Vm = csv.writer(f_Vm)
f_idx = open('idx.csv', 'w')
#writer_idx = csv.writer(f_idx)

#! Configurable Parameters
#! =======================
#!
#! Here we define those parameters that we take to be
#! configurable. The choice of configurable parameters is obviously
#! arbitrary, and in practice one would have far more configurable
#! parameters. We restrict ourselves to:
#! 
#! - Network size in neurons ``N``, each layer is ``N x N``.
#! - Network size in subtended visual angle ``visSize``, in degree.
#! - Temporal frequency of drifting grating input ``f_dg``, in Hz.
#! - Spatial wavelength and direction of drifting grating input,
#!   ``lambda_dg`` and ``phi_dg``, in degree/radian.
#! - Background firing rate of retinal nodes and modulation amplitude,
#!   ``retDC`` and ``retAC``, in Hz.
#! - Simulation duration ``simtime``; actual simulation is split into
#!   intervals of ``sim_interval`` length, so that the network state
#!   can be visualized in those intervals. Times are in ms. 
Params = {'N'           :     40,
          'visSize'     :    8.0,
          'f_dg'        :    2.0,
          'lambda_dg'   :    2.0, # spatial structure
          #'lambda_dg'   :   -1.0, # random: each pixel with random lambda_dg / phi_dg
          'phi_dg'      :    0.0,
          'retDC'       :   30.0,
          'retAC'       :   30.0,
          'simtime'     :  100.0,
          'sim_interval':    5.0
          }

#! Neuron Models
#! =============
#!
#! We declare models in two steps:
#!
#! 1. We define a dictionary specifying the NEST neuron model to use
#!    as well as the parameters for that model.
#! #. We create three copies of this dictionary with parameters
#!    adjusted to the three model variants specified in Table~2 of
#!    Hill & Tononi (2005) (cortical excitatory, cortical inhibitory,
#!    thalamic)
#! 
#! In addition, we declare the models for the stimulation and
#! recording devices.
#!
#! The general neuron model
#! ------------------------
#!
#! We use the ``iaf_cond_alpha`` neuron, which is an
#! integrate-and-fire neuron with two conductance-based synapses which
#! have alpha-function time course.  Any input with positive weights
#! will automatically directed to the synapse labeled ``_ex``, any
#! with negative weights to the synapes labeled ``_in``.  We define
#! **all** parameters explicitly here, so that no information is
#! hidden in the model definition in NEST. ``V_m`` is the membrane
#! potential to which the model neurons will be initialized.
#! The model equations and parameters for the Hill-Tononi neuron model
#! are given on pp. 1677f and Tables 2 and 3 in that paper. Note some
#! peculiarities and adjustments:
#!
#! - Hill & Tononi specify their model in terms of the membrane time
#!   constant, while the ``iaf_cond_alpha`` model is based on the
#!   membrane capcitance. Interestingly, conducantces are unitless in
#!   the H&T model. We thus can use the time constant directly as
#!   membrane capacitance.
#! - The model includes sodium and potassium leak conductances. We
#!   combine these into a single one as follows:
#$   \begin{equation}-g_{NaL}(V-E_{Na}) - g_{KL}(V-E_K) 
#$      =
#$   -(g_{NaL}+g_{KL})\left(V-\frac{g_{NaL}E_{NaL}+g_{KL}E_K}{g_{NaL}g_{KL}}\right)
#$   \end{equation}
#! - We write the resulting expressions for g_L and E_L explicitly
#!   below, to avoid errors in copying from our pocket calculator.
#! - The paper gives a range of 1.0-1.85 for g_{KL}, we choose 1.5
#!   here.
#! - The Hill-Tononi model has no explicit reset or refractory
#!   time. We arbitrarily set V_reset and t_ref.
#! - The paper uses double exponential time courses for the synaptic
#!   conductances, with separate time constants for the rising and
#!   fallings flanks. Alpha functions have only a single time
#!   constant: we use twice the rising time constant given by Hill and
#!   Tononi.
#! - In the general model below, we use the values for the cortical
#!   excitatory cells as defaults. Values will then be adapted below.
#!
nest.CopyModel('iaf_cond_alpha', 'NeuronModel',
               params = {'C_m'       :  16.0,
                         'E_L'       : (0.2 * 30.0 + 1.5 * -90.0)/(0.2 + 1.5),
                         'g_L'       : 0.2 + 1.5,
                         #'E_L'       : 30.0,
                         #'g_L'       : 0.2,
                         'E_ex'      :   0.0,
                         'E_in'      : -70.0,
                         'V_reset'   : -60.0,
                         'V_th'      : -51.0,
                         't_ref'     :   2.0,
                         'tau_syn_ex':   1.0,
                         'tau_syn_in':   2.0,
                         'I_e'       :   0.0,
                         'V_m'       : -70.0})

#! Adaptation of models for different populations
#! ----------------------------------------------

#! We must copy the `NeuronModel` dictionary explicitly, otherwise
#! Python would just create a reference.

#! Cortical excitatory cells 
#! .........................
#! Parameters are the same as above, so we need not adapt anything
nest.CopyModel('NeuronModel', 'CtxExNeuron')

#! Cortical inhibitory cells 
#! .........................
nest.CopyModel('NeuronModel', 'CtxInNeuron', 
               params = {'C_m'  :   8.0,
                         'V_th' : -53.0,
                         't_ref':   1.0})

#! Thalamic cells 
#! ..............
nest.CopyModel('NeuronModel', 'ThalamicNeuron', 
               params = {'C_m'  :   8.0,
                         'V_th' : -53.0,
                         't_ref':   1.0,
                         'E_in' : -80.0})

#! Input generating nodes
#! ----------------------

#! Input is generated by sinusoidally modulate Poisson generators,
#! organized in a square layer of retina nodes. These nodes require a
#! slightly more complicated initialization than all other elements of
#! the network:
#!
#! - Average firing rate ``rate``, firing rate modulation depth ``amplitude``, and
#!   temporal modulation frequency ``frequency`` are the same for all retinal
#!   nodes and are set directly below.
#! - The temporal phase ``phase`` of each node depends on its position in
#!   the grating and can only be assigned after the retinal layer has
#!   been created. We therefore specify a function for initalizing the
#!   ``phase``. This function will be called for each node.
def phaseInit(pos, lam, alpha):
    '''Initializer function for phase of drifting grating nodes.

       pos  : position (x,y) of node, in degree
       lam  : wavelength of grating, in degree
       alpha: angle of grating in radian, zero is horizontal

       Returns number to be used as phase of sinusoidal Poisson generator.
    '''
    return 360.0 / lam * (math.cos(alpha) * pos[0] + math.sin(alpha) * pos[1]) 

nest.CopyModel('sinusoidal_poisson_generator', 'RetinaNode',
               params = {'amplitude': Params['retAC'],
                         'rate'     : Params['retDC'],
                         'frequency': Params['f_dg'],
                         'phase'    : 0.0,
                         'individual_spike_trains': False})

#! Recording nodes
#! ---------------

#! We use the new ``multimeter`` device for recording from the model
#! neurons. At present, ``iaf_cond_alpha`` is one of few models
#! supporting ``multimeter`` recording.  Support for more models will
#! be added soon; until then, you need to use ``voltmeter`` to record
#! from other models.
#!
#! We configure multimeter to record membrane potential to membrane
#! potential at certain intervals to memory only. We record the GID of
#! the recorded neurons, but not the time.
nest.CopyModel('multimeter', 'RecordingNode',
               params = {'interval'   : Params['sim_interval'],
                         'record_from': ['V_m'],
                         'record_to'  : ['memory'],
                         'withgid'    : True,
                         'withtime'   : False})


#! Populations
#! ===========

#! We now create the neuron populations in the model, again in the
#! form of Python dictionaries. We define them in order from eye via
#! thalamus to cortex.
#!
#! We first define a dictionary defining common properties for all
#! populations
layerProps = {'rows'     : Params['N'], 
              'columns'  : Params['N'],
              'extent'   : [Params['visSize'], Params['visSize']],
              'edge_wrap': True}
#! This dictionary does not yet specify the elements to put into the
#! layer, since they will differ from layer to layer. We will add them
#! below by updating the ``'elements'`` dictionary entry for each
#! population.

#! Retina
#! ------
layerProps.update({'elements': 'RetinaNode'})
retina = topo.CreateLayer(layerProps)

# Original: Gabor retina input

if Params['lambda_dg'] >= 0:
    #! Now set phases of retinal oscillators; we use a list comprehension instead
    #! of a loop.
    [nest.SetStatus([n], {"phase": phaseInit(topo.GetPosition([n])[0],
                                         Params["lambda_dg"],
                                         Params["phi_dg"])})
    for n in nest.GetLeaves(retina)[0]]
else:
    # Leonardo: Random retina input

    [nest.SetStatus([n], {"phase": phaseInit(topo.GetPosition([n])[0],
                                         np.pi * np.random.rand(),
                                         np.pi * np.random.rand())})
    for n in nest.GetLeaves(retina)[0]]

#! Thalamus
#! --------

#! We first introduce specific neuron models for the thalamic relay
#! cells and interneurons. These have identical properties, but by
#! treating them as different models, we can address them specifically
#! when building connections.
#!
#! We use a list comprehension to do the model copies.
[nest.CopyModel('ThalamicNeuron', SpecificModel) for SpecificModel in ('TpRelay', 'TpInter')]

#! Now we can create the layer, with one relay cell and one
#! interneuron per location:
layerProps.update({'elements': ['TpRelay', 'TpInter']})
Tp = topo.CreateLayer(layerProps)

#! Reticular nucleus
#! -----------------
#! We follow the same approach as above, even though we have only a
#! single neuron in each location.
[nest.CopyModel('ThalamicNeuron', SpecificModel) for SpecificModel in ('RpNeuron',)]
layerProps.update({'elements': 'RpNeuron'})
Rp = topo.CreateLayer(layerProps)

#! Primary visual cortex
#! ---------------------

#! We follow again the same approach. We differentiate neuron types
#! between layers and between pyramidal cells and interneurons. At
#! each location, there are two pyramidal cells and one interneuron in
#! each of layers 2-3, 4, and 5-6. Finally, we need to differentiate
#! between vertically and horizontally tuned populations. When creating
#! the populations, we create the vertically and the horizontally
#! tuned populations as separate populations.

#! We use list comprehesions to create all neuron types:
[nest.CopyModel('CtxExNeuron', layer+'pyr') for layer in ('L23','L4','L56')]
[nest.CopyModel('CtxInNeuron', layer+'in' ) for layer in ('L23','L4','L56')]

#! Now we can create the populations, suffixes h and v indicate tuning
layerProps.update({'elements': ['L23pyr', 2, 'L23in', 1,
                                'L4pyr' , 2, 'L4in' , 1,
                                'L56pyr', 2, 'L56in', 1]})
Vp_h = topo.CreateLayer(layerProps)
Vp_v = topo.CreateLayer(layerProps)

#! Collect all populations
#! -----------------------

#! For reference purposes, e.g., printing, we collect all populations
#! in a tuple:
populations = (retina, Tp, Rp, Vp_h, Vp_v)

#! Inspection
#! ----------

#! We can now look at the network using `PrintNetwork`:
nest.PrintNetwork()

#! We can also try to plot a single layer in a network. For
#! simplicity, we use Rp, which has only a single neuron per position.
#topo.PlotLayer(Rp)
#pylab.title('Layer Rp')
#pylab.show()


#! Synapse models
#! ==============

#! Actual synapse dynamics, e.g., properties such as the synaptic time
#! course, time constants, reversal potentials, are properties of
#! neuron models in NEST and we set them in section `Neuron models`_
#! above. When we refer to *synapse models* in NEST, we actually mean
#! connectors which store information about connection weights and
#! delays, as well as port numbers at the target neuron (``rport``)
#! and implement synaptic plasticity. The latter two aspects are not
#! relevant here.
#!
#! We just use NEST's ``static_synapse`` connector but copy it to
#! synapse models ``AMPA`` and ``GABA_A`` for the sake of
#! explicitness. Weights and delays are set as needed in section
#! `Connections`_ below, as they are different from projection to
#! projection. De facto, the sign of the synaptic weight decides
#! whether input via a connection is handle by the ``_ex`` or the
#! ``_in`` synapse.
nest.CopyModel('static_synapse', 'AMPA')
nest.CopyModel('static_synapse', 'GABA_A')

#! Connections
#! ====================

#! Building connections is the most complex part of network
#! construction. Connections are specified in Table 1 in the
#! Hill-Tononi paper. As pointed out above, we only consider AMPA and
#! GABA_A synapses here.  Adding other synapses is tedious work, but
#! should pose no new principal challenges. We also use a uniform in
#! stead of a Gaussian distribution for the weights.
#!
#! The model has two identical primary visual cortex populations,
#! ``Vp_v`` and ``Vp_h``, tuned to vertical and horizonal gratings,
#! respectively. The *only* difference in the connection patterns
#! between the two populations is the thalamocortical input to layers
#! L4 and L5-6 is from a population of 8x2 and 2x8 grid locations,
#! respectively. Furthermore, inhibitory connection in cortex go to 
#! the opposing orientation population as to the own.
#!
#! To save us a lot of code doubling, we thus defined properties
#! dictionaries for all connections first and then use this to connect
#! both populations. We follow the subdivision of connections as in
#! the Hill & Tononi paper.
#!
#! **Note:** Hill & Tononi state that their model spans 8 degrees of
#! visual angle and stimuli are specified according to this. On the
#! other hand, all connection patterns are defined in terms of cell
#! grid positions. Since the NEST Topology Module defines connection
#! patterns in terms of the extent given in degrees, we need to apply
#! the following scaling factor to all lengths in connections:
dpc = Params['visSize'] / (Params['N'] - 1)

#! We will collect all same-orientation cortico-cortical connections in
ccConnections = []
#! the cross-orientation cortico-cortical connections in
ccxConnections = []
#! and all cortico-thalamic connections in 
ctConnections = []

#! Horizontal intralaminar
#! -----------------------
#! *Note:* "Horizontal" means "within the same cortical layer" in this
#! case.
#!
#! We first define a dictionary with the (most) common properties for
#! horizontal intralaminar connection. We then create copies in which
#! we adapt those values that need adapting, and  
horIntraBase = {"connection_type": "divergent",
                "synapse_model": "AMPA",
                "mask": {"circular": {"radius": 12.0 * dpc}},
                "kernel": {"gaussian": {"p_center": 0.05, "sigma": 7.5 * dpc}},
                "weights": 1.0,
                "delays": {"uniform": {"min": 1.75, "max": 2.25}}}

#! We use a loop to do the for for us. The loop runs over a list of 
#! dictionaries with all values that need updating
for conn in [{"sources": {"model": "L23pyr"}, "targets": {"model": "L23pyr"}},
             {"sources": {"model": "L23pyr"}, "targets": {"model": "L23in" }},
             {"sources": {"model": "L4pyr" }, "targets": {"model": "L4pyr" },
              "mask"   : {"circular": {"radius": 7.0 * dpc}}},
             {"sources": {"model": "L4pyr" }, "targets": {"model": "L4in"  },
              "mask"   : {"circular": {"radius": 7.0 * dpc}}},
             {"sources": {"model": "L56pyr"}, "targets": {"model": "L56pyr" }},
             {"sources": {"model": "L56pyr"}, "targets": {"model": "L56in"  }}]:
    ndict = horIntraBase.copy()
    ndict.update(conn)
    ccConnections.append(ndict)

#! Vertical intralaminar
#! -----------------------
#! *Note:* "Vertical" means "between cortical layers" in this
#! case.
#!
#! We proceed as above.
verIntraBase = {"connection_type": "divergent",
                "synapse_model": "AMPA",
                "mask": {"circular": {"radius": 2.0 * dpc}},
                "kernel": {"gaussian": {"p_center": 1.0, "sigma": 7.5 * dpc}},
                "weights": 2.0,
                "delays": {"uniform": {"min": 1.75, "max": 2.25}}}

for conn in [{"sources": {"model": "L23pyr"}, "targets": {"model": "L56pyr"}, "weights": 1.0},
             {"sources": {"model": "L23pyr"}, "targets": {"model": "L56in"}, "weights": 1.0}, #---keiko
             #{"sources": {"model": "L23pyr"}, "targets": {"model": "L23in"}, "weights": 1.0}, #---original
             {"sources": {"model": "L4pyr" }, "targets": {"model": "L23pyr"}},
             {"sources": {"model": "L4pyr" }, "targets": {"model": "L23in" }},
             {"sources": {"model": "L56pyr"}, "targets": {"model": "L23pyr"}},
             {"sources": {"model": "L56pyr"}, "targets": {"model": "L23in" }},
             {"sources": {"model": "L56pyr"}, "targets": {"model": "L4pyr" }},
             {"sources": {"model": "L56pyr"}, "targets": {"model": "L4in"  }}]:
    ndict = verIntraBase.copy()
    ndict.update(conn)
    ccConnections.append(ndict)

#! Intracortical inhibitory
#! ------------------------
#!
#! We proceed as above, with the following difference: each connection 
#! is added to the same-orientation and the cross-orientation list of
#! connections.
#!
#! **Note:** Weights increased from -1.0 to -2.0, to make up for missing GabaB
#!
#! Note that we have to specify the **weight with negative sign** to make
#! the connections inhibitory.
intraInhBase = {"connection_type": "divergent",
                "synapse_model": "GABA_A",
                "mask": {"circular": {"radius": 7.0 * dpc}},
                "kernel": {"gaussian": {"p_center": 0.25, "sigma": 7.5 * dpc}},
                "weights": -2.0,
                "delays": {"uniform": {"min": 1.75, "max": 2.25}}}

#! We use a loop to do the for for us. The loop runs over a list of 
#! dictionaries with all values that need updating
for conn in [{"sources": {"model": "L23in"}, "targets": {"model": "L23pyr"}},
             {"sources": {"model": "L23in"}, "targets": {"model": "L23in" }},
             {"sources": {"model": "L4in" }, "targets": {"model": "L4pyr" }},
             {"sources": {"model": "L4in" }, "targets": {"model": "L4in"  }},
             {"sources": {"model": "L56in"}, "targets": {"model": "L56pyr"}},
             {"sources": {"model": "L56in"}, "targets": {"model": "L56in" }}]:
    ndict = intraInhBase.copy()
    ndict.update(conn)
    ccConnections.append(ndict)
    ccxConnections.append(ndict)

#! Corticothalamic
#! ---------------
corThalBase = {"connection_type": "divergent",
               "synapse_model": "AMPA",
               "mask": {"circular": {"radius": 5.0 * dpc}},
               "kernel": {"gaussian": {"p_center": 0.5, "sigma": 7.5 * dpc}},
               "weights": 1.0,
               "delays": {"uniform": {"min": 7.5, "max": 8.5}}}

#! We use a loop to do the for for us. The loop runs over a list of 
#! dictionaries with all values that need updating
for conn in [{"sources": {"model": "L56pyr"}, "targets": {"model": "TpRelay" }},
             {"sources": {"model": "L56pyr"}, "targets": {"model": "TpInter" }}]:
    #ndict = corThalBase.copy() #---keiko
    ndict = intraInhBase.copy() #---original
    ndict.update(conn)
    ctConnections.append(ndict)

#! Corticoreticular
#! ----------------

#! In this case, there is only a single connection, so we write the
#! dictionary itself; it is very similar to the corThalBase, and to 
#! show that, we copy first, then update. We need no ``targets`` entry,
#! since Rp has only one neuron per location.
corRet = corThalBase.copy()
corRet.update({"sources": {"model": "L56pyr"}, "weights": 2.5})


#! Build all connections beginning in cortex
#! -----------------------------------------

#! Cortico-cortical, same orientation
print("Connecting: cortico-cortical, same orientation")
[topo.ConnectLayers(Vp_h, Vp_h, conn) for conn in ccConnections]
[topo.ConnectLayers(Vp_v, Vp_v, conn) for conn in ccConnections]

#! Cortico-cortical, cross-orientation
print("Connecting: cortico-cortical, other orientation")
[topo.ConnectLayers(Vp_h, Vp_v, conn) for conn in ccxConnections]
[topo.ConnectLayers(Vp_v, Vp_h, conn) for conn in ccxConnections]

#! Cortico-thalamic connections
print("Connecting: cortico-thalamic")
[topo.ConnectLayers(Vp_h, Tp, conn) for conn in ctConnections]
[topo.ConnectLayers(Vp_v, Tp, conn) for conn in ctConnections]
topo.ConnectLayers(Vp_h, Rp, corRet) 
topo.ConnectLayers(Vp_v, Rp, corRet) 

#! Thalamo-cortical connections
#! ----------------------------

#! **Note:** According to the text on p. 1674, bottom right, of 
#! the Hill & Tononi paper, thalamocortical connections are 
#! created by selecting from the thalamic population for each
#! L4 pyramidal cell, ie, are *convergent* connections.
#!
#! We first handle the rectangular thalamocortical connections.
thalCorRect = {"connection_type": "convergent",
               "sources": {"model": "TpRelay"},
               "synapse_model": "AMPA",
               "weights": 5.0,
               "delays": {"uniform": {"min": 2.75, "max": 3.25}}}

print("Connecting: thalamo-cortical")

#! Horizontally tuned
thalCorRect.update({"mask": {"rectangular": {"lower_left" : [-4.0*dpc, -1.0*dpc],
                                             "upper_right": [ 4.0*dpc,  1.0*dpc]}}})
for conn in [{"targets": {"model": "L4pyr" }, "kernel": 0.5},
             {"targets": {"model": "L56pyr"}, "kernel": 0.3}]:
    thalCorRect.update(conn)
    topo.ConnectLayers(Tp, Vp_h, thalCorRect)

#! Vertically tuned
thalCorRect.update({"mask": {"rectangular": {"lower_left" : [-1.0*dpc, -4.0*dpc],
                                             "upper_right": [ 1.0*dpc,  4.0*dpc]}}})
for conn in [{"targets": {"model": "L4pyr" }, "kernel": 0.5},
             {"targets": {"model": "L56pyr"}, "kernel": 0.3}]:
    thalCorRect.update(conn)
    topo.ConnectLayers(Tp, Vp_v, thalCorRect)

#! Diffuse connections
thalCorDiff = {"connection_type": "convergent",
               "sources": {"model": "TpRelay"},
               "synapse_model": "AMPA",
               "weights": 5.0,
               "mask": {"circular": {"radius": 5.0 * dpc}},
               "kernel": {"gaussian": {"p_center": 0.1, "sigma": 7.5 * dpc}},
               "delays": {"uniform": {"min": 2.75, "max": 3.25}}}

#---keiko
for conn in [{"targets": {"model": "L4in"}},
             {"targets": {"model": "L56in"}}]:
#---original
#for conn in [{"targets": {"model": "L4pyr"}},
#             {"targets": {"model": "L56pyr"}}]:
    thalCorDiff.update(conn)
    topo.ConnectLayers(Tp, Vp_h, thalCorDiff)
    topo.ConnectLayers(Tp, Vp_v, thalCorDiff)

#! Thalamic connections
#! --------------------

#! Connections inside thalamus, including Rp
#!
#! *Note:* In Hill & Tononi, the inhibition between Rp cells is mediated by
#! GABA_B receptors. We use GABA_A receptors here to provide some self-dampening
#! of Rp.
#!
#! **Note:** The following code had a serious bug in v. 0.1: During the first
#! iteration of the loop, "synapse_model" and "weights" were set to "AMPA" and "0.1",
#! respectively and remained unchanged, so that all connections were created as 
#! excitatory connections, even though they should have been inhibitory. We now
#! specify synapse_model and weight explicitly for each connection to avoid this.

thalBase = {"connection_type": "divergent",
            "delays": {"uniform": {"min": 1.75, "max": 2.25}}}

print("Connecting: intra-thalamic")

for src, tgt, conn in [(Tp, Rp, {"sources": {"model": "TpRelay"},
                                 "synapse_model": "AMPA",
                                 "mask": {"circular": {"radius": 2.0 * dpc}},
                                 "kernel": {"gaussian": {"p_center": 1.0, "sigma": 7.5 * dpc}},
                                 "weights": 2.0}),
                       (Tp, Tp, {"sources": {"model": "TpInter"},
                                 "targets": {"model": "TpRelay"},
                                 "synapse_model": "GABA_A",
                                 "weights": -1.0,
                                 "mask": {"circular": {"radius": 2.0 * dpc}},
                                 "kernel": {"gaussian": {"p_center": 0.25, "sigma": 7.5 * dpc}}}),
                       (Tp, Tp, {"sources": {"model": "TpInter"},
                                 "targets": {"model": "TpInter"},
                                 "synapse_model": "GABA_A",
                                 "weights": -1.0,
                                 "mask": {"circular": {"radius": 2.0 * dpc}},
                                 "kernel": {"gaussian": {"p_center": 0.25, "sigma": 7.5 * dpc}}}),
                       (Rp, Tp, {"targets": {"model": "TpRelay"},
                                 "synapse_model": "GABA_A",
                                 "weights": -1.0,
                                 "mask": {"circular": {"radius": 12.0 * dpc}},
                                 "kernel": {"gaussian": {"p_center": 0.15, "sigma": 7.5 * dpc}}}),
                       (Rp, Tp, {"targets": {"model": "TpInter"},
                                 "synapse_model": "GABA_A",
                                 "weights": -1.0,
                                 "mask": {"circular": {"radius": 12.0 * dpc}},
                                 "kernel": {"gaussian": {"p_center": 0.15, "sigma": 7.5 * dpc}}}),
                       (Rp, Rp, {"targets": {"model": "RpNeuron"},
                                 "synapse_model": "GABA_A",
                                 "weights": -1.0,
                                 "mask": {"circular": {"radius": 12.0 * dpc}},
                                 "kernel": {"gaussian": {"p_center": 0.5, "sigma": 7.5 * dpc}}})]:
    thalBase.update(conn)
    topo.ConnectLayers(src, tgt, thalBase)

#! Thalamic input
#! --------------

#! Input to the thalamus from the retina.
#!
#! **Note:** Hill & Tononi specify a delay of 0 ms for this connection.
#! We use 1 ms here.
retThal = {"connection_type": "divergent",
           "synapse_model": "AMPA",
           "mask": {"circular": {"radius": 1.0 * dpc}},
           "kernel": {"gaussian": {"p_center": 0.75, "sigma": 2.5 * dpc}},
           "weights": 10.0,
           "delays": 1.0}

print("Connecting: retino-thalamic")

for conn in [{"targets": {"model": "TpRelay"}},
             {"targets": {"model": "TpInter"}}]:
    retThal.update(conn)
    topo.ConnectLayers(retina, Tp, retThal)


#! Checks on connections
#! ---------------------

#! As a very simple check on the connections created, we inspect
#! the connections from the central node of various layers. 

#! Connections from Retina to TpRelay
#topo.PlotTargets(topo.FindCenterElement(retina), Tp, 'TpRelay', 'AMPA')
#pylab.title('Connections Retina -> TpRelay')
#pylab.show()

#! Connections from TpRelay to L4pyr in Vp (horizontally tuned)
#topo.PlotTargets(topo.FindCenterElement(Tp), Vp_h, 'L4pyr', 'AMPA')
#pylab.title('Connections TpRelay -> Vp(h) L4pyr')
#pylab.show()

#! Connections from TpRelay to L4pyr in Vp (vertically tuned)
#topo.PlotTargets(topo.FindCenterElement(Tp), Vp_v, 'L4pyr', 'AMPA')
#pylab.title('Connections TpRelay -> Vp(v) L4pyr')
#pylab.show()

#! Recording devices
#! =================

#! This recording device setup is a bit makeshift. For each population
#! we want to record from, we create one ``multimeter``, then select
#! all nodes of the right model from the target population and
#! connect. ``loc`` is the subplot location for the layer.
print("Connecting: Recording devices")
recorders = {}
for name, loc, population, model in [('TpRelay'   , 1, Tp  , 'TpRelay'),
                                     ('Rp'        , 2, Rp  , 'RpNeuron'),
                                     ('Vp_v L4pyr', 3, Vp_v, 'L4pyr'),
                                     ('Vp_h L4pyr', 4, Vp_h, 'L4pyr')]:
    recorders[name] = (nest.Create('RecordingNode'), loc)
    tgts = [nd for nd in nest.GetLeaves(population)[0]
            if nest.GetStatus([nd], 'model')[0]==model]
    nest.Connect(recorders[name][0], tgts)   # one recorder to all targets

#! create the spike detectors
detectors = {}
nraster = 0
for name, loc, population, model in [('Vp_v L4pyr', 3, Vp_v, 'L4pyr'),
                                     ('Vp_h L4pyr', 4, Vp_h, 'L4pyr')]:
    tgts = [nd for nd in nest.GetLeaves(population)[0]
            if nest.GetStatus([nd], 'model')[0]==model]
    detectors[name] = (nest.Create('spike_detector', params={"withgid": True, "withtime": True}), loc, tgts)
    nraster += max(tgts) - min(tgts) + 1
    nest.Connect(tgts, detectors[name][0])

#! Example simulation
#! ====================

#! This simulation is set up to create a step-wise visualization of
#! the membrane potential. To do so, we simulate ``sim_interval``
#! milliseconds at a time, then read out data from the multimeters,
#! clear data from the multimeters and plot the data as pseudocolor
#! plots. 

#! show time during simulation
nest.SetStatus([0],{'print_time': True})

#! lower and upper limits for color scale, for each of the four
#! populations recorded.
vmn=[-80,-80,-80,-80]
vmx=[-50,-50,-50,-50]
#vmn=[-62,-62,-62,-62]
#vmx=[-55,-55,-55,-55]

#nest.Simulate(Params['sim_interval'])

# #! loop over simulation intervals
# nsteps = len(pylab.arange(Params['sim_interval'], Params['simtime'], Params['sim_interval']))
# raster_plot = np.zeros((nraster, nsteps))
# step = 0
#
# for t in pylab.arange(Params['sim_interval'], Params['simtime'], Params['sim_interval']):
#
#     # do the simulation
#     nest.Simulate(Params['sim_interval'])
#
#     # clear figure and choose colormap
#     pylab.clf()
#     pylab.jet()
#
#     # now plot data from each recorder in turn, assume four recorders
#     for name, r in recorders.items():
#         rec = r[0]
#         sp = r[1]
#         pylab.subplot(2,2,sp)
#
#         d = nest.GetStatus(rec)[0]['events']['V_m']
#         idx = nest.GetStatus(rec)[0]['events']['senders'] #---keiko
#
#         if len(d) != Params['N']**2:
#             # cortical layer with two neurons in each location, take average
#             d = 0.5 * ( d[::2] + d[1::2] )
#
#         # clear data from multimeter
#         nest.SetStatus(rec, {'n_events': 0})
#
#         #---keiko
#         #print(name)
#         #print('length d')
#         #print(len(d))
#         #print('length idx')
#         #print(len(idx))
#         #f_Vm.write(str(pylab.reshape(d,(1,len(d)))))
#         #f_idx.write(str(pylab.reshape(idx,(1,len(d)))))
#         #writer_Vm.writerows(pylab.reshape(d,(1,len(d))))
#         #writer_idx.writerows(pylab.reshape(idx,(1,len(idx))))
#         #print(Params['N'])
#
#         pylab.imshow(pylab.reshape(d, (Params['N'],Params['N'])),
#                      aspect='equal', interpolation='nearest',
#                      extent=(0,Params['N']+1,0,Params['N']+1),
#                      vmin=vmn[sp-1], vmax=vmx[sp-1])
#         pylab.colorbar()
#         pylab.title(name + ', t = %6.1f ms' % nest.GetKernelStatus()['time'])
#
#     #pylab.draw()  # force drawing inside loop
#     #pylab.show()  # required by ``pyreport``
#
#     pylab.savefig('./figures/recorders/lambda_dg_%f_t_%f.png' % (Params['lambda_dg'], t))
#
#     # save rater information
#     curpos = 0
#     for name, r in detectors.items():
#         rec = r[0]
#         sp = r[1]
#         tgts = r[2]
#
#         senders = nest.GetStatus(rec)[0]['events']['senders']
#         times = nest.GetStatus(rec)[0]['events']['times']
#
#         nneurons = max(tgts) - min(tgts)
#         this_neurons = np.zeros(nneurons+1)
#         this_neurons[senders-min(tgts)] = times
#         raster_plot[curpos:curpos+nneurons+1, step] = this_neurons
#         curpos = curpos + nneurons + 1
#
#     # is cummulative, so remove last count
#     # if ep > 0:
#     #   raster_plot[:,step] -= raster_plot[:,step-1]
#
#     print(raster_plot[0,:])
#     step += 1
#
# # create raster figure
# pylab.clf()
#
# #      =========   =======================================================
# #      Colormap    Description
# #      =========   =======================================================
# #      autumn      sequential linearly-increasing shades of red-orange-yellow
# #      bone        sequential increasing black-white color map with
# #                  a tinge of blue, to emulate X-ray film
# #      cool        linearly-decreasing shades of cyan-magenta
# #      copper      sequential increasing shades of black-copper
# #      flag        repetitive red-white-blue-black pattern (not cyclic at
# #                  endpoints)
# #      gray        sequential linearly-increasing black-to-white
# #                  grayscale
# #      hot         sequential black-red-yellow-white, to emulate blackbody
# #                  radiation from an object at increasing temperatures
# #      hsv         cyclic red-yellow-green-cyan-blue-magenta-red, formed
# #                  by changing the hue component in the HSV color space
#      inferno     perceptually uniform shades of black-red-yellow
#      jet         a spectral map with dark endpoints, blue-cyan-yellow-red;
#                  based on a fluid-jet simulation by NCSA [#]_
#      magma       perceptually uniform shades of black-red-white
#      pink        sequential increasing pastel black-pink-white, meant
#                  for sepia tone colorization of photographs


# #pylab.set_cmap('hot')
# pylab.set_cmap('gray')
#
# figure = pylab.gcf() # get current figure
# figure.set_size_inches(5, 3)
#
#
# pylab.imshow(raster_plot, aspect='auto', interpolation='nearest')
# pylab.colorbar()
#
# font = {'family' : 'normal',
#         'weight' : 'normal',
#         'size'   : 8}

# pylab.rc('font', **font)
#
# print('printing raster for lambda_dg == %f' % Params['lambda_dg'])
# pylab.savefig('./figures/detectors/spikes_lambda_dg_%f.png' % Params['lambda_dg'], dpi = 300)

# run simulation
nest.Simulate(Params['simtime'])

import os.path

data_folder = './data/detectors'
if not os.path.isdir(data_folder):
    os.makedirs(data_folder)

figure_folder = './figures/detectors'
if not os.path.isdir(figure_folder):
    os.makedirs(figure_folder)

# save raster plot and data
for name, r in detectors.items():
    rec = r[0]

    # raster plot
    p = raster_plot.from_device(rec, hist=True)
    f = p[0].figure
    f.set_size_inches(5, 3)
    f.savefig(figure_folder + '/spikes_' + name + '.png', dpi=300)

    # data
    spikes = nest.GetStatus(rec, "events")[0]
    print ('Saving spikes for ' + name)
    with open(data_folder + '/spikes_' + name + '.pickle', 'w') as f:
        pickle.dump(spikes, f)

    scipy.io.savemat(data_folder + '/spikes_' + name + '.mat', mdict={'senders': spikes['senders'], 'times': spikes['times']})

#! just for some information at the end
print(nest.GetKernelStatus())

#--- keiko 9/14/2016
f_Vm.close()
f_idx.close()