Changeset 48

Timestamp:

05/11/07 11:35:19 (2 years ago)

Author:

apdavison

Message:

Merged trunk changes r37:47 into pyNN-0.3 branch

Files:

• branches/0.3/common.py (modified) (2 diffs)
• branches/0.3/doc/lowlevelapi.txt (modified) (3 diffs)
• branches/0.3/doc/standardmodels.txt (modified) (1 diff)
• branches/0.3/facetsml.py (modified) (3 diffs)
• branches/0.3/hoc/standardCells.hoc (modified) (2 diffs)
• branches/0.3/nest.py (modified) (9 diffs)
• branches/0.3/neuron.py (modified) (6 diffs)
• branches/0.3/oldneuron.py (modified) (4 diffs)
• branches/0.3/pcsim.py (modified) (21 diffs)
• branches/0.3/test/IF_cond_alpha.py (modified) (1 diff)
• branches/0.3/test/IF_curr_exp.py (modified) (1 diff)
• branches/0.3/test/pyNNPcsimLowLevelTest.py (modified) (5 diffs)
• branches/0.3/test/runtests.py (modified) (4 diffs)

branches/0.3/common.py

@@ -149 +149 @@
-
-class IF_cond_alpha(StandardCellType):
-#
-#
-#
+
+
+
-    default_parameters = {
-        'v_rest'     : -65.0,   # Resting membrane potential in mV. 
@@ -218 +218 @@
-    both be individual cells or lists of cells, in which case all possible
-    connections are made with probability p, using either the random number
-
+
+
-    pass
-

branches/0.3/doc/lowlevelapi.txt

@@ -2 +2 @@
-Neurons and Connections
-=======================
+
+Initialising the simulator
+==========================
+
+Before using any other functions or classes from PyNN, the user must call the ``setup()`` function::
+
+    >>> setup()
+    
+``setup()`` takes various optional arguments: setting the simulation timestep (there is currently no support in the API for variable timestep methods although native simulator code can be used to select this option where the simulator supports it), setting the minimum and maximum synaptic delays, and turning debugging output on and off, e.g.::
+
+    >>> setup(timestep=0.1, min_delay=0.1, max_delay=0.1, debug=False)
+    
+Debugging information is written to a log file in the working directory.
-
-Creating neurons
@@ -55 +68 @@
-    >>> connect(spike_source, cells)
-    
-
+`
-For clarity, we could also have specified the argument names::
-
@@ -110 +123 @@
-Recording spikes and membrane potential
-=======================================
+
+To record action potentials use the ``record()`` function and to record membrane potential use the ``record_v()`` function.
+The arguments for both functions are a cell id or list of ids, and a filename, e.g.::
+
+    >>> record(cell, "spikes.dat")
+    >>> record_v(cell_list, "allspikes.dat")
+    
+By default, all simulators write data files in the same format.
+
+The beginning of a typical spike file looks like::
+
+    # dt = 0.1
+    # n = 1000
+    0.0     2
+    0.3     5
+    0.4     3
+    0.9     2
+    1.0     1
+    . . .
+    
+The beginning of a typical membrane potential file looks like::
+
+    # dt = 0.1
+    # n = 1000
+    -65.0   0
+    -64.9   0
+    -64.7   0
+    -64.5   0
+    . . .
+
+Both file types begin with header lines giving the timestep (there is currently no support for variable-time step recording) and the number of data points [per cell or total?] in the file.
+Each line of the spike file then gives the occurence time of a spike (in ms) and the id of the neuron in which it was recorded.
+Each line of the membrane potential file gives the membrane potential (in mV) followed by the id of the neuron in which it was recorded.
+In both cases, whether the file is sorted by cell id or by time depends on the simulator: it is not standardised.
+
+In some cases it is more efficient to write files in the simulator's native format, rather than the standard PyNN format.
+In this case, use the ``compatible_output=False`` argument to the ``end()`` function.
+
+Considerable enhancements to file formats are planned for future releases of PyNN, including recording to binary (HDF5) rather than text files, support for variable time-step recording, and user-specified output formats.
+
+Running a simulation
+====================
+
+The ``run()`` function runs the simulation for a given number of milliseconds, e.g.::
+
+    >>> run(1000.0)
+    
+Finishing up
+============
+
+Just as a simulation must be begun with a call to ``setup()``, it must be ended with a call to ``end()``.
+
+Example
+=======
+
+We now give a full example of a PyNN simulation script::
+
+    from pyNN.nest import *
+    
+    <insert IF_curr_alpha.py here (with line numbers?)>
+    
+To run the script with NEURON, replace the first line with::
+
+    from pyNN.neuron import *
+    
+and to run the script with PCSIM::
+
+    from pyNN.pcsim import *
+    

branches/0.3/doc/standardmodels.txt

@@ -2 +2 @@
-Standard models
-===============
+
+Standard models are neuron models that are available in at least two of the simulation engines supported by PyNN.
+PyNN performs automatic translation of parameter names, types and units.
+Only a handful of models are currently available, but the list will be expanded in future releases.
+
+Integrate-and-fire neurons
+==========================
+
+IF_curr_alpha
+-------------
+
+Leaky integrate and fire model with fixed threshold and alpha-function-shaped post-synaptic current.
+
+Availability: NEST, NEURON, PCSIM
+
+==============  =============  =====  ========================================
+Name            Default value  Units  Description
+==============  =============  =====  ========================================
+``v_rest``      -65.0          mV     Resting membrane potential 
+``cm``            1.0          nF     Capacity of the membrane
+``tau_m``        20.0          ms     Membrane time constant
+``tau_refrac``    0.0          ms     Duration of refractory period
+``tau_syn``       5.0          ms     Rise time of the synaptic alpha function
+``i_offset``      0.0          nA     Offset current
+``v_reset``     -65.0          mV     Reset potential after a spike
+``v_thresh``    -50.0          mV     Spike threshold
+``v_init``      -65.0          mV     Membrane potential at ``t`` = 0
+==============  =============  =====  ========================================
+
+
+IF_curr_exp
+-----------
+
+Leaky integrate and fire model with fixed threshold and decaying-exponential post-synaptic current.
+(Separate synaptic currents for excitatory and inhibitory synapses.
+    
+Availability: NEST, NEURON, PCSIM
+    
+==============  =============  =====  =========================================
+Name            Default value  Units  Description
+==============  =============  =====  =========================================
+``v_rest``      -65.0          mV     Resting membrane potential
+``cm``            1.0          nF     Capacity of the membrane
+``tau_m``        20.0          ms     Membrane time constant
+``tau_refrac``    0.0          ms     Duration of refractory period
+``tau_syn_E``     5.0          ms     Decay time of excitatory synaptic current
+``tau_syn_I``     5.0          ms     Decay time of inhibitory synaptic current
+``i_offset``      0.0          nA     Offset current
+``v_reset``     -65.0          mV     Reset potential after a spike
+``v_thresh``    -50.0          mV     Spike threshold
+``v_init``      -65.0          mV     Membrane potential ``t`` = 0
+==============  =============  =====  =========================================
+
+
+IF_cond_alpha
+_____________
+
+Leaky integrate and fire model with fixed threshold and alpha-function-shaped post-synaptic conductance.
+
+Availability: NEST, NEURON, PCSIM
+
+==============  =============  =====  ===================================================
+Name            Default value  Units  Description
+==============  =============  =====  ===================================================
+``v_rest``      -65.0          mV     Resting membrane potential 
+``cm``            1.0          nF     Capacity of the membrane
+``tau_m``        20.0          ms     Membrane time constant
+``tau_refrac``    0.0          ms     Duration of refractory period
+``tau_syn_E``     5.0          ms     Rise time of the excitatory synaptic alpha function
+``tau_syn_I``     5.0          ms     Rise time of the inhibitory synaptic alpha function
+``e_rev_E``       0.0          mV     Reversal potential for excitatory input
+``e_rev_I``     -70.0          mV     Reversal potential for inhibitory input
+``v_thresh``    -50.0          mV     Spike threshold
+``v_reset``     -65.0          mV     Reset potential after a spike
+``i_offset``      0.0          nA     Offset current
+``v_init``      -65.0          mV     Membrane potential at ``t`` = 0
+==============  =============  =====  ===================================================
+
+
+
+Spike sources
+=============
+
+SpikeSourcePoisson
+------------------
+
+Spike source, generating spikes according to a Poisson process.
+
+Availability: NEST, NEURON, PCSIM
+[note problems with this in NEST]
+
+============  =============  ======  ==========================
+Name          Default value  Units   Description
+============  =============  ======  ==========================
+``rate`       0.0            s^`-1`  Mean spike frequency
+``start``     0.0            ms      Start time
+``duration``  10^9           ms      Duration of spike sequence
+============  =============  ======  ==========================
+
+
+SpikeSourceArray
+----------------
+Spike source generating spikes at the times given in the ``spike_times`` array.
+
+Availability: NEST, NEURON, PCSIM
+
+===============  =============  ======  ==========================
+Name             Default value  Units   Description
+===============  =============  ======  ==========================
+``spike_times``  ``[]``         ms      list or numpy array containing spike times
+===============  =============  ======  ==========================

branches/0.3/facetsml.py

@@ -5 +5 @@
-
-import common
-
+#
-import RandomArray
+from xml.dom import *
+from xml.dom.minidom import *
+from xml.dom.ext import *
+
-        
-open_files = []
-
-dt = 0.1
+xmldoc = Document()
+
+"""
+warning :
+    in order to write xml in a format which respects the namespaces, you must use xml.dom.ext.PrettyPrint
+namespaces allowed are :
+        neuromlNode.setAttribute('xmlns:net','http://morphml.org/networkml/schema')
+        neuromlNode.setAttribute('xmlns:mml','http://morphml.org/morphml/schema')
+        neuromlNode.setAttribute('xmlns:meta','http://morphml.org/metadata/schema')
+        neuromlNode.setAttribute('xmlns:bio','http://morphml.org/biophysics/schema')
+        neuromlNode.setAttribute('xmlns:cml','http://morphml.org/channelml/schema')
+
+"""
+
+def initDocument(parentElementNS,parentElementName,prefix=''):
+    """
+    create the root element <neuroml> if doesn't exist
+    and the specified parentElement just below <neuroml> if doesn't exist
+    returns the parentElement node
+    """
+    neuromlNodes = xmldoc.getElementsByTagNameNS('http://morphml.org/neuroml/schema','neuroml')
+    #if the <neuroml> markup is not yet created
+    if(neuromlNodes.length == 0):
+        #seems createElementNS doesn't create the xmlns attribute
+        neuromlNode = xmldoc.createElementNS('http://morphml.org/neuroml/schema','neuroml')
+        xmldoc.appendChild(neuromlNode)
+    else:
+        neuromlNode = neuromlNodes[0]
+    parentElementNodes = neuromlNode.getElementsByTagNameNS(parentElementNS,parentElementName)
+    if(parentElementNodes.length == 0):
+        if(prefix == ''):
+            parentElementNode = xmldoc.createElementNS(parentElementNS,parentElementName)
+        else:
+            parentElementNode = xmldoc.createElementNS(parentElementNS,prefix + ":" + parentElementName)
+        neuromlNode.appendChild(parentElementNode)
+    else:
+        parentElementNode = parentElementNodes[0]
+    return parentElementNode
-
-
@@ -222 +264 @@
-    nPop = 0
-    
-type
+class
-        """
-        dims should be a tuple containing the population dimensions, or a single
@@ -232 +274 @@
-          constructor
-        label is an optional name for the population.
-
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-                     
-        self.translate = getattr(StandardCells,self.celltype) # move this to common.Population.__init__()?
-        
-        if not self.label:
-            self.label = 'population%d' % Population.nPop
-
-
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-        
-        
-        Population.nPop += 1
+    PrettyPrint(xmldoc)
+    
-        
-    def set(self,param,val):

branches/0.3/hoc/standardCells.hoc

@@ -32 +32 @@
-  if ($o1.has_key("t_refrac")) { t_refrac = $o1.get("t_refrac") } else { t_refrac = 2 }      // (ms)
-  if ($o1.has_key("i_offset")) { i_offset = $o1.get("i_offset") } else { i_offset = 0 }      // (nA)
+  if ($o1.has_key("v_init"))   { v_init   = $o1.get("v_init") }   else { v_init   = v_rest } // (mV)
-  if ($o1.has_key("tau_syn"))  {
-    tau_e = $o1.get("tau_syn")
@@ -46 +47 @@
-    if ($o1.has_key("e_i"))    { e_i      = $o1.get("e_i") }      else { e_i      = -70 }    // (mV)
-  }
-  v_init   = v_rest
-  fih = new FInitializeHandler("memb_init()",this)
-  fih2 = new FInitializeHandler("param_update()", this)

branches/0.3/nest.py

@@ -97 +97 @@
-class IF_cond_alpha(common.IF_cond_alpha):
-    """Leaky integrate and fire model with fixed threshold and alpha-function-
-urrent
+onductance
-    
-    translations = {
@@ -109 +109 @@
-            'v_thresh'  : ('Theta'       , "parameters['v_thresh']"),
-            'i_offset'  : ('I0'          , "parameters['i_offset']*1000.0"), # I0 is in pA, i_offset in nA
-            'v_init'    : ('u'           , "parameters['v_init']"),
-            'e_rev_E'   : ('V_reversal_E', "parameters['e_rev_E']"),
-            'e_rev_I'   : ('V_reversal_I', "parameters['e_rev_I']"),
+            'v_init'    : ('u'           , "parameters['v_init']"),
-    }
-    nest_name = "iaf_cond_neuron"
@@ -265 +265 @@
-                if p < 1:
-                    if rng: # use the supplied RNG
-self
+rng
-                    else:   # use the default RNG
-                        rarr = numpy.random.uniform(0,1,len(target))
@@ -376 +376 @@
-            if (len(single_line) > 1) and (single_line[1] != '-'):
-               neuron = int(single_line[0])
-g\t%d\n" %(float(single_line[1])
+s\t%d\n" %(single_line[1]
-    else:
-        f = open(tempfilename.replace('/','_'),'r',100)
@@ -456 +456 @@
-            raise IndexError, 'Invalid cell address %s' % str(addr)
-        return id
+    
+    def __getitems__(self,addrs):
+        """Returns the ids of neurons. Input should have format:
+        n = number of synapses; nd = number of dimensions
+        shape(addrs) == (n,nd)
+        """
+        #if isinstance(addr,int):
+        #    addr = (addr,)
+        ids = []
+        if len(addrs[0]) == self.ndim:
+            for addr in range(len(addrs)):
+                try:
+                    ids.append(self.cell[tuple(addrs[addr,:])])
+                except IndexError:
+                    pass                
+        else:
+            raise common.InvalidDimensionsError, "Population has %d dimensions. Address was %s" % (self.ndim,str(len(addrs)))
+        return ids
+
+         #   for syn_nr in range(len(target_position_x)):
+         #       try:
+         #           target_id.append(self.post[(target_position_x[syn_nr],target_position_y[syn_nr],target_position_z[syn_nr])])
+         #       except IndexError:
+         #           target_id.append(False)
+
-    
-    def __len__(self):
@@ -726 +751 @@
-        random values.
-        """
-
-
-
-
+
+
+
+
+
-    
-    def print_v(self,filename,gather=True, compatible_output=True):
@@ -781 +807 @@
-                line = line.rstrip()
-                single_line = line.split("\t", 2)
-
-
+
+
+
-        else:
-            f = open(tempfilename.replace('/','_'),'r',1)
@@ -1166 +1193 @@
-        sigma: sigma of the Gauss
-        """
+
+        #def get_ids(self,parameters):
+            #ids = []
+            #if len(addrs) == self.ndim:
+        #
+        #for addr in range(len(parameters['x'])):
+        #    try:
+        #        ids = numpy.append(ids,post.cell[addr])
+        #    except IndexError:
+        #        pass
+        #else:
+        #    raise common.InvalidDimensionsError, "Population has %d dimensions. Address was %s" % (self.ndim,str(addrs))
+        #return ids.astype('int')
+
-        
-        def rcf_3D(parameters):
@@ -1180 +1221 @@
-            r = rng.normal(scale=sigma,size=n)
-            # for z 
-
-
-
-
+ # here post dim because it does not metter where it comes from in pre dim
+
+.astype('int')
+.astype('int')
-            target_position_z = numpy.floor(h).astype('int')
-            
-            target_id = []
+            # __getitems__ version
+            
+            
-            for syn_nr in range(len(target_position_x)):
-                try:
-
+.cell
-                except IndexError:
-
+
+
-            
-            pynest.divConnect(pre_id,target_id,[weight],[delay])

branches/0.3/neuron.py

@@ -149 +149 @@
-    return hoc_commands, argstr.strip().strip(',')
-
-
+
+
+
+
+
+
+
+
-    if synapse_type:
-        if synapse_type == 'excitatory':
@@ -161 +168 @@
-            syn_objref = synapse_type
-    else:
-
+
+
+
+
-    return syn_objref
-
@@ -268 +278 @@
-class IF_cond_alpha(common.IF_cond_alpha):
-    """Leaky integrate and fire model with fixed threshold and alpha-function-
-urrent
+onductance
-    
-    translations = {
@@ -484 +494 @@
-    if weight is None:  weight = 0.0
-    if delay  is None:  delay = _min_delay
-
+,weight
-    nc_start = ncid
-    hoc_commands = []
@@ -562 +572 @@
-    hoc_execute(hoc_commands, "--- set() ---")
-
-, compatible_output=True
+
-    """Record spikes to a file. source can be an individual cell or a list of
-    cells."""
@@ -579 +589 @@
-    hoc_execute(hoc_commands, "---record() ---")
-
-, compatible_output=True
+
-    """
-    Record membrane potential to a file. source can be an individual cell or

branches/0.3/oldneuron.py

@@ -136 +136 @@
-    return hoc_commands, argstr.strip().strip(',')
-
-
+
+
+
+
+
+
+
+
-    if synapse_type:
-        if synapse_type == 'excitatory':
@@ -145 +152 @@
-            # More sophisticated treatment needed once we have more sophisticated synapse
-            # models, e.g. NMDA...
-
+
+
-    else:
-
+
+
+
+
-    return syn_objref
-
@@ -259 +270 @@
-class IF_cond_alpha(common.IF_cond_alpha):
-    """Leaky integrate and fire model with fixed threshold and alpha-function-
-urrent
+onductance
-    
-    translations = {
@@ -425 +436 @@
-    if weight is None:  weight = 0.0
-    if delay  is None:  delay = _min_delay
-
+, weight
-    nc_start = hoc_netcons
-    hoc_commands = []

branches/0.3/pcsim.py

@@ -85 +85 @@
-        
-class SpikesMultiChannelRecorder(object):
-
+#
-    
-    def __init__(self, source, filename = None, source_indices = None, gather = False):        
@@ -98 +98 @@
-            Add celllist list to the list of the cells for which spikes 
-            are recorded by this spikes multi recorder
-
+
+
+
-        if not src_indices:
-            src_indices = range(len(self.recordings), len(self.recordings) + len(sources))
@@ -149 +151 @@
-
-class FieldMultiChannelRecorder:
-
+#
-    
-    def __init__(self,sources,filename = None,src_indices = None, gather = False, fieldname = "Vm"):        
-        self.filename = filename
-        self.fieldname = fieldname
-
+
+
-        self.record(sources, src_indices)
-        
@@ -162 +165 @@
-            Add celllist to the list of the cells for which field values
-            are recorded by this field multi recorder
-
+
+
+
-        if not src_indices:
-            src_indices = range(len(self.recordings), len(self.recordings) + len(sources))
-        global pcsim_globals
-        if type(sources) != types.ListType:
-            sources = [sources]        
-        for i,src in zip(src_indices, sources):
-            src_id = SimObject.ID(src)
@@ -174 +177 @@
-            if (src_id.node == pcsim_globals.net.mpi_rank()):
-                self.recordings += [ (i, rec, src) ]
-        
-                
-    def saveValuesH5(self, filename = None):
@@ -188 +190 @@
-        h5file.close()
-        
-
+, compatible_output=True
-        if filename:
-            self.filename = filename
@@ -195 +197 @@
-        f = file(self.filename, "w")
-        all_spikes = []
-
-
-
-
-
+
+
+
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+
-        f.close()
-
@@ -211 +222 @@
-    
-    translations = {        
-
-
-
-
-
-
-
-
-
+*1e-3
+*1e-9
+*1e-3
+*1e-3
+*1e-3
+*1e-3
+*1e-9
+*1e-3
+*1e-3
-    }
-    pcsim_name = "LIFCurrAlphaNeuron"    
@@ -232 +243 @@
-                                                Cm       = self.parameters['Cm'], 
-                                                Vresting = self.parameters['Vresting'], 
-
+
+
-                                                Trefract = self.parameters['Trefract'], 
-                                                Iinject  = self.parameters['Iinject'], 
@@ -247 +259 @@
-    
-    translations = {
-
-
-
-
-
-
-"),
-
-
-
+*1e-3
+*1e-9
+*1e-3
+*1e-3
+*1e-3
+*1e-3
+*1e-9"), # i_offset in nA, Iinject in A
+*1e-3
+*1e-3
+*1e-3
-    }
-    
@@ -269 +281 @@
-        self.simObjFactory = LIFCurrExpNeuron(taum     = self.parameters['taum'], 
-                                              Cm       = self.parameters['Cm'], 
-
+
+
-                                              Vthresh  = self.parameters['Vthresh'], 
-                                              Trefract = self.parameters['Trefract'], 
@@ -278 +291 @@
-                                              TauSynInh = self.parameters['TauSynInh'])
-
+
+#class IF_cond_alpha(common.IF_cond_alpha):
+#    """Leaky integrate and fire model with fixed threshold and alpha-function-
+#    shaped post-synaptic conductance."""
+#    
+#    translations = {        
+#        'tau_m'     : ('taum',      "parameters['tau_m']*1e-3" ) ,
+#        'cm'        : ('Cm',        "parameters['cm']*1e-9"), 
+#        'v_rest'    : ('Vresting',  "parameters['v_rest']*1e-3"), 
+#        'v_thresh'  : ('Vthresh',   "parameters['v_thresh']*1e-3"), 
+#        'v_reset'   : ('Vreset',    "parameters['v_reset']*1e-3"), 
+#        'tau_refrac': ('Trefract',  "parameters['tau_refrac']*1e-3"), 
+#        'i_offset'  : ('Iinject',   "parameters['i_offset']*1e-9"),         
+#        'tau_syn_E' : ('TauSynExc', "parameters['tau_syn_E']*1e-3"),
+#        'tau_syn_I' : ('TauSynInh', "parameters['tau_syn_I']*1e-3"),
+#        'e_rev_E'   : ('ErevExc',   "parameters['e_rev_E']*1e-3"),
+#        'e_rev_I'   : ('ErevInh',   "parameters['e_rev_I']*1e-3"),
+#        'v_init'    : ('Vinit',     "parameters['v_init']*1e-3"), 
+#    }
+#    
+#    pcsim_name = "LIFCondAlphaNeuron"    
+#    simObjFactory = None
+#    
+#        
+#    def __init__(self, parameters):
+#        common.IF_cond_alpha.__init__(self, parameters) # checks supplied parameters and adds default                                               # values for not-specified parameters.
+#        self.parameters = self.translate(self.parameters)                
+#        self.parameters['Inoise'] = 0.0
+#        self.simObjFactory = LIFCondAlphaNeuron(taum      = self.parameters['taum'], 
+#                                                Cm        = self.parameters['Cm'], 
+#                                                Vresting  = self.parameters['Vresting'], 
+#                                                Vthresh   = self.parameters['Vthresh'],
+#                                                Vreset    = self.parameters['Vreset'],
+#                                                Trefract  = self.parameters['Trefract'], 
+#