Changeset 427

Timestamp:

07/27/08 18:22:35 (3 weeks ago)

Author:

pierre

Message:

Some work performed on the brian implementation of the pyNN API, after having met Dan at the CNS Workshop. Still experimental, VA benchmark is working but not Brunel one

Files:

• trunk/src/brian/__init__.py (modified) (15 diffs)
• trunk/src/brian/cells.py (modified) (4 diffs)
• trunk/src/brian/connectors.py (modified) (3 diffs)
• trunk/src/brian/synapses.py (modified) (1 diff)

trunk/src/brian/__init__.py

@@ -36 +36 @@
-        int.__init__(n)
-        common.IDMixin.__init__(self)
+    
+    def __getattr__(self, name):
+        try:
+            val = float(self.parent.brian_cells[int(self)].__getattr__(name))
+        except KeyError:
+            raise NonExistentParameterError(name, self.cellclass)
+        return val
+    
+    def set_native_parameters(self, parameters):
+        for key, value in parameters.items():
+            self.parent.brian_cells[int(self)].__setattr__(key,value)
+        
+
-        
-def list_standard_models():
@@ -78 +91 @@
-    min_delay = 0.001*min_delay
-    max_delay = 0.001*max_delay
-
-
+ 
+ 
-    return 0
-
@@ -225 +238 @@
-            if isinstance(self.celltype,SpikeSourcePoisson):
-                rate       = self.cellparams['rate']
-
+
+
-            else:
-                v_thresh   = self.cellparams['v_thresh']
-                v_reset    = self.cellparams['v_reset']
-                tau_refrac = self.cellparams['tau_refrac']
-cell = brian.NeuronGroup(self.size,model=cellclass.eqs,threshold=v_thresh,reset=v_reset, refractory=tau_refrac, clock=simclock
+brian_cells = brian.NeuronGroup(self.size,model=cellclass.eqs,threshold=v_thresh,reset=v_reset, refractory=tau_refrac, clock=simclock, compile=True
-
-        elif isinstance(cellclass, str):
-
+
+
+
+
-            self.cellparams = self.celltype.parameters
-
-nchangeable_params=['v_thresh','v_reset','tau_refrac
+seless_params=['v_thresh','v_reset','tau_refrac','cm
-        if self.cellparams:
-            for key, value in self.cellparams.items():
-                if not key in unchangeable_params:
-
-
-
+
+
+
+
+
-        self.spike_recorder = None
-        self.vm_recorder    = None
+        self.ce_recorder    = None
+        self.ci_recorder    = None
+        self.first_id       = 0
-        
-        if not self.label:
-            self.label = 'population%d' % Population.nPop
-        Population.nPop += 1
-cell
+brian_cells
-    
-    def __getitem__(self, addr):
@@ -261 +283 @@
-            addr = (addr,)
-        if len(addr) == self.ndim:
-_id
+
-        else:
-            raise common.InvalidDimensionsError, "Population has %d dimensions. Address was %s" % (self.ndim, str(addr))
-        if addr != self.locate(id):
-            raise IndexError, 'Invalid cell address %s' % str(addr)
-self.cell[id]
+id
-    
-    def __iter__(self):
@@ -322 +344 @@
-        Get the values of a parameter for every cell in the population.
-        """
-cell
+brian_cells
-        if as_array:
-            values = numpy.array(values)
@@ -346 +368 @@
-            raise common.InvalidParameterValueError
-        for key, value in para_dict.items():
-cell
+brian_cells
-        
-
@@ -385 +407 @@
-        else:
-            rarr = rand_distr.next(n=self.size)
-cell_id
-cell
+brian_cells
+brian_cells
-        
-    def _call(self, methodname, arguments):
@@ -399 +421 @@
-        """
-        `Topographic' call. Calls the method methodname() for every cell in the 
-
+    
-        cell. objarr is an array with the same dimensions as the Population.
-        e.g. p.tcall("memb_init", vinitArray) calls
@@ -421 +443 @@
-                N = record_from
-            print "Warning: Brian can record only the %d first cells of the population" %N
-cell
-
-cell
+brian_cells
+
+brian_cells
-        net.add(self.spike_recorder)
-
@@ -436 +458 @@
-        global net
-        if record_from:
-            if isinstance(record_from,list):
-                N = len(record_from)
-            if isinstance(record_from,int):
-                N = record_from
-print "Warning: Brian can record only the %d first cells of the population" %N
-cell[0:N],'v',record=True
-
-cell
+    record_from = numpy.random.permutation(self.cell.flatten()[0:N])
+brian_cells,'v',record=record_from
+
+brian_cells
-        net.add(self.vm_recorder)
+    
+    def record_c(self, record_from=None, rng=None, to_file=True):
+        """
+        If record_from is not given, record the conductances/currents for all cells in
+        the Population.
+        record_from can be an integer - the number of cells to record from, chosen
+        at random (in this case a random number generator can also be supplied)
+        - or a list containing the ids of the cells to record.
+        """
+        global net
+        if record_from:
+            if isinstance(record_from,int):
+                N = record_from
+                record_from = numpy.random.permutation(self.cell.flatten()[0:N])
+            self.ce_recorder = brian.StateMonitor(self.brian_cells,'ge',record=record_from)
+            self.ci_recorder = brian.StateMonitor(self.brian_cells,'gi',record=record_from)
+        else:
+            self.ce_recorder = brian.StateMonitor(self.brian_cells,'ge',record=True)
+            self.ci_recorder = brian.StateMonitor(self.brian_cells,'gi',record=True)
+        net.add(self.ce_recorder)
+        net.add(self.ci_recorder)
-    
-    def printSpikes(self, filename, gather=True, compatible_output=True):
@@ -471 +512 @@
-            f = open(filename,"w", DEFAULT_BUFFER_SIZE)
-            f.write("# dimensions =" + "\t".join([str(d) for d in self.dim]) + "\n")
+            f.write("# first_id = %d\n" % self.first_id)
+            f.write("# last_id = %d\n" % (self.first_id+len(self)-1,))
-            f.write("# dt = %g\n" % dt)
-
-
+
+
+
+
-            f.close()
-    
@@ -490 +535 @@
-        """
-        # gather is not relevant, but is needed for API consistency
-
+
+
+
+
-
-    def randomInit(self, rand_distr):
@@ -497 +545 @@
-        random values.
-        """
-_init
+
-
-
@@ -524 +572 @@
-            N = len(self.vm_recorder[0])
-            f.write("# dimensions =" + "\t".join([str(d) for d in self.dim]) + "\n")
+            f.write("# first_id = %d\n" % self.first_id)
+            f.write("# last_id = %d\n" % (self.first_id+len(self)-1,))
-            f.write("# dt = %g\n" % dt)
-            f.write("# n = %d\n" % N)
-            cells = self.vm_recorder.get_record_indices()
-            for cell in cells:
+                vm = 1000*self.vm_recorder[cell]
-                for idx in xrange(N):
-self.vm_recorder[cell][idx]*1000.
+vm[idx]
-            f.close()
-        pass
-
-    
@@ -546 +596 @@
-        
-        source - string specifying which attribute of the presynaptic cell
-
+s
-                 
-        target - string specifying which synapse on the postsynaptic cell to

trunk/src/brian/cells.py

@@ -14 +14 @@
-    shaped post-synaptic current."""
-    translations = common.build_translations(
+        ('v_rest',     'v_rest', mV),
+        ('v_reset',    'v_reset', mV),
+        ('cm',         'cm'), # C is in pF, cm in nF
+        ('tau_m',      'tau_m', ms),
+        ('tau_refrac', 'tau_refrac',"max(get_time_step(), tau_refrac*0.001)", "tau_refrac"),
+        ('tau_syn_E',  'tau_syn_E', ms),
+        ('tau_syn_I',  'tau_syn_I', ms),
+        ('v_thresh',   'v_thresh', mV),
+        ('i_offset',   'i_offset', pA), # I0 is in pA, i_offset in nA
+        ('v_init',     'v', mV),
+    )
+    eqs= brian.Equations('''
+        dv/dt  = (ge + gi + (v_rest-v))/tau_m : volt
+        dge/dt = (ye-ge)/tau_syn_E              : 1 
+        dye/dt = -ye/tau_syn_E                  : 1
+        dgi/dt = (yi-gi)/tau_syn_I              : 1 
+        dyi/dt = -yi/tau_syn_I                  : 1
+        tau_syn_E : second
+        tau_syn_I : second
+        tau_m     : second
+        v_rest    : volt
+        '''
+        )
+
+
+class IF_curr_exp(common.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."""
+    
+    translations = common.build_translations(
-        ('v_rest',     'v_rest', 0.001),
-        ('v_reset',    'v_reset', 0.001),
-        ('cm',         'cm'), # C is in pF, cm in nF
-        ('tau_m',      'tau_m', 0.001),
- "max(get_time_step(), tau_refrac
+"max(get_time_step(), tau_refrac*0.001
-        ('tau_syn_E',  'tau_syn_E', 0.001),
-        ('tau_syn_I',  'tau_syn_I', 0.001),
@@ -26 +57 @@
-    )
-    eqs= brian.Equations('''
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-    
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-class IF_cond_alpha(common.ModelNotAvailable):
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@@ -76 +102 @@
-    """Leaky integrate and fire model with fixed threshold and 
-    exponentially-decaying post-synaptic conductance."""
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-class IF_facets_hardware1(common.IF_facets_hardware1):
@@ -92 +142 @@
-    translations = common.build_translations(
-        ('rate',     'rate'),
-
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-    
-class SpikeSourceArray(common.SpikeSourceArray):
-    """Spike source generating spikes at the times given in the spike_times array."""
-
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+
-class EIF_cond_alpha_isfa_ista(common.ModelNotAvailable):
-    pass
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-class SpikeSourceInhGamma(common.ModelNotAvailable):

trunk/src/brian/connectors.py

@@ -10 +10 @@
-from pyNN.random import RandomDistribution, NativeRNG
-from math import *
+from numpy import arccos, arcsin, arctan, arctan2, ceil, cos, cosh, e, exp, \
+                  fabs, floor, fmod, hypot, ldexp, log, log10, modf, pi, power, \
+                  sin, sinh, sqrt, tan, tanh
+
+def _targetConnection(Connector, projection):
+    if projection.synapse_type == "excitatory":
+        target="ge"
+    else:
+        target="gi"
+    return brian.Connection(projection.pre.brian_cells,projection.post.brian_cells,target, delay=Connector.delays*0.001)
-
-class AllToAllConnector(common.AllToAllConnector):    
-    
-    def connect(self, projection):
-
-
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+
-        return projection._connections.W.getnnz()
-
@@ -24 +31 @@
-    
-    def connect(self, projection):
-
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-    
-class FixedProbabilityConnector(common.FixedProbabilityConnector):
-    
-    def connect(self, projection):
-
-
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-        return projection._connections.W.getnnz()
-    
@@ -39 +45 @@
-    
-    def connect(self, projection):
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-    
-

trunk/src/brian/synapses.py

@@ -8 +8 @@
-
-class SynapseDynamics(common.SynapseDynamics):
-*args, **kwargs
-raise NotImplementedError("Dynamic synapses are not available for this simulator."
+fast=None, slow=None
+common.SynapseDynamics.__init__(self, fast, slow
-
-class STDPMechanism(common.STDPMechanism):
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-class TsodkysMarkramMechanism(common.ModelNotAvailable):
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-class AdditiveWeightDependence(common.ModelNotAvailable):