def _generate_neuron_models(net_id):
txt = """
# Neuron models
"""
for idx, neuron in enumerate(Global._objects['neurons']):
# Name
if neuron.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(neuron._rk_neurons_type)
else:
neuron_name = neuron.name
# Description
description = neuron.short_description
if description == None:
description = "Spiking neuron." if neuron.type == 'spike' else 'Rate-coded neuron'
# Parameters
parameters = extract_parameters(neuron.parameters, neuron.extra_values)
parameters_list = [
["$" + LatexParser._latexify_name(param['name'], []) + "$", param['init'],
_adapt_locality_neuron(param['locality']), param['ctype']]
for param in parameters]
parameters_headers = ["Name", "Default value", "Locality", "Type"]
parameters_table = _make_table(parameters_headers, parameters_list)
if len(parameters) == 0:
parameters_table = "$$\\varnothing$$"
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = _process_variables(variables, neuron=True)
# Spiking neurons
if neuron.type == 'spike':
reset_txt = "* Emit a spike a time $t$.\n"
for r in spike_reset:
reset_txt += "* $" + r + "$\n"
eqs += """
**Spike emission:**
if $%(condition)s$ :
%(reset)s
""" % {'condition': spike_condition, 'reset': reset_txt}
# Possible function
if not neuron.functions == None:
eqs += """
**Functions**
%(functions)s
""" % {'functions': LatexParser._process_functions(neuron.functions, begin="$$", end="$$\n\n")}
# Finalize the template
txt += neuron_tpl % { 'name': neuron_name,
'description': description,
'parameters': parameters_table,
'eqs': eqs}
return txt
def _generate_neuron_models(net_id):
neurons = ""
firstneuron = "\\hdr{2}{D}{Neuron Models}\\\\ \\hline"
neuron_tpl = """
\\noindent
\\begin{tabularx}{\\linewidth}{|p{0.15\\linewidth}|X|}\\hline
%(firstneuron)s
\\textbf{Name} & %(name)s \\\\ \\hline
\\textbf{Type} & %(description)s\\\\ \\hline
\\textbf{%(equation_type)s} &
%(variables)s
%(spike)s
%(functions)s
\\end{tabularx}
\\vspace{2ex}
"""
for idx, neuron in enumerate(Global._objects['neurons']):
# Name
if neuron.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(neuron._rk_neurons_type)
else:
neuron_name = neuron.name
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = ""
for var in variables:
eqs += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': var['latex']}
variables_eqs = """
%(eqs)s
\\\\ \\hline
""" % {'eqs': eqs}
# Spiking neurons have an extra field for the spike condition
spike_extra = ""
if neuron.type == 'spike':
spike_code = "If $" + spike_condition + "$ or $t \leq t^* + t_{\\text{refractory}}$:"
# Reset
spike_code += """
\\begin{enumerate}
\\item Emit a spike at time $t^*$"""
for var in spike_reset:
spike_code += """
\\item $""" + var + "$"
spike_code += """
\\end{enumerate}"""
spike_extra = """
\\textbf{Spiking} &
%(spike)s
\\\\ \\hline
""" % {'spike': spike_code}
# Possible function
functions = ""
if not neuron.functions == None:
functions = """
\\textbf{Functions} &
%(functions)s
\\\\ \\hline
""" % {'functions': LatexParser._process_functions(neuron.functions)}
# Build the dictionary
desc = {
'name': neuron_name,
'description': neuron.short_description,
'firstneuron': firstneuron if idx ==0 else "",
'variables': variables_eqs,
'spike': spike_extra,
'functions': functions,
'equation_type': "Subthreshold dynamics" if neuron.type == 'spike' else 'Equations'
}
# Generate the code depending on the neuron position
neurons += neuron_tpl % desc
return neurons
def _generate_neuron_models(net_id):
neurons = ""
firstneuron = "\\hdr{2}{D}{Neuron Models}\\\\ \\hline"
neuron_tpl = """
\\noindent
\\begin{tabularx}{\\linewidth}{|p{0.15\\linewidth}|X|}\\hline
%(firstneuron)s
\\textbf{Name} & %(name)s \\\\ \\hline
\\textbf{Type} & %(description)s\\\\ \\hline
\\textbf{%(equation_type)s} &
%(variables)s
%(spike)s
%(functions)s
\\end{tabularx}
\\vspace{2ex}
"""
first_neuron = True
for neuron in Global._objects['neurons']:
# skip bold models
if isinstance(neuron, BoldModel):
continue
# Name
if neuron.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(neuron._rk_neurons_type)
else:
neuron_name = neuron.name
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = ""
for var in variables:
eqs += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': var['latex']}
variables_eqs = """
%(eqs)s
\\\\ \\hline
""" % {'eqs': eqs}
# Spiking neurons have an extra field for the spike condition
spike_extra = ""
if neuron.type == 'spike':
spike_code = "If $" + spike_condition + "$ or $t \leq t^* + t_{\\text{refractory}}$:"
# Reset
spike_code += """
\\begin{enumerate}
\\item Emit a spike at time $t^*$"""
for var in spike_reset:
spike_code += """
\\item $""" + var + "$"
spike_code += """
\\end{enumerate}"""
spike_extra = """
\\textbf{Spiking} &
%(spike)s
\\\\ \\hline
""" % {'spike': spike_code}
# Possible function
functions = ""
if not neuron.functions == None:
functions = """
\\textbf{Functions} &
%(functions)s
\\\\ \\hline
""" % {'functions': LatexParser._process_functions(neuron.functions)}
# Build the dictionary
desc = {
'name': neuron_name,
'description': neuron.short_description,
'firstneuron': firstneuron if first_neuron else "",
'variables': variables_eqs,
'spike': spike_extra,
'functions': functions,
'equation_type': "Subthreshold dynamics" if neuron.type == 'spike' else 'Equations'
}
# Generate the code depending on the neuron position
neurons += neuron_tpl % desc
# first_neuron = False, to prevent multiple header
first_neuron = False
return neurons
def _generate_measurements(net_id):
measurements = ""
header = "\\hdr{2}{D}{Bold Measurement Models}\\\\ \\hline"
measurements_template = """
\\noindent
\\begin{tabularx}{\\linewidth}{|p{0.15\\linewidth}|X|}\\hline
%(header)s
\\textbf{Name} & %(name)s \\\\ \\hline
\\textbf{Type} & %(description)s\\\\ \\hline
\\textbf{%(equation_type)s} &
%(variables)s
\\end{tabularx}
\\vspace{2ex}
"""
first_define = True
for neuron in Global._objects['neurons']:
# skip non-bold models
if not isinstance(neuron, BoldModel):
continue
# the model is not used
if not neuron._model_instantiated:
continue
# Name
if neuron.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(neuron._rk_neurons_type)
else:
neuron_name = neuron.name
# Generate the code for the equations
variables, _, _ = LatexParser._process_neuron_equations(neuron)
eqs = ""
for var in variables:
eqs += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': var['latex']}
variables_eqs = """
%(eqs)s
\\\\ \\hline
""" % {'eqs': eqs}
# Build the dictionary
desc = {
'name': neuron_name,
'description': neuron.short_description,
'header': header if first_define else "",
'variables': variables_eqs,
'equation_type': "Equations"
}
# Generate the code depending on the neuron position
measurements += measurements_template % desc
# first_define disable to prevent multiple table header
first_define = False
# no models were processed
if len(measurements) == 0:
measurements = """
\\noindent
\\begin{tabularx}{\\linewidth}{|l|X|}\\hline
\\hdr{2}{H}{Measurement}\\\\ \\hline
\\textbf{Type} & \\textbf{Description} \\\\ \\hline
---
\\\\ \\hline
\\end{tabularx}
"""
return measurements
def _generate_neuron_models(net_id):
txt = """
# Neuron models
"""
for idx, neuron in enumerate(Global._objects['neurons']):
# Name
if neuron.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(neuron._rk_neurons_type)
else:
neuron_name = neuron.name
# Description
description = neuron.short_description
if description == None:
description = "Spiking neuron." if neuron.type == 'spike' else 'Rate-coded neuron'
# Parameters
parameters = extract_parameters(neuron.parameters, neuron.extra_values)
parameters_list = [
["$" + LatexParser._latexify_name(param['name'], []) + "$", param['init'],
_adapt_locality_neuron(param['locality']), param['ctype']]
for param in parameters]
parameters_headers = ["Name", "Default value", "Locality", "Type"]
parameters_table = _make_table(parameters_headers, parameters_list)
if len(parameters) == 0:
parameters_table = "$$\\varnothing$$"
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = _process_variables(variables, neuron=True)
# Spiking neurons
if neuron.type == 'spike':
reset_txt = "* Emit a spike a time $t$.\n"
for r in spike_reset:
reset_txt += "* $" + r + "$\n"
eqs += """
**Spike emission:**
if $%(condition)s$ :
%(reset)s
""" % {'condition': spike_condition, 'reset': reset_txt}
# Possible function
if not neuron.functions == None:
eqs += """
**Functions**
%(functions)s
""" % {'functions': LatexParser._process_functions(neuron.functions, begin="$$", end="$$\n\n")}
# Finalize the template
txt += neuron_tpl % { 'name': neuron_name,
'description': description,
'parameters': parameters_table,
'eqs': eqs}
return txt