def create_leaky_integrate_and_fire(): dyn = al.Dynamics( name='LeakyIntegrateAndFire', regimes=[ al.Regime('dv/dt = (i_synaptic*R - v)/tau', transitions=[al.On('v > v_threshold', do=[al.OutputEvent('spike_output'), al.StateAssignment( 'refractory_end', 't + refractory_period'), al.StateAssignment('v', 'v_reset')], to='refractory')], name='subthreshold'), al.Regime(transitions=[al.On('t > refractory_end', to='subthreshold')], name='refractory')], state_variables=[al.StateVariable('v', dimension=un.voltage), al.StateVariable('refractory_end', dimension=un.time)], parameters=[al.Parameter('R', un.resistance), al.Parameter('refractory_period', un.time), al.Parameter('v_reset', un.voltage), al.Parameter('v_threshold', un.voltage), al.Parameter('tau', un.time)], analog_ports=[al.AnalogReducePort('i_synaptic', un.current, operator='+'), al.AnalogSendPort('refractory_end', un.time), al.AnalogSendPort('v', un.voltage)]) return dyn
def create_alpha(): dyn = al.Dynamics( name="Alpha", aliases=["Isyn := A"], regimes=[ al.Regime( name="default", time_derivatives=[ "dA/dt = (B - A)/tau", # TGC 4/15 changed from "B - A/tau_syn" as dimensions didn't add up @IgnorePep8 "dB/dt = -B/tau" ], transitions=al.On('spike', do=["B = B + q"])) ], state_variables=[ al.StateVariable('A', dimension=un.current), al.StateVariable('B', dimension=un.current), ], analog_ports=[ al.AnalogSendPort("Isyn", dimension=un.current), al.AnalogSendPort("A", dimension=un.current), al.AnalogSendPort("B", dimension=un.current), al.AnalogReceivePort("q", dimension=un.current) ], parameters=[al.Parameter('tau', dimension=un.time)]) return dyn
def create_static(): dyn = al.Dynamics( name="Static", aliases=["fixed_weight := weight"], regimes=[al.Regime(name="default")], analog_ports=[al.AnalogSendPort("fixed_weight", dimension=un.current)], parameters=[al.Parameter('weight', dimension=un.current)]) return dyn
def get_Izh_FS_component(): """ Load Fast spiking Izhikevich XML definition from file and parse into Abstraction Layer of Python API. """ izhi_fs = al.Dynamics( name='IzhikevichFS', parameters=[ al.Parameter('a', un.per_time), al.Parameter('b', un.conductance / (un.voltage**2)), al.Parameter('c', un.voltage), al.Parameter('k', un.conductance / un.voltage), al.Parameter('Vr', un.voltage), al.Parameter('Vt', un.voltage), al.Parameter('Vb', un.voltage), al.Parameter('Vpeak', un.voltage), al.Parameter('Cm', un.capacitance) ], analog_ports=[ al.AnalogReducePort('iSyn', un.current, operator="+"), al.AnalogReducePort('iExt', un.current, operator="+"), al.AnalogSendPort('U', un.current), al.AnalogSendPort('V', un.voltage) ], event_ports=[al.EventSendPort("spikeOutput")], state_variables=[ al.StateVariable('V', un.voltage), al.StateVariable('U', un.current) ], regimes=[ al.Regime('dU/dt = a * (b * pow(V - Vb, 3) - U)', 'dV/dt = V_deriv', transitions=[ al.On('V > Vpeak', do=['V = c', al.OutputEvent('spikeOutput')], to='subthreshold') ], name="subthreshold"), al.Regime('dU/dt = - U * a', 'dV/dt = V_deriv', transitions=[al.On('V > Vb', to="subthreshold")], name="subVb") ], aliases=[ "V_deriv := (k * (V - Vr) * (V - Vt) - U + iExt + iSyn) / Cm" ]) # @IgnorePep8 return izhi_fs
def create_stdp_guetig(): dyn = al.Dynamics( name="StdpGuetig", parameters=[ al.Parameter(name='tauLTP', dimension=un.time), al.Parameter(name='aLTD', dimension=un.dimensionless), al.Parameter(name='wmax', dimension=un.dimensionless), al.Parameter(name='muLTP', dimension=un.dimensionless), al.Parameter(name='tauLTD', dimension=un.time), al.Parameter(name='aLTP', dimension=un.dimensionless) ], analog_ports=[ al.AnalogReceivePort(dimension=un.dimensionless, name="w"), al.AnalogSendPort(dimension=un.dimensionless, name="wsyn") ], event_ports=[al.EventReceivePort(name="incoming_spike")], state_variables=[ al.StateVariable(name='tlast_post', dimension=un.time), al.StateVariable(name='tlast_pre', dimension=un.time), al.StateVariable(name='deltaw', dimension=un.dimensionless), al.StateVariable(name='interval', dimension=un.time), al.StateVariable(name='M', dimension=un.dimensionless), al.StateVariable(name='P', dimension=un.dimensionless), al.StateVariable(name='wsyn', dimension=un.dimensionless) ], regimes=[ al.Regime( name="sole", al.On('incoming_spike', target_regime="sole", do=[ al.StateAssignment( 'tlast_post', '((w >= 0) ? ( tlast_post ) : ( t ))'), al.StateAssignment( 'tlast_pre', '((w >= 0) ? ( t ) : ( tlast_pre ))'), al.StateAssignment( 'deltaw', '((w >= 0) ? ' '( 0.0 ) : ' '( P*pow(wmax - wsyn, muLTP) * ' 'exp(-interval/tauLTP) + deltaw ))'), al.StateAssignment( 'interval', '((w >= 0) ? ( -t + tlast_post ) : ' '( t - tlast_pre ))'), al.StateAssignment( 'M', '((w >= 0) ? ( M ) : ' '( M*exp((-t + tlast_post)/tauLTD) - aLTD ))'), al.StateAssignment( 'P', '((w >= 0) ? ' '( P*exp((-t + tlast_pre)/tauLTP) + aLTP ) : ' '( P ))'), al.StateAssignment( 'wsyn', '((w >= 0) ? ( deltaw + wsyn ) : ' '( wsyn ))') ])) ]) return dyn
def create_izhikevich(): subthreshold_regime = al.Regime( name="subthreshold_regime", time_derivatives=[ "dV/dt = alpha*V*V + beta*V + zeta - U + Isyn / C_m", "dU/dt = a*(b*V - U)", ], transitions=[ al.On("V > theta", do=["V = c", "U = U+ d", al.OutputEvent('spike')], to='subthreshold_regime') ]) ports = [ al.AnalogSendPort("V", un.voltage), al.AnalogReducePort("Isyn", un.current, operator="+") ] parameters = [ al.Parameter('theta', un.voltage), al.Parameter('a', un.per_time), al.Parameter('b', un.per_time), al.Parameter('c', un.voltage), al.Parameter('d', old_div(un.voltage, un.time)), al.Parameter('C_m', un.capacitance), al.Parameter('alpha', old_div(un.dimensionless, (un.voltage * un.time))), al.Parameter('beta', un.per_time), al.Parameter('zeta', old_div(un.voltage, un.time)) ] state_variables = [ al.StateVariable('V', un.voltage), al.StateVariable('U', old_div(un.voltage, un.time)) ] c1 = al.Dynamics(name="Izhikevich", parameters=parameters, state_variables=state_variables, regimes=[subthreshold_regime], analog_ports=ports) return c1
def create_hodgkin_huxley(): """A Hodgkin-Huxley single neuron model. Written by Andrew Davison. See http://phobos.incf.ki.se/src_rst/ examples/examples_al_python.html#example-hh """ aliases = [ "q10 := 3.0**((celsius - qfactor)/tendegrees)", # temperature correction factor @IgnorePep8 "m_alpha := m_alpha_A*(V-m_alpha_V0)/(exp(-(V-m_alpha_V0)/m_alpha_K) - 1.0)", # @IgnorePep8 "m_beta := m_beta_A*exp(-(V-m_beta_V0)/m_beta_K)", "mtau := 1.0/(q10*(m_alpha + m_beta))", "minf := m_alpha/(m_alpha + m_beta)", "h_alpha := h_alpha_A*exp(-(V-h_alpha_V0)/h_alpha_K)", "h_beta := h_beta_A/(exp(-(V-h_beta_V0)/h_beta_K) + 1.0)", "htau := 1.0/(q10*(h_alpha + h_beta))", "hinf := h_alpha/(h_alpha + h_beta)", "n_alpha := n_alpha_A*(V-n_alpha_V0)/(exp(-(V-n_alpha_V0)/n_alpha_K) - 1.0)", # @IgnorePep8 "n_beta := n_beta_A*exp(-(V-n_beta_V0)/n_beta_K)", "ntau := 1.0/(q10*(n_alpha + n_beta))", "ninf := n_alpha/(n_alpha + n_beta)", "gna := gnabar*m*m*m*h", "gk := gkbar*n*n*n*n", "ina := gna*(ena - V)", "ik := gk*(ek - V)", "il := gl*(el - V )" ] hh_regime = al.Regime("dn/dt = (ninf-n)/ntau", "dm/dt = (minf-m)/mtau", "dh/dt = (hinf-h)/htau", "dV/dt = (ina + ik + il + isyn)/C", transitions=al.On("V > v_threshold", do=al.SpikeOutputEvent())) state_variables = [ al.StateVariable('V', un.voltage), al.StateVariable('m', un.dimensionless), al.StateVariable('n', un.dimensionless), al.StateVariable('h', un.dimensionless) ] # the rest are not "parameters" but aliases, assigned vars, state vars, # indep vars, analog_analog_ports, etc. parameters = [ al.Parameter('el', un.voltage), al.Parameter('C', un.capacitance), al.Parameter('ek', un.voltage), al.Parameter('ena', un.voltage), al.Parameter('gkbar', un.conductance), al.Parameter('gnabar', un.conductance), al.Parameter('v_threshold', un.voltage), al.Parameter('gl', un.conductance), al.Parameter('celsius', un.temperature), al.Parameter('qfactor', un.temperature), al.Parameter('tendegrees', un.temperature), al.Parameter('m_alpha_A', old_div(un.dimensionless, (un.time * un.voltage))), al.Parameter('m_alpha_V0', un.voltage), al.Parameter('m_alpha_K', un.voltage), al.Parameter('m_beta_A', old_div(un.dimensionless, un.time)), al.Parameter('m_beta_V0', un.voltage), al.Parameter('m_beta_K', un.voltage), al.Parameter('h_alpha_A', old_div(un.dimensionless, un.time)), al.Parameter('h_alpha_V0', un.voltage), al.Parameter('h_alpha_K', un.voltage), al.Parameter('h_beta_A', old_div(un.dimensionless, un.time)), al.Parameter('h_beta_V0', un.voltage), al.Parameter('h_beta_K', un.voltage), al.Parameter('n_alpha_A', old_div(un.dimensionless, (un.time * un.voltage))), al.Parameter('n_alpha_V0', un.voltage), al.Parameter('n_alpha_K', un.voltage), al.Parameter('n_beta_A', old_div(un.dimensionless, un.time)), al.Parameter('n_beta_V0', un.voltage), al.Parameter('n_beta_K', un.voltage) ] analog_ports = [ al.AnalogSendPort("V", un.voltage), al.AnalogReducePort("isyn", un.current, operator="+") ] dyn = al.Dynamics("HodgkinHuxley", parameters=parameters, state_variables=state_variables, regimes=(hh_regime, ), aliases=aliases, analog_ports=analog_ports) return dyn
def get_HH_component(): """A Hodgkin-Huxley single neuron model. Written by Andrew Davison. See http://phobos.incf.ki.se/src_rst/ examples/examples_al_python.html#example-hh """ aliases = [ "q10 := 3.0**((celsius - qfactor)/tendegrees)", # temperature correction factor @IgnorePep8 "alpha_m := alpha_m_A*(V-alpha_m_V0)/(exp(-(V-alpha_m_V0)/alpha_m_K) - 1.0)", # @IgnorePep8 "beta_m := beta_m_A*exp(-(V-beta_m_V0)/beta_m_K)", "mtau := 1.0/(q10*(alpha_m + beta_m))", "minf := alpha_m/(alpha_m + beta_m)", "alpha_h := alpha_h_A*exp(-(V-alpha_h_V0)/alpha_h_K)", "beta_h := beta_h_A/(exp(-(V-beta_h_V0)/beta_h_K) + 1.0)", "htau := 1.0/(q10*(alpha_h + beta_h))", "hinf := alpha_h/(alpha_h + beta_h)", "alpha_n := alpha_n_A*(V-alpha_n_V0)/(exp(-(V-alpha_n_V0)/alpha_n_K) - 1.0)", # @IgnorePep8 "beta_n := beta_n_A*exp(-(V-beta_n_V0)/beta_n_K)", "ntau := 1.0/(q10*(alpha_n + beta_n))", "ninf := alpha_n/(alpha_n + beta_n)", "gna := gnabar*m*m*m*h", "gk := gkbar*n*n*n*n", "ina := gna*(ena - V)", "ik := gk*(ek - V)", "il := gl*(el - V )" ] hh_regime = al.Regime("dn/dt = (ninf-n)/ntau", "dm/dt = (minf-m)/mtau", "dh/dt = (hinf-h)/htau", "dV/dt = (ina + ik + il + Isyn)/C", transitions=al.On("V > theta", do=al.SpikeOutputEvent())) state_variables = [ al.StateVariable('V', un.voltage), al.StateVariable('m', un.dimensionless), al.StateVariable('n', un.dimensionless), al.StateVariable('h', un.dimensionless) ] # the rest are not "parameters" but aliases, assigned vars, state vars, # indep vars, analog_analog_ports, etc. parameters = [ al.Parameter('el', un.voltage), al.Parameter('C', un.capacitance), al.Parameter('ek', un.voltage), al.Parameter('ena', un.voltage), al.Parameter('gkbar', un.conductance), al.Parameter('gnabar', un.conductance), al.Parameter('theta', un.voltage), al.Parameter('gl', un.conductance), al.Parameter('celsius', un.temperature), al.Parameter('qfactor', un.temperature), al.Parameter('tendegrees', un.temperature), al.Parameter('alpha_m_A', un.dimensionless / (un.time * un.voltage)), al.Parameter('alpha_m_V0', un.voltage), al.Parameter('alpha_m_K', un.voltage), al.Parameter('beta_m_A', un.dimensionless / un.time), al.Parameter('beta_m_V0', un.voltage), al.Parameter('beta_m_K', un.voltage), al.Parameter('alpha_h_A', un.dimensionless / un.time), al.Parameter('alpha_h_V0', un.voltage), al.Parameter('alpha_h_K', un.voltage), al.Parameter('beta_h_A', un.dimensionless / un.time), al.Parameter('beta_h_V0', un.voltage), al.Parameter('beta_h_K', un.voltage), al.Parameter('alpha_n_A', un.dimensionless / (un.time * un.voltage)), al.Parameter('alpha_n_V0', un.voltage), al.Parameter('alpha_n_K', un.voltage), al.Parameter('beta_n_A', un.dimensionless / un.time), al.Parameter('beta_n_V0', un.voltage), al.Parameter('beta_n_K', un.voltage) ] analog_ports = [ al.AnalogSendPort("V", un.voltage), al.AnalogReducePort("Isyn", un.current, operator="+") ] c1 = al.Dynamics("HodgkinHuxley", parameters=parameters, state_variables=state_variables, regimes=(hh_regime, ), aliases=aliases, analog_ports=analog_ports) return c1
def get_aeIF_component(): """ Adaptive exponential integrate-and-fire neuron as described in A. Destexhe, J COmput Neurosci 27: 493--506 (2009) Author B. Kriener (Jan 2011) ## neuron model: aeIF ## variables: ## V: membrane potential ## w: adaptation variable ## parameters: ## C_m # specific membrane capacitance [muF/cm**2] ## g_L # leak conductance [mS/cm**2] ## E_L # resting potential [mV] ## Delta # steepness of exponential approach to threshold [mV] ## V_T # spike threshold [mV] ## S # membrane area [mum**2] ## trefractory # refractory time [ms] ## tspike # spike time [ms] ## tau_w # adaptation time constant ## a, b # adaptation parameters [muS, nA] """ aeIF = al.Dynamics( name="aeIF", parameters=[ al.Parameter('C_m', un.capacitance), al.Parameter('g_L', un.conductance), al.Parameter('E_L', un.voltage), al.Parameter('Delta', un.voltage), al.Parameter('V_T', un.voltage), al.Parameter('S'), al.Parameter('trefractory', un.time), al.Parameter('tspike', un.time), al.Parameter('tau_w', un.time), al.Parameter('a', un.dimensionless / un.voltage), al.Parameter('b') ], state_variables=[ al.StateVariable('V', un.voltage), al.StateVariable('w') ], regimes=[ al.Regime( name="subthresholdregime", time_derivatives=[ "dV/dt = -g_L*(V-E_L)/C_m + Isyn/C_m + g_L*Delta*exp((V-V_T)/Delta-w/S)/C_m", # @IgnorePep8 "dw/dt = (a*(V-E_L)-w)/tau_w", ], transitions=al.On( "V > V_T", do=["V = E_L", "w = w + b", al.OutputEvent('spikeoutput')], to="refractoryregime")), al.Regime(name="refractoryregime", transitions=al.On("t>=tspike+trefractory", to="subthresholdregime")) ], analog_ports=[al.AnalogReducePort("Isyn", un.current, operator="+")]) return aeIF
), al.Regime( name="refractory", transitions=[al.On("t > refractory_end", to="subthreshold")], ) ], state_variables=[ al.StateVariable('v', dimension=voltage), al.StateVariable('refractory_end', dimension=time) ], analog_ports=[ al.AnalogSendPort("v", dimension=voltage), al.AnalogSendPort("refractory_end", dimension=time), al.AnalogReducePort("i_synaptic", operator="+", dimension=current) ], event_ports=[ al.EventSendPort('spike_output'), ], parameters=[ al.Parameter('tau', time), al.Parameter('v_threshold', voltage), al.Parameter('refractory_period', time), al.Parameter('v_reset', voltage), al.Parameter('R', resistance) ]) if __name__ == "__main__": import nineml filename = __file__[0].upper() + __file__[1:].replace(".py", ".xml") nineml.write(model, filename)
""" """ import nineml.abstraction as al from nineml.units import time model = al.Dynamics( name="Tonic", regimes=[ al.Regime(name="default", transitions=al.On("t > t_next", do=[ "t_next = t + interval", al.OutputEvent('spikeOutput') ])) ], event_ports=[al.EventSendPort('spikeOutput')], state_variables=[al.StateVariable('t_next', dimension=time)], parameters=[al.Parameter('interval', dimension=time)], ) if __name__ == "__main__": import nineml filename = __file__[0].upper() + __file__[1:].replace(".py", ".xml") nineml.write(model, filename)
""" import nineml.abstraction as al from nineml.units import per_time, time, ms, per_ms model = al.Dynamics( name="Poisson", regimes=[ al.Regime( name="default", transitions=al. On("t > t_next", do=[ "t_next = t + one_ms * random.exponential(thousand_per_millisecond / rate)", al.OutputEvent('spikeOutput') ])) ], event_ports=[al.EventSendPort('spikeOutput')], state_variables=[al.StateVariable('t_next', dimension=time)], parameters=[al.Parameter('rate', dimension=per_time)], constants=[ al.Constant('thousand_per_millisecond', value=1000.0, units=per_ms), al.Constant('one_ms', value=1.0, units=ms) ]) if __name__ == "__main__": import nineml filename = __file__[0].upper() + __file__[1:].replace(".py", ".xml") nineml.write(model, filename)
model = al.Dynamics( name="Alpha", aliases=["i_synaptic := a"], regimes=[ al.Regime( name="sole", time_derivatives=[ "da/dt = (b - a)/tau", "db/dt = -b/tau"], transitions=al.On('spike', do=["b = b + weight"]), ) ], state_variables=[ al.StateVariable('a', dimension=current), al.StateVariable('b', dimension=current), ], analog_ports=[al.AnalogSendPort("i_synaptic", dimension=current), al.AnalogSendPort("a", dimension=current), al.AnalogSendPort("b", dimension=current), al.AnalogReceivePort("weight", dimension=current)], parameters=[al.Parameter('tau', dimension=time)] ) if __name__ == "__main__": import nineml filename = __file__[0].upper() + __file__[1:].replace(".py", ".xml") nineml.write(model, filename)