api.CRI_network

api.CRI_network(self, axons, connections, config, outputs, target=None, simDump=False, coreID=0, perturbMag=None, leak=0)

This class represents a CRI network which initializes the network, checks hardware, generates connectome, formats input, reads and writes synapse, and runs simulation steps.

Attributes

Name	Type	Description
userAxons	dict	A copy of the axons dictionary provided by the user.
userConnections	dict	A copy of the connections dictionary provided by the user.
config	dict	The configuration parameters for the network.
perturb	bool	A boolean value indicating whether to perturb the network.
perturbMag	int	The magnitude of perturbation.
simpleSim	str	A string representing the simple simulation.
key2index	dict	A dictionary mapping keys to indices.
simDump	bool	A boolean value indicating whether to dump simulation results.
connectome	str	A string representing the connectome of the network.
axons	dict	The formatted axons dictionary.
connections	dict	The formatted connections dictionary.

Examples

>>> axons = {'axon1': [('neuron1', 1), ('neuron2', 2)]}
>>> connections = {'neuron1': [('axon1', 1)], 'neuron2': [('axon1', 2)]}
>>> config = {'neuron_type': 'type1', 'global_neuron_params': {'v_thr': 1.0}}
>>> outputs = ['output1', 'output2']
>>> network = CRI_network(axons, connections, config, outputs)

Methods

Name	Description
checkHw	Checks if the magic file exists to demark that we’re running on a system with CRI hardware accessible.
gen_connectome	Generates a connectome for the CRI network.
read_synapse	Reads a synapse from the connectome.
run_cont	Runs a continuous simulation.
sim_flush	Flushes the simulation results to a file.
step	Runs a step of the simulation.
write_listofSynapses	Writes a list of synapses to the connectome.
write_synapse	Writes a synapse to the connectome.

checkHw

api.CRI_network.checkHw(self)

Checks if the magic file exists to demark that we’re running on a system with CRI hardware accessible.

Returns

Type	Description
bool	True if the magic file exists, False otherwise.

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> network.checkHw()
True

gen_connectome

api.CRI_network.gen_connectome(self)

Notes

The function resets the count of neurons and creates a new connectome. It then adds neurons/axons and assigns synapses to them.

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> network.gen_connectome()

read_synapse

api.CRI_network.read_synapse(self, preKey, postKey)

Reads a synapse from the connectome.

Parameters

Name	Type	Description	Default
preKey	str	A string representing the key of the presynaptic neuron.	required
postKey	str	A string representing the key of the postsynaptic neuron.	required

Returns

Type	Description
int	The weight of the synapse.

Raises

Type	Description
Exception	If the target is not valid (“simpleSim” or “CRI”).

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> network.read_synapse('axon1', 'neuron1')
1

run_cont

api.CRI_network.run_cont(self, inputs)

Runs a continuous simulation.

Parameters

Name	Type	Description	Default
inputs	list of list	A list of lists of inputs for the simulation.	required

Returns

Type	Description
tuple	A tuple containing the spike list, a boolean indicating whether a break occurred, and the execution counter.

Raises

Type	Description
Exception	If the simulation dump flag is False and an error occurs during the conversion of neuron indices to user keys.

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> network.run_cont([['input1', 'input2'], ['input3', 'input4']])

sim_flush

api.CRI_network.sim_flush(self, file)

Flushes the simulation results to a file.

Parameters

Name	Type	Description	Default
file	str	A string representing the file to which to flush the simulation results.	required

Returns

Type	Description
None

Raises

Type	Description
Exception	If the target is not “CRI” or if the target is invalid.

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> network.sim_flush('results.txt')

step

api.CRI_network.step(self, inputs, target='simpleSim', membranePotential=False)

Runs a step of the simulation.

Parameters

Name	Type	Description	Default
inputs	list	A list of inputs for the simulation.	required
target	str	A string representing the target for the simulation. Default is “simpleSim”.	'simpleSim'
membranePotential	bool	A boolean value indicating whether to return the membrane potential. Default is False.	False

Returns

Type	Description
list or tuple	The simulation outputs or a tuple containing the simulation outputs and spike outputs.

Raises

Type	Description
Exception	If the target is invalid.

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> network.step(['input1', 'input2'])

write_listofSynapses

api.CRI_network.write_listofSynapses(self, preKeys, postKeys, weights)

Writes a list of synapses to the connectome.

Parameters

Name	Type	Description	Default
preKeys	list of str	A list of strings representing the keys of the presynaptic neurons.	required
postKeys	list of str	A list of strings representing the keys of the postsynaptic neurons.	required
weights	list of int	A list of integers representing the weights of the synapses.	required

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> preKeys = ['axon1', 'axon2']
>>> postKeys = ['neuron1', 'neuron2']
>>> weights = [1, 2]
>>> network.write_listofSynapses(preKeys, postKeys, weights)

write_synapse

api.CRI_network.write_synapse(self, preKey, postKey, weight)

Writes a synapse to the connectome.

Parameters

Name	Type	Description	Default
preKey	str	A string representing the key of the presynaptic neuron.	required
postKey	str	A string representing the key of the postsynaptic neuron.	required
weight	int	An integer representing the weight of the synapse.	required

Raises

Type	Description
Exception	If the target is not valid (“simpleSim” or “CRI”).

Examples

>>> network = CRI_network(axons, connections, config, outputs)
>>> network.write_synapse('axon1', 'neuron1', 1)