sns_toolbox.backends

Simulation backends for synthetic nervous system networks. Each of these are python-based, and are constructed using a Network. They can then be run for a step, with the inputs being a vector of neural states and applied currents and the output being the next step of neural states.

Classes

`Backend`
`SNS_Numpy`
`SNS_Torch`
`SNS_Sparse`
`SNS_Iterative`

Module Contents

class sns_toolbox.backends.Backend(params: Dict)

abstract forward(x=None)

set_params(params: Dict) → None

__call__(x=None)

abstract reset()

class sns_toolbox.backends.SNS_Numpy(params: Dict)

Bases: Backend

forward(x=None)

reset()

class sns_toolbox.backends.SNS_Torch(params: Dict)

Bases: Backend

forward(x=None)

reset()

class sns_toolbox.backends.SNS_Sparse(params: Dict)

Bases: Backend

forward(x=None)

reset()

class sns_toolbox.backends.SNS_Iterative(params: Dict)

Bases: Backend

set_params(params: Dict) → None

forward(x=None)

reset()