calix.spiking.correlation

Provides classes for calibration of the correlation characteristics, i.e. time constants and amplitudes.

Classes

AmplitudeCalib(target, …)	Calibrate the correlation amplitude at delay zero.
CorrelationBranches(value)	Select branches of correlation: causal, acausal or both.
CorrelationCalibOptions(branches, …)	Further options for correlation calibration.
CorrelationCalibResult(target, options, …)	Result of a synapse correlation sensor calibration.
CorrelationCalibTarget(amplitude, time_constant)	Target parameters for correlation calibration.
Enum(value)	Generic enumeration.
TimeConstantCalib(target, *, branches, …)	Calibrate the correlation time constant.
auto()	Instances are replaced with an appropriate value in Enum class suites.

Functions

calix.spiking.correlation.calibrate(connection: pyhxcomm_vx.ConnectionHandle, *, target: Optional[calix.spiking.correlation.CorrelationCalibTarget] = None, options: Optional[calix.spiking.correlation.CorrelationCalibOptions] = None) → calix.spiking.correlation.CorrelationCalibResult

Calibrate all synapses’ correlation parameters.

The CapMem bias currents are set such that the target amplitude and time constant are reached in the median of the measured synapses.

If desired, the individual synapses’ calibration bits are then used to shrink the deviations between synapses. This takes some two hours of runtime, and can be enabled via the calibrate_synapses option.

Note that a strong asymmetry between causal and acausal traces is present on many synapses. This cannot be countered by calibration, as calibration parameters apply to both branches equally. In case your experiment uses only one branch, you can set the branches option accordingly, so that only one is considered during calibration.

Requirements: * CADCs are enabled and calibrated.

Parameters

• connection – Connection to the chip to calibrate.

• target – Calibration targets, given as an instance of CorrelationCalibTargets.

• options – Further options for calibration, given as an instance of CorrelationCalibOptions.

Returns

Correlation calib result.

calix.spiking.correlation.calibrate_synapses(connection: pyhxcomm_vx.ConnectionHandle, calib_result: calix.spiking.correlation.CorrelationCalibResult) → None

Set the individual synapses’ calibration bits.

The bits are selected such that the deviation from the targets is smallest. To calculate the deviation, the relative difference in amplitude and time constant is weighted according to the time_constant_priority option and then added squared.

This function will be called automatically as part of the calibrate function, if individual synapses are to be calibrated.

Requirements: * CADCs are enabled and calibrated. * Correlation sensors are enabled and configured with CapMem

biases suitable for the given targets. This can be achieved using the calibrate function.

Parameters

• connection – Connection to the chip to calibrate.

• calib_result – Correlation calib result, with CapMem biases already calibrated.

calix.spiking.correlation.dataclass(cls=None, /, *, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)

Returns the same class as was passed in, with dunder methods added based on the fields defined in the class.

Examines PEP 526 __annotations__ to determine fields.

If init is true, an __init__() method is added to the class. If repr is true, a __repr__() method is added. If order is true, rich comparison dunder methods are added. If unsafe_hash is true, a __hash__() method function is added. If frozen is true, fields may not be assigned to after instance creation.

calix.spiking.correlation.field(*, default=<dataclasses._MISSING_TYPE object>, default_factory=<dataclasses._MISSING_TYPE object>, init=True, repr=True, hash=None, compare=True, metadata=None)

Return an object to identify dataclass fields.

default is the default value of the field. default_factory is a 0-argument function called to initialize a field’s value. If init is True, the field will be a parameter to the class’s __init__() function. If repr is True, the field will be included in the object’s repr(). If hash is True, the field will be included in the object’s hash(). If compare is True, the field will be used in comparison functions. metadata, if specified, must be a mapping which is stored but not otherwise examined by dataclass.

It is an error to specify both default and default_factory.