genn_eventprop

Implementation of eventprop learning in GeNN

The repository contains research code that was used to make a first implementation of the Eventprop learning rule (Wunderlich and Pehle 2021) and test it against increasingly difficult benchmarks. The code underlies the publication: Nowotny, T., Turner, J.P. and Knight, J.C., 2022. Loss shaping enhances exact gradient learning with eventprop in spiking neural networks. arXiv preprint arXiv:2212.01232.

The code is generally organised so that the GeNN model definitions are colected in models.py and for the benchmarks there are individual python files simulator_XXX.py as follows:

• simulator_yinyang.py: Train to classify the Yinyang benchmark dataset (Kriener et al. 2022)
• simulator_MNIST.py: Train to classify the MNIST dataset (Lecun et al. 1998)
• simulator_SHD.py: Train to classify the Spikeing Heidelberg Digits (SHD) (Cramer et al. 2020) or Spiking Google Speech Commands (SSC), a spiking version of Google Speech Commands (Warden 2018) datasets. Which to train is selected with parameter p["DATASET"]
• simulator_DVS_gesture.py: experimental, train to classify the DVS gesture dataset (Amir et al. 2017)
• simulator_SMNIST.py: experimental, train to classify the sequential MNIST dataset (Quoc et al. 2015), (Costa et al. 2017)

The latter two simulators are in an early experimental stage and did not contribute to the publication.

Please note that this is research code and comes with no warranty of fitness for any purpose. Also, the lessons learned with this code are incorporated into the ml-genn that is better suitable for third party use.

Details

Each of the simulator_XXX.py files contains the definition of a simulator class that can then be used to train, test or run cross-validation on the benchmark datasets in question. An example for a simple script to do so is train_SHD.py.

The behaviour of the simulator class is fine-tuned by a dictionary p of parameters that is passed to the methods. It is defined in the simulator_XXX.py files with standard values and can then be adjusted to the intended values, e.g.

from simulator_SHD import *
p["TRAIL_MS"] = 100
mn = SHD_model(p)
mn.train(p)

All potential parameters used in these dictionaries are detailed in the tables below.

Yinyang parameters

General parameters

Name	Description	Default
OUT_DIR	Directory where to write results	"." (current dir)
DT_MS	Time step od the simulations in millisceonds	0.1
BUILD	Whether to (re)build the GeNN model, can be set to False if there are repeated runs of the same model	True
TIMING	Whether to record timing information during a run	True

Experiment parameters

Name	Description	Default
TRAIN_DATA_SEED	Seed for the random number generator used for generating training data	123
TEST_DATA_SEED	Seed for the random number generator used for generating testing data	456
TRIAL_MS	Duration of a trial in milliseconds	30.0
N_MAX_SPIKE	Size of the bufferfor saved spikes in number of spikes; note the buffer contains saved spikes across two trials	400
N_BATCH	Size of mini-batches run in parallel	32
N_TRAIN	Number of training examples	1000 mini-batches
N_EPOCHS	Number of epochs to train	10
N_TEST	Number of test examples	25 mini-batches

Network structure

Name	Description	Default
NUM_HIDDEN	Number of hidden neurons	200

Model parameters

Name	Description	Default
TAU_SYN	Synaptic timescale in millisecons	5.0
TAU_MEM	Membrane times constant in milliseconds	20.0
V_THRESH	Spiking threshold	1.0
V_RESET	Reset potential	0.0
TAU_0	Parameter of the first_spike loss function	0.5
TAU_1	Parameter of the first_spike loss function	6.4
ALPHA	Parameter of the first_spike loss function	3e-3
INPUT_HIDDEN_MEAN	Mean synaptic weight from input to hidden neurons	1.5
INPUT_HIDDEN_STD	Standard deviation of synaptic weights from input to hidden neurons	0.78
HIDDEN_OUTPUT_MEAN	Mean synaptic weight from hidden rto output neurons	0.93
HIDDEN_OUTPUT_STD	Standard deviation of synaptic weights from hidden to output neurons	0.1

Learning parameters

Name	Description	Default
ETA	Learning rate	5e-3
ADAM_BETA1	Adam optimiser parameter	0.9
ADAM_BETA2	Adam optimiser parameter	0.999
ADAM_EPS	Adam optimiser parameter	1e-8
ETA_DECAY	Multiplier for learnign rate decay, applied every epoch	0.95

Recording controls

Name	Description	Default
SPK_REC_STEPS	Size of the GeNN spike recording buffer in timesteps	TRIAL_MS/DT_MS
REC_SPIKES	List of neuron populations to record spikes from, possible entries "input", "hidden", "output"	[]
REC_NEURONS	List of pairs (neuron population, variable name) to record from, possible entries for population are "input", "hidden", "output"	[]
REC_SYNAPSES	List of pairs (synapse population, synapse variable name) to record from, possible entries for synapse population are "in_to_hid", "hid_to_out"	[]
WRITE_TO_DISK	Whether to write outputs to disk or just return them from the run function	True
TRAINING_PLOT	Whether to display plots during training	False
TRAINING_PLOT_INTERVAL	How often to display a training plot in number of epochs	10
FANCY_PLOTS	Whether to display additional plots	False
LOAD_LAST	Whether to load a checkpoint from a previous run	False
W_REPORT_INTERVAL	How often to save weight matrices; interval in number of trials	100
W_EPOCH_INTERVAL	How often to save weight matrices; interval in terms of epochs	10

MNIST parameters

Many parameters are the same across the different benchmarks but we list them againg for ease of reference.

General parameters

Name	Description	Default
NAME	A unique name for an experiment; results will be appended if a result file with this name already exists	"test"
DEBUG_HIDDEN_N	Whether to collect and return information about the activity levels of hidden neurons	False
OUT_DIR	Directory where to write results	"." (current dir)
DT_MS	Time step od the simulations in millisceonds	1.0
BUILD	Whether to (re)build the GeNN model, can be set to False if there are repeated runs of the same model	True
TIMING	Whether to record timing information during a run	True

Experiment parameters

Name	Description	Default
TRAIN_DATA_SEED	Seed for the random number generator used for generating training data	123
TEST_DATA_SEED	Seed for the random number generator used for generating testing data	456
MODEL_SEED	A separate random number seed for the random number generator that is used during model generation, e.g. for random initial values of synapse weights	None
TRIAL_MS	Duration of trials in milliseconds	20.0
N_MAX_SPIKE	Size of the bufferfor saved spikes in number of spikes; note the buffer contains saved spikes across two trials	50
N_TRAIN	Number of training examples	55000
N_VALIDATE	Number of examples in the validation set	5000
SHUFFLE	Whether to shuffle the inputs in the training set	True
N_TEST	Number of examples in the test set	10000

Network structure

Name	Description	Default
NUM_HIDDEN	Number of neurons in the hidden layer	128
RECURRENT	Whether to include recurrent connections	False

Model parameters

Name	Description	Default
TAU_SYN	Synaptic timescale in millisecons	5.0
TAU_MEM	Membrane times constant in milliseconds	20.0
V_THRESH	Spiking threshold	1.0
V_RESET	Reset potential	0.0
INPUT_HIDDEN_MEAN	Mean synaptic weight from input to hidden neurons	0.078
INPUT_HIDDEN_STD	Standard deviation of synaptic weights from input to hidden neurons	0.045
HIDDEN_OUTPUT_MEAN	Mean synaptic weight from hidden rto output neurons	0.2
HIDDEN_OUTPUT_STD	Standard deviation of synaptic weights from hidden to output neurons	0.37
HIDDEN_HIDDEN_MEAN	Mean of initial synaptic weights of hidden to hidden recurrent connections	0.2
HIDDEN_HIDDEN_STD	Mean of initial synaptic weights of hidden to hidden recurrent connections	0.37
PDROP_INPUT	Probability of dropping input spikes	0.2
PDROP_HIDDEN	Probability of dropping spikes in the hidden layer	0.0
REG_TYPE	Type of regularisation to apply to the hidden layer	"none"
LBD_UPPER	Regularisation strength of per-neuron regularisation for hyper-active neurons	0.000005
LBD_LOWER	Regularisation strength of per-neuron regularisation for neurons with low activity	0.001
NU_UPPER	Target activity level per hidden neuron per trial"]= 2
RHO_UPPER	Target activity level for the entire hidden layer per trial	5000.0
GLB_UPPER	Strength of regularisation based on global number of spikes per trial (type "Thomas1"	1e-5

Learning parameters

Name	Description	Default
ETA	Learning rate	5e-3
ADAM_BETA1	Adam optimiser parameter	0.9
ADAM_BETA2	Adam optimiser parameter	0.999
ADAM_EPS	Adam optimiser parameter	1e-8

Recording controls

Name	Description	Default
W_OUTPUT_EPOCH_TRIAL	List of 2-entry lists [epoch,trial] at wich to save weights (replaes the intrvals above)	[]
SPK_REC_STEPS	Size of the GeNN spike recording buffer in timesteps	TRIAL_MS/DT_MS
REC_SPIKES_EPOCH_TRIAL	Controls at which [epoch,trial] to record spikes	[]
REC_SPIKES	List of neuron populations to record spikes from, possible entries "input", "hidden", "output"	[]
REC_NEURONS_EPOCH_TRIAL	Controls at which [epoch,trial] to record neuron vars	[]
REC_NEURONS	List of pairs (neuron population, variable name) to record from, possible entries for population are "input", "hidden", "output"	[]
REC_SYNAPSES_EPOCH_TRIAL	Controls at which [epoch,trial] to record synapse vars	[]
REC_SYNAPSES	List of pairs (synapse population, synapse variable name) to record from, possible entries for synapse population are "in_to_hid", "hid_to_out"	[]
WRITE_TO_DISK	Whether to write outputs to disk or just return them from the run function	True
LOAD_LAST	Whether to load a checkpoint from a previous run	False

Loss types and parameters

Name	Description	Default
LOSS_TYPE	The loss function to use, possible values "first_spike", "first_spike_exp", "max", "sum", "avg_xentropy"	"avg_xentropy"
EVALUATION	How to form a validation set	"random"
CUDA_VISIBLE_DEVICES	Internal GeNN switch how CUDA devices are addressed	True
AVG_SNSUM	Whether to average spike counts across a mini-batch for regularisation spike counts	False
REDUCED_CLASSES	A list of classes to train; if None, all classes are trained	None
TAU_0	Parameter of the first_spike loss functions	0.5
TAU_1	Parameter of the first_spike loss functions	6.4
ALPHA	Parameter of the first_spike loss functions	3e-3

SHD/SSC parameters

General parameters

Name	Description	Default
NAME	A unique name for an experiment; results will be appended if a result file with this name already exists	"test"
DEBUG_HIDDEN_N	Whether to collect and return information about the activity levels of hidden neurons	False
OUT_DIR	Directory where to write results	"." (current dir)
DT_MS	Time step od the simulations in millisceonds	1.0
BUILD	Whether to (re)build the GeNN model, can be set to False if there are repeated runs of the same model	True
TIMING	Whether to record timing information during a run	True

Experiment parameters

Name	Description	Default
TRAIN_DATA_SEED	Seed for the random number generator used for generating training data	123
TEST_DATA_SEED	Seed for the random number generator used for generating testing data	456
MODEL_SEED	A separate random number seed for the random number generator that is used during model generation, e.g. for random initial values of synapse weights	None
TRIAL_MS	Duration of trials in milliseconds	1400.0
N_MAX_SPIKE	Size of the bufferfor saved spikes in number of spikes; note the buffer contains saved spikes across two trials	1500
N_TRAIN	Number of training examples	7644
N_VALIDATE	Number of examples in the validation set	512
SHUFFLE	Whether to shuffle the inputs in the training set	True

Network structure

Name	Description	Default
NUM_HIDDEN	Number of neurons in the hidden layer	256
RECURRENT	Whether to include recurrent connections	False
N_HID_LAYER	Number of hidden layers	1

Model parameters

Name	Description	Default
TAU_SYN	Synaptic timescale in millisecons	5.0
TAU_MEM	Membrane times constant in milliseconds	20.0
V_THRESH	Spiking threshold	1.0
V_RESET	Reset potential	0.0
HIDDEN_NEURON_TYPE	Hidden neuron type, possible values "LIF", "hetLIF", ALIF"	"LIF"
OUTPUT_NEURON_TYPE	Output neuron type, possible values "LI", "hetLI"	"LI"
TAU_B	Timascale of adaptation of ALIF neurons	100.0
B_INCR	Increment of the spike rate adaptation variable B upon a spike	0.1
B_INIT	Initial value of the spike rate adaptation variable B	0.0
INPUT_HIDDEN_MEAN	Mean synaptic weight from input to hidden neurons	0.078
INPUT_HIDDEN_STD	Standard deviation of synaptic weights from input to hidden neurons	0.045
HIDDEN_OUTPUT_MEAN	Mean synaptic weight from hidden rto output neurons	0.2
HIDDEN_OUTPUT_STD	Standard deviation of synaptic weights from hidden to output neurons	0.37
HIDDEN_HIDDEN_MEAN	Mean of initial synaptic weights of hidden to hidden recurrent connections	0.2
HIDDEN_HIDDEN_STD	Mean of initial synaptic weights of hidden to hidden recurrent connections	0.37
HIDDEN_HIDDENFWD_MEAN	Mean synaptic weight of forward synapses between multiple hidden layers	0.02
HIDDEN_HIDDENFWD_STD	Standard deviation of weights of forward synapses between multiple hidden layers	0.01
PDROP_INPUT	Probability of dropping input spikes	0.2
PDROP_HIDDEN	Probability of dropping spikes in the hidden layer	0.0
REG_TYPE	Type of regularisation to apply to the hidden layer	"none"
LBD_UPPER	Regularisation strength of per-neuron regularisation for hyper-active neurons	0.000005
LBD_LOWER	Regularisation strength of per-neuron regularisation for neurons with low activity	0.001
NU_UPPER	Target activity level per hidden neuron per trial"]= 2
RHO_UPPER	Target activity level for the entire hidden layer per trial	5000.0
GLB_UPPER	Strength of regularisation based on global number of spikes per trial (type "Thomas1"	1e-5
REWIRE_SILENT	Whether to rewire silent hidden neurons	False
REWIRE_LIFT	Whether and how much to uplift incoming synapse weights of silent hidden neurons (if not zero, this replaces rewiring)	0.0

Learning parameters

Name	Description	Default
ETA	Learning rate	5e-3
ADAM_BETA1	Adam optimiser parameter	0.9
ADAM_BETA2	Adam optimiser parameter	0.999
ADAM_EPS	Adam optimiser parameter	1e-8
EMA_ALPHA1	Factor of the fast exponential moving average of accuracy (used for LR schedule)	0.8
EMA_ALPHA2	Factor of the fast exponential moving average of accuracy (used for LR schedule)	0.95
ETA_FAC	Factor by which to multiply the learning rate when applying the LR schedule	0.5
MIN_EPOCH_ETA_FIXED	Minimal number of epochs that the learning rate stays fixed during a learning rate schedule	300
TRAIN_TAU	Whether to train tau values	False
MIN_TAU_M	Lower bound for the value of tau_m	3.0
MIN_TAU_SYN	Lower bound for the value of tau_syn	1.0
TRAIN_TAU_OUTPUT	Whether to train the timescale of the output neurons	False
TRAIN_W	Whether to train synaptic weights	True
TRAIN_W_OUTPUT	Whether to train the synaptic weights towards the output neurons	True

Recording controls

Name	Description	Default
W_OUTPUT_EPOCH_TRIAL	List of 2-entry lists [epoch,trial] at wich to save weights	[]
TAU_OUTPUT_EPOCH_TRIAL	List of 2-entry lists [epoch,trial] at wich to save tau values	[]
SPK_REC_STEPS	Size of the GeNN spike recording buffer in timesteps	TRIAL_MS/DT_MS
REC_SPIKES_EPOCH_TRIAL	Controls at which [epoch,trial] to record spikes	[]
REC_SPIKES	List of neuron populations to record spikes from, possible entries "input", "hidden", "output"	[]
REC_NEURONS_EPOCH_TRIAL	Controls at which [epoch,trial] to record neuron vars	[]
REC_NEURONS	List of pairs (neuron population, variable name) to record from, possible entries for population are "input", "hidden", "output"	[]
REC_SYNAPSES_EPOCH_TRIAL	Controls at which [epoch,trial] to record synapse vars	[]
REC_SYNAPSES	List of pairs (synapse population, synapse variable name) to record from, possible entries for synapse population are "in_to_hid", "hid_to_out"	[]
WRITE_TO_DISK	Whether to write outputs to disk or just return them from the run function	True
LOAD_LAST	Whether to load a checkpoint from a previous run	False
LOAD_BEST	Whether to load a checkpoint of a best observed training stage	False

Loss types and parameters

Name	Description	Default
LOSS_TYPE	The loss function to use, possible values "first_spike", "first_spike_exp", "max", "sum", "sum_weigh_linear", sum_weigh_exp", "sum_weigh_sigmoid", "sum_weigh_input", "avg_xentropy"	"sum_weigh_exp"
EVALUATION	How to form a validation set, values "random" or "speaker"	"random"
SPEAKER_LEFT	List of speakers to leave out (ordinal number in the list of speakers not speaker IDs - values 0-9 allowed)	[0]
CUDA_VISIBLE_DEVICES	Internal GeNN switch how CUDA devices are addressed	True
AVG_SNSUM	Whether to average spike counts across a mini-batch for regularisation spike counts	False
REDUCED_CLASSES	A list of classes to train; if None, all classes are trained	None
TAU_0	Parameter of the first_spike loss functions	1.0
TAU_1	Parameter of the first_spike loss functions	100.0
ALPHA	Parameter of the first_spike loss functions	5e-5
AUGMENTATION	Dictionary of augmentations to apply to the training data, possible values {"random_shift": x}, {"random_dilate": [y0, y1]}, {"ID_jitter": z} where x is max size of shoft, y0,y1 min/max dilation factors, z range of the jitter across input channels	{}
COLLECT_CONFUSION	Whether to collect confusion matrices during training / testing	False
REC_PREDICTIONS	Whether to record the individual predictions during training / testing	False