Tutorial 9 — Synaptic plasticity

neurogenesis.spiking.plasticity provides three rules, each usable offline (apply on a recorded spike train) or online, tick by tick (step, no T × N memory):

Rule	Class	Parameters
Pair-based STDP	`STDPRule`	`A_plus`, `A_minus`, `tau_plus`, `tau_minus`, `w_min`, `w_max`, `soft_bounds`, `integer_traces`
Hebbian with normalisation	`HebbianRule`	`eta`, `norm_target`, `norm_type`
Three-factor (eligibility trace × modulator)	`ThreeFactorRule`	`eta`, `tau_e`

Offline

import jax.numpy as jnp
from neurogenesis.spiking.plasticity import STDPRule

rule = STDPRule(A_plus=1.0, A_minus=1.05, tau_plus=20, tau_minus=20, w_min=0, w_max=1000)
new_w = rule.apply(weights, pre, post, delays, spikes, original_weights=weights)   # spikes: (T, N) array

Online

tr_pre, tr_post = rule.init_state(num_neurons, num_synapses)
for t in range(T):
    weights, tr_pre, tr_post = rule.step(weights, pre, post, spikes[t], arrivals[t], tr_pre, tr_post)

Inside development

The refine_by_activity stage (C11) runs a simulation inside a schedule and applies a rule, so that spontaneous activity can shape connectivity before the network is used (see Tutorial 5 — Projects, schedules and stages (experimental)).

refine_by_activity does nothing unless a rule is given, and keeps every synapse’s sign (Dale’s principle): excitatory and inhibitory weights have separate bounds.

Each rule declares its cost (declared_cost(num_neurons, num_synapses, ticks)).

PLACEHOLDER

Figures: the STDP window measured from the implementation; weight distributions before and after refinement by spontaneous waves; a GIF of receptive fields sharpening during activity-dependent refinement.