Table 1. Model parameters.

Neural activity
N		Number of synaptic inputs
r0	[s−1]	Baseline rate of the output neuron
τS	[s]	Adaptation kernel short time constant
τL	[s]	Adaptation kernel long time constant
μ		Adaptation kernel scaling parameter
Spatial exploration
L	[m]	Side-length of the arena
v	[m/s]	Running speed of the virtual rat
σθ		Standard deviation of running directions
Input spatial tuning
rav	[s−1]	Average input rate in the arena
σ	[m]	Width of the input receptive fields
M		Number of receptive fields per neuron (spatially-irregular inputs)
Synaptic plasticity
η		Learning rate
τW	[s]	Decay time constant of the learning window W
Wtot	[s]	Integral of the learning window W
α		Multiplicative weight-normalization constant
β		Additive weight-normalization constant
Derived quantities
a	[s−1]	Multiplicative weight-normalization rate
b	[s−1]	Additive weight-normalization rate
λmax	[s−1]	Maximal eigenvalue
$w_{\text{av}}^{\infty }$		Average synaptic weight
τav	[s]	Weight normalization time scale
τstr	[s]	Structure formation time scale