This is the readme file for the model associated with the paper

Peter Jedlicka, Lubica Benuskova, Wickliffe C. Abraham (2015)
A Voltage-Based STDP Rule Combined with Fast BCM-like Metaplasticity
Accounts for LTP and Concurrent "Heterosynaptic" LTD in the Dentate
Gyrus in vivo.
PLOS Computational Biology 11(11):e1004588.

Send bug reports, comments and questions on how to use this model to

The code for granule cell morphology, active and passive properties was
taken from Santhakumar et al:


The vast majority of computational studies that model synaptic
plasticity neglect the fact that in vivo neurons exhibit an ongoing
spontaneous spiking which affects the dynamics of synaptic
changes. Here we study how key components of learning mechanisms in
the brain, namely spike timing-dependent plasticity and
metaplasticity, interact with spontaneous activity in the input
pathways of the neuron. Using biologically realistic simulations we
show that ongoing background activity is a key determinant of the
degree of long-term potentiation and long-term depression of synaptic
transmission between nerve cells in the hippocampus of freely moving
animals. This work helps better understand the computational rules
which drive synaptic plasticity in vivo.

Details: We used computational modeling of plasticity at the medial
and lateral perforant path inputs to dentate gyrus granule cells to
account for the effects of different frequencies and temporal patterns
of HFS on the induction of homosynaptic LTP and concurrent
heterosynaptic LTD, as observed in vivo (Bowden et al. Hippocampus
2012). The main conclusions are: (1) Combined STDP and BCM rules can
reproduce the LTP and heterosynaptic LTD, (2) as long as spontaneous
activity continues in the input pathways. (3) The degree of LTD
depends on the degree of LTP, due to the implemented homeostatic BCM
rule that stabilizes cell firing rate. (4) Standard 100 Hz-TBS gives
counter-intuitively poor LTP and LTD because this protocol is very
good at firing granule cells, which in turn causes the potentiation
amplitude parameter to transiently decline, hence braking LTP.


1. Download and install NEURON (available from
2. Compile the NEURON mod files:

	mswin: run mknrndll and select this folder to create the
	linux: run nrnivmodl in this folder
	mac os x: drag and drop this folder onto mknrndll

3. Insert and use the STDP-BCM mechanisms from the mod files:

	Exp2SynSTDP_multNNb_globBCM_intscount_precentred.mod is a
	synaptic mechanism implementing a form of stream-specific
	spike-timing-dependent plasticity (STDP).
	BCMthreshold.mod is a cell-wide metaplasticity mechanism for
	computing sliding BCM threshold based on recent spike count
	(Benuskova et al. PNAS 2001, Benuskova and Abraham JCNS 2007)
	You can find an example using the 2 interlinked mechanisms
	(from the 2 mod files) in the mosinit.hoc file.
	Start the simulation:
	mswin: double click the mosinit.hoc file using windows
	explorer linux: type "nrngui mosinit.hoc" in the shell prompt
	in this folder mac os x: drag and drop the mosinit.hoc file
	onto the nrngui icon

	To run the simulation click on Init & Run button.

	The simulation executes stochastic activation of one STDP-BCM
	synapse with mean frequency of 100 Hz.  The synapse is
	connected to an active single compartment cell with hh
	channels.  The simulation starts running and begins to
	generate a plot of the voltage (cell.v),
screenshot 2
	weight (syncon.weight[1]) 
screenshot 3
	and integrated spike count (alpha_scount_BCMthreshold).
screenshot 1
	Note the homeostatic adjustment of spiking, weight and the spike 
	count (due to BCM-like metaplasticity).