Thanks Poul.
--------------------------
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----- Original Message -----
From: cellml-discussion-boun...@cellml.org 
<cellml-discussion-boun...@cellml.org>
To: CellML Discussion List <cellml-discussion@cellml.org>
Sent: Fri Jun 05 09:22:23 2009
Subject: Re: [cellml-discussion] Using CellML for simplified neuron models?     
(Alan Garny)

Dear Hans

Thank you for raising this. It s, in fact, one of the issues discussed
at the recent combined CellML SBGN-SBO BioPAX MIASE Workshop held this
April on Waiheke Island. There is a clear need to be able to specify
discontinuous processes and events, such as you have described.
However, both CellML and SBML use a declarative specification of
models, described with content MathML. Event handling fits more
naturally with imperative descriptions of models so there is currently
no clean way of describing events using content MathML. SBML, which
also uses content MathML as its underlying mathematical description
language, has addressed this problem by augmenting the language with
events and reset rules. After some discussion at the recent workshop,
the consensus was that the next iteration of CellML (1.2) would
include facilities for specifying events and applying reset rules in a
way that is consistent with SBML. There are several reasons for taking
this approach: it is a method that fits reasonably naturally with
modellers' notion of describing such models; the solution has been
tested by the SBML community; the construct will be straightforward to
handle when translating between SBML and CellML. We are currently
working on the CellML 1.2 specification and plan to have a draft
released shortly with the addition of events and reset rules to handle
problems such as you have described.

Best wishes
Poul

On 2009-06-05, at 00:50, Dr. Hans Ekkehard Plesser wrote:

>
> Hi Alan!
>
> [Apologies for breaking the thread, I had subscribed to the list in
> digest form.]
>
> Thank you for your example. Unfortunately, fixed time-stepping
> schemes where events (threshold
> crossings and membrane potential resets) can occur only on a fixed
> time grid are one of the big
> no-nos in neuronal network modeling, since they can lead to strong
> synchronization artefacts.
> Indeed, quite a lot of research in recent years has focused on
> algorithms to determine the exact
> time of threshold crossings efficiencly. I'd be happy to send you
> reference if you are interested.
>
> Thus, if we wanted to use CellML to represent neuron models in a
> general form, we would need a
> possibility to represent instantaneous events in continuous time. I
> believe SBML events provide
> this, don't they?
>
> Best,
> Hans
>
>
>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Mon, 1 Jun 2009 12:17:54 +0100
>> From: "Alan Garny" <alan.ga...@dpag.ox.ac.uk>
>> Subject: Re: [cellml-discussion] Using CellML for simplified neuron
>>      models?
>> To: "'CellML Discussion List'" <cellml-discussion@cellml.org>
>> Message-ID: <001001c9e2aa$9beba840$d3c2f8...@garny@dpag.ox.ac.uk>
>> Content-Type: text/plain; charset="us-ascii"
>>
>> Hi Hans,
>>
>>> We are looking for a good way to describe and share neuron models,
>>> and
>>> CellML appears a good
>>> candidate. The neuron models we are interested in consist mostly
>>> of a
>>> single compartment,
>>> possibly of a small number of compartments.
>>>
>>> As far as I can see, CellML appears well suited to describe the so-
>>> called
>>> subthreshold dynamics
>>> of model neurons. But I am wondering if CellML can also capture
>>> (or be
>>> extended to capture) the
>>> threshold operation present in most simplified neuron models.
>>> Briefly, the
>>> model dynamics are
>>> integrated according to a set of differential equations. When the
>>> membrane
>>> potential of the
>>> neuron crosses a certain threshold, the neuron is said to "fire":
>>> the
>>> membrane potential is
>>> reset to a certain value instantaneuously, and often clamped at
>>> that value
>>> for a certain period
>>> of time afterwards (refractory period); also, an output signal is
>> generated.
>>> In simple
>>> pseudocode, this would look like:
>>>
>>> while ( simulation time not up )
>>>
>>>  process input
>>>  update dynamics according to ODE
>>>
>>>  if ( neuron is refractory )
>>>    V_m = V_reset
>>>    count down "refractoriness"
>>>
>>>  if ( V_m > Threshold )
>>>    V_m = V_reset
>>>    emit output signal
>>>
>>>  count up time
>>
>> I believe this could easily be done, as long as you are OK with the
>> following:
>> - this would require integrating the model using an integration
>> technique
>> that relies on a fixed time step. In my experience, anything will
>> make your
>> resetting of Vm difficult.
>> - your output signal (Vm?) will always be generated.
>>
>> Attached is a very simple CellML file (based on the van der Pol
>> model) that
>> illustrates the kind of thing I think you are after. You want to
>> plot the x
>> and y parameters (see attached screenshot).
>>
>> Alan
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>>
>> ------------------------------
>>
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>>
>> End of cellml-discussion Digest, Vol 59, Issue 1
>> ************************************************
>
> --
> Dr. Hans Ekkehard Plesser
> Associate Professor
>
> Dept. of Mathematical Sciences and Technology
> Norwegian University of Life Sciences
>
> Phone +47 6496 5467
> Fax   +47 6496 5401
> Email hans.ekkehard.ples...@umb.no
> Home  http://arken.umb.no/~plesser
> _______________________________________________
> cellml-discussion mailing list
> cellml-discussion@cellml.org
> http://www.cellml.org/mailman/listinfo/cellml-discussion

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