[Neuro] [ml] [Noisebridge-discuss] Technical question: Directionally sensitive photosensors

Christoph Maier cm.hardware.software.elsewhere at gmail.com
Tue Jun 11 19:47:40 UTC 2013

On Tue, Jun 11, 2013 at 10:59 AM, John Hurliman <jhurliman at jhurliman.org> wrote:
> It sounds like you are implementing some form of the Rall cable equation
> (http://www.scholarpedia.org/article/Rall_model) to model signal decay? I
> don't have any code off-hand, but both of the neuroscience Coursera classes
> I'm taking right now (https://class.coursera.org/bluebrain-001 and
> https://class.coursera.org/compneuro-001) covered this. Particularly, the
> video "Dendritic computation"
> (https://class.coursera.org/bluebrain-001/lecture/115). I'm not sure if the
> videos are accessible unless you enroll in the class though.

I really don't know what the birds are called that I'm trying to put
together ... for now, I call them "fireflies".
As for Coursera, I'm just downloading a lot of courses without really
trying to digest them yet, including the bluebrain one.
i find https://github.com/jplehmann/coursera/ a helpful tool for that.

> You might try posting to the openworm-discuss mailing list, since many of
> the devs there have been experimenting with a variety of neuron simulators
> and might be able to point you to some working code.

I'm trying to do this in hardware, with about 4 transistors and a
handful of passives (where the unknown one is the photosensor I'm
asking about),
so the working code would be the relative positioning of the
4-transistor units, and the regions of sensitivity of the photosensors
[hence the question]

> On Tue, Jun 11, 2013 at 9:34 AM, Mike Schachter <mschachter at eigenminds.com>
> wrote:
>> Also cc'ing this discussion to the neuro and ML lists, there are might be
>> people there that could help with the problem.
>>  mike
>> On Tue, Jun 11, 2013 at 9:25 AM, Mike Schachter
>> <mschachter at eigenminds.com> wrote:
>>> I suppose the basic idea is to create a chain of integrate-and-fire
>>> neurons.
>>> Referring to the attached diagram, each neuron gets input from it's own
>>> photosensor, and from the neuron to the left of it. You will also have to
>>> make it so that the signal from the photosensor to neuron *decreases* as you
>>> move right on the chain.
>>> The general idea is that movement to the right makes each neuron spike,
>>> which sends a spike to the neighboring neuron. If the inputs are timed right
>>> (which is probably the most tricky part), each successive neuron will spike
>>> more and send more input to it's neighbors. The last neuron, the readout
>>> neuron, will spike the most. You can look at the spike rate of the last
>>> neuron, and if it's high enough, then you declare movement to the right. The
>>> number of spikes of the last neuron could even be related to the velocity of
>>> the movement.
>>> Movement to the left shouldn't elicit a single spike from the readout
>>> neuron.
>>> Another option might be to build a Reichardt correlator:
>>> http://people.csail.mit.edu/rondror/Ronfiles/Research/reichardt.htm
>>> mike
>>> On Mon, Jun 10, 2013 at 11:46 PM, Christoph Maier
>>> <cm.hardware.software.elsewhere at gmail.com> wrote:
>>>> On Mon, Jun 10, 2013 at 5:49 PM, Mike Schachter
>>>> <mschachter at eigenminds.com> wrote:
>>>> > Oh ok! Well I'd be happy to help Chris with code to do
>>>> > directional-selection
>>>> > given data from an array of photo-sensitive inputs if that would be
>>>> > useful.
>>>> >
>>>> >  mike
>>>> >
>>>> >
>>>> >
>>>> > On Mon, Jun 10, 2013 at 5:20 PM, Norman <pryankster at gmail.com> wrote:
>>>> >>
>>>> >> Mike, that is exactly how a phased array works.
>>>> >>
>>>> >> Norman
>>>> >>
>>>> >>
>>>> >> On 6/10/2013 5:07 PM, Mike Schachter wrote:
>>>> >>
>>>> >> Can you just get an array of non-directional photosensors and do the
>>>> >> direction-selectivity computationally on an arduino or something?
>>>> >>
>>>> >>  mike
>>>> Great!
>>>> Just tell me how to put code into this kind of computer ...
>>>> http://pony.noisebridge.net/~cmaier/neuromorphic/BIL2011/html/img10.html
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