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Last modified by adavison on 2022/10/04 13:55
From version 23.1
edited by shailesh
on 2021/12/09 21:40
Change comment: There is no comment for this version
To version 22.1
edited by shailesh
on 2021/12/09 20:27
Change comment: There is no comment for this version

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85 85  PyNN comes with a selection of integrate-and-fire models. We're going to use the IF_curr_exp model, where "IF" is for integrate-and-fire, "curr" means that synaptic responses are changes in current, and "exp" means that the shape of the current is a decaying exponential function.
86 86  
87 -This is where we set the parameters of the model: the resting membrane potential is -65 millivolts, the spike threshold is -55 millivolts, the reset voltage after a spike is again -65 millivolts, the refractory period after a spike is one millisecond, the membrane time constant is 10 milliseconds, and the membrane capacitance is 1 nanofarad. We're also going to inject a constant bias current of 1.1 nanoamps into these neurons, so that we get some action potentials.
87 +This is where we set the parameters of the model: the resting membrane potential is -65 millivolts, the spike threshold is -55 millivolts, the reset voltage after a spike is again -65 millivolts, the refractory period after a spike is one millisecond, the membrane time constant is 10 milliseconds, and the membrane capacitance is 1 nanofarad. We're also going to inject a constant bias current of 0.1 nanoamps into these neurons, so that we get some action potentials.
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89 89  (% class="box infomessage" %)
90 90  (((