{"action":"opened","issue":{"url":"https://api.github.com/repos/NeuralEnsemble/PyNN/issues/367","labels_url":"https://api.github.com/repos/NeuralEnsemble/PyNN/issues/367/labels{/name}","comments_url":"https://api.github.com/repos/NeuralEnsemble/PyNN/issues/367/comments","events_url":"https://api.github.com/repos/NeuralEnsemble/PyNN/issues/367/events","html_url":"https://github.com/NeuralEnsemble/PyNN/issues/367","id":92080793,"number":367,"title":"Wrong AdEx dynamics for delta_T=0 in Neuron backend","user":{"login":"bvogginger","id":1301537,"avatar_url":"https://avatars.githubusercontent.com/u/1301537?v=3","gravatar_id":"","url":"https://api.github.com/users/bvogginger","html_url":"https://github.com/bvogginger","followers_url":"https://api.github.com/users/bvogginger/followers","following_url":"https://api.github.com/users/bvogginger/following{/other_user}","gists_url":"https://api.github.com/users/bvogginger/gists{/gist_id}","starred_url":"https://api.github.com/users/bvogginger/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/bvogginger/subscriptions","organizations_url":"https://api.github.com/users/bvogginger/orgs","repos_url":"https://api.github.com/users/bvogginger/repos","events_url":"https://api.github.com/users/bvogginger/events{/privacy}","received_events_url":"https://api.github.com/users/bvogginger/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-06-30T12:33:45Z","updated_at":"2015-06-30T12:33:45Z","closed_at":null,"body":"In the Adapative-Exponential I&F neuron ('EIF_cond_exp_isfa_ista' or 'EIF_curr_exp_isfa_ista'), the neuron dynamics are wrong in the Neuron backend when the slope factor 'delta_T' is set to 0.\r\n\r\nNEURON triggers a spike when \"v_spike\" is reached. But, the membrane voltage should go to infinity as soon as \"v_thresh\" is reached.\r\nSee http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model#Physiological_interpretation, where the limit delta_T -> 0 is discussed.\r\n\r\nThe term: g_L * delta_T *exp((V-v_thresh)/delta_T) for limes delta_T -> 0 goes to 0 when V < v_thresh, and towards infinity when V>v_thresh. Hence, v_thresh is virtual spike detection threshold.\r\nSee: [wolframalpha] (http://www.wolframalpha.com/input/?i=limes+x-%3E0+x*exp%281%2Fx%29)\r\n\r\n\r\nIn contrast, Nest handles this case correctly.\r\n![adex_delta_t_zero](https://cloud.githubusercontent.com/assets/1301537/8430783/6e6a5e8e-1f34-11e5-9635-abaeb200e7b9.png)\r\n\r\nVersions used:\r\npyNN: 0.7.6dev\r\nNest: 2.2.2\r\nNeuron: 7.2\r\n\r\nI can provide the script to generate the plot.\r\n"}}