payload: {"action":"opened","issue":{"url":"https://api.github.com/repos/BindsNET/bindsnet/issues/342","repository_url":"https://api.github.com/repos/BindsNET/bindsnet","labels_url":"https://api.github.com/repos/BindsNET/bindsnet/issues/342/labels{/name}","comments_url":"https://api.github.com/repos/BindsNET/bindsnet/issues/342/comments","events_url":"https://api.github.com/repos/BindsNET/bindsnet/issues/342/events","html_url":"https://github.com/BindsNET/bindsnet/issues/342","id":524203873,"node_id":"MDU6SXNzdWU1MjQyMDM4NzM=","number":342,"title":"Minibatch for clamp/unclamp?","user":{"login":"khengyeww","id":49545508,"node_id":"MDQ6VXNlcjQ5NTQ1NTA4","avatar_url":"https://avatars0.githubusercontent.com/u/49545508?v=4","gravatar_id":"","url":"https://api.github.com/users/khengyeww","html_url":"https://github.com/khengyeww","followers_url":"https://api.github.com/users/khengyeww/followers","following_url":"https://api.github.com/users/khengyeww/following{/other_user}","gists_url":"https://api.github.com/users/khengyeww/gists{/gist_id}","starred_url":"https://api.github.com/users/khengyeww/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/khengyeww/subscriptions","organizations_url":"https://api.github.com/users/khengyeww/orgs","repos_url":"https://api.github.com/users/khengyeww/repos","events_url":"https://api.github.com/users/khengyeww/events{/privacy}","received_events_url":"https://api.github.com/users/khengyeww/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2019-11-18T08:14:24Z","updated_at":"2019-11-18T08:14:24Z","closed_at":null,"author_association":"NONE","body":"Hello, I'm currently trying out the minibatch feature.\r\n\r\nIn network.run() function, inputs takes the tensor shape of `[time, batch_size, *input_shape]`.\r\nIn my use case, I need to clamp certain neurons to spike according to each input. Thus, to use the minibatch feature I have generated shape of `[time, batch_size, n_neurons]` for the clamp dict (to follow the shape of inputs) and received below error.\r\n```\r\n   self.layers[l].s[:, clamp[t]] = 1\r\nIndexError: too many indices for tensor of dimension 2\r\n```\r\nIs there a solution for this?"}}
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