David Beniaguev

8) Here depicted are weights for one of the artificial units in first layer of the large DNN that mimics a neuron with the full complexity: One can see the spatio-temporal structure of synaptic integration: The basal and oblique trees integrate predominantly recent inputspic.twitter.com/93JOjgd4yr

9) Here, a different first layer unit, the apical tree appears to pay attention to what happened on it for many more milliseconds than the basal and oblique trees that we saw in previous unit (BTW, the blue traces are inhibition, the red traces are excitation)pic.twitter.com/O5oAr8vxkK

10) if we look at the first layer units of the small DNN that fitted the neuron with AMPA only synapses, then it appears that the units don't really pay attention at all to what happens at the apical tree or any distal locations at basal and oblique trees.pic.twitter.com/QEBDzsB2KY

11) it is a little bit hard to see the details of what's going on in those weight plots because there are so many synapses. So Let's focus on a single dendritic branch and zoom in on it. For a single branch with NMDA, it's possible to mimic it's behavior with only 4 hidden unitspic.twitter.com/W1RKKURaUu

12) and here are it's spatio-temporal patterns of integration: I'll verbally describe those filters from top to bottom as questions that the neuron is "asking" the input in order to determine its output (Note: no inhibition here, only excitation. time window extent is 100ms)pic.twitter.com/J6Qqdy01fu

13) unit 1: was there very recent excitation that was proximal to soma? unit 2: was there very recent excitation that was distal to soma? unit 3: was there a quick distal to proximal pattern of excitation? unit 4: was there a slow distal to proximal pattern of excitation?pic.twitter.com/jE9uarrJkD