... | ... | @@ -12,7 +12,7 @@ University of Heidelberg |
|
|
**Computing with physics: aspects of bio-inspired artificial intelligence**
|
|
|
|
|
|
Tuesday, 19 November 2019, 1:15pm<br>
|
|
|
Where: building 14.6U, room 241
|
|
|
Building 14.6U, room 241
|
|
|
|
|
|
|
|
|
**Abstract**
|
... | ... | @@ -24,8 +24,7 @@ What is certainly much less obvious is the level of abstraction at which one sho |
|
|
In my talk, I will discuss some intriguing recent ideas about how complex cortical computation could emerge from rather simple mathematical principles. While the line of thought will certainly betray a physicist's bias towards parsimony, it is substantiated by links to both biological data and applications on artificial neuromorphic substrates. With respect to neuronal dynamics, I will address some computational advantages of having spikes in a Bayesian brain and how the associated information processing links to other cortical observables. With respect to synaptic plasticity, I will discuss how efficient learning can happen both at the level of single neurons and across cortical hierarchies. These insights help reconnect the booming field of artificial neural networks to its original, biological source of inspiration, and further nourish the development of efficient computation on synthetic, brain-inspired substrates.
|
|
|
|
|
|
|
|
|
**Host: Prof. Dr. Markus Diesmann**
|
|
|
|
|
|
**Host: Prof. Dr. Markus Diesmann**<br>
|
|
|
Institute of Neuroscience and Medicine (INM-6)<br>
|
|
|
Computational and Systems Neuroscience<br>
|
|
|
Institute for Advanced Simulation (IAS-6)<br>
|
... | ... | |