Searching for principles of brain computation
Experimental methods in neuroscience, such as calcium-imaging and recordings
with multi-electrode arrays, are advancing at a rapid pace. They produce
insight into the simultaneous activity of large numbers of neurons, and into
plasticity processes in the brains of awake and behaving animals. These new
data constrain models for neural computation and network plasticity that
underlie perception, cognition, behavior, and learning. I will discuss in
this short article four such constraints: Inherent recurrent network activity
and heterogeneous dynamic properties of neurons and synapses, stereotypical
spatio-temporal activity patterns in networks of neurons, high trial-to-trial
variability of network responses, and functional stability in spite of
permanently ongoing changes in the network. I am proposing that these
constraints provide hints to underlying principles of brain computation and
Reference: W. Maass.
Searching for principles of brain computation.
Current Opinion in Behavioral Sciences (Special Issue on Computational
Modelling), 11:81-92, 2016.