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SUMMARY:It's the Network Dummy: Exhuming the reticular theory while shovel
 ing a little dirt on the neuron doctrine - Tom Dean\, Google Research
DTSTART:20160316T100000Z
DTEND:20160316T110000Z
UID:TALK64743@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Scott McNealy\, CEO of Sun Microsystems\, is rumored to have q
 uipped\, "It's the network dummy"\, when a reporter asked where the comput
 er was upon seeing a cluster of workstations. McNealy's point was that the
  power of computer networks isn't the (linear) sum of individual computers
 \; the power is in the (nonlinear) manner in which they can work together.
  When a computational neuroscientist examines an EM image of neural tissue
 \, does she see a network or a bunch of neurons? The answer will depend on
  what she understands to be the fundamental unit of neural computation?\n\
 nWe assume the fundamental unit of neural computation is not the individua
 l neuron\, compartment\, synapse or even circuit in the traditional sense 
 in which electrical engineers think of circuits\, but rather ensembles of 
 hundreds or thousands of neurons that organize themselves depending on the
  task\, participate in multiple tasks\, switch between tasks depending on 
 context and are easily reprogrammed to perform new tasks. Consequently\, t
 he total number of computational units is far fewer than the number of neu
 rons.\n\nWe also assume that much of what goes on in individual neurons an
 d their pairwise interactions is in service to maintaining an equilibrium 
 state conducive to performing their primary role in maintaining the body a
 nd controlling behavior. This implies that the contribution of small neura
 l circuits to computations facilitating meso- or macro-scale behavior is c
 onsiderably less than one might expect given the considerable complexity o
 f the individual components. Since much of the complexity will manifest it
 self in the topology of the network\, we need some means of computing topo
 logical invariants at multiple scales in order to tease out the computatio
 nal roles of the multitude of circuit motifs that are likely present even 
 in parts of the brain assumed to be structurally and functionally homogene
 ous.\n\nIn the talk\, we describe the convergence of several key technolog
 ies that will facilitate our understanding of neural circuits satisfying t
 hese assumptions. These technologies include (i) high-throughput electron 
 microscopy and circuit reconstruction for structural connectomics\, (ii) d
 ense two-photon-excitation fluorescent voltage and calcium probes for func
 tional connectomics\, and (iii) analytical methods from algebraic topology
 \, nonlinear dynamical systems and deep recurrent neural networks for infe
 rring function from structure and activity recordings.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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