Neurophysiology and Behavior: Spike Trains and Fields David Moorman Psychological and Brain Sciences Neuroscience and Behavior Graduate Program University of Massachusetts Amherst CCNS: Challenges in Functional Connectivity Modeling and Analysis 2016
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Neurophysiology and Behavior: Spike Trains and Fields...Final thoughts •Bridging the gap between biology/behavior-based neuroscience and computational neuroscience •Train next
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Neurophysiology and Behavior: Spike Trains and Fields
David Moorman
Psychological and Brain Sciences
Neuroscience and Behavior Graduate Program
University of Massachusetts Amherst
CCNS: Challenges in Functional Connectivity Modeling and Analysis 2016
How to Read Character: A New
Illustrated Hand-Book of Phrenology
and Physiognomy, for Students and
Examiners; with a Descriptive Chart.
(New York, Fowler & Wells Co.,
Pubs., 1891)
Neural circuitry presents a complex view of the brain
MGH Human connectome project acquisition team,
Sanes, Lichtman, et al., Brainbow/Brainstorm Consortium
Cellular neural circuitry is even more complex
Singh 2012
Presentation Outline
• Brief background
• Biological basis of neural signals and how data are collected
• Different types of neural signals• Synaptic potentials/currents (briefly)
• Spikes/action potentials
• Local Field Potentials (LFP)
• EEG (briefly)
• Association of neural signals with behavior
• Relationship to BOLD signal
• Analysis of neural signals
• Future directions and challenges going forward
Some caveats
• Pace of presentation (slow)
• My research focus (minimized)
• My areas of expertise (and lack thereof)
• Happy to look into anything that I can’t address here
The problem
Information (sensory,
etc.)
Neural processing
Information transformation
Cognition
Action, behavior
Which level of analysis?
Rosie Cowell
Gazzaniga 2009
General THM
• Information is conveyed through neuronal activity• Contributions of non-neuronal cells (glia, etc.)?
• Neuron ensemble activity encodes information• Within brain areas• Across brain areas
• Neural code is complex• Spikes?• Fields?
• Neural data sets can be enormous and heterogeneous
• Neurons/ensembles themselves are highly heterogeneous• Periodic “check-ins” with biology
fMRI
Gazzaniga 2009
“…it’s like looking down at the US from a satellite seeing the grid of lights at night. You can infer certain things: Here’s a city, here’s a city. But to really understand the interactions between those cities you need to get down to the level of individual people moving around in cars. It’s a matter of scale and resolution.” -- Bill Newsome, Wired, 2013
“It makes no sense to read a newspaper with a microscope.” -- Valentino Braitenburg (quoted in Logothetis 2008)
Switfyscience.blogspot.com
Lent et al., 2012
• Approximately 1000 Trillion synapses
• Approximately 10^5 “switches” per synapse (channels, receptors, transporters)
• One recording study of the type proposed • ~ 1 gigabyte/sec
• 4 terabytes/hour,
• 100 terabytes/day
• Compressed, this equals ~3 petabytes/year
Future Challenges
• Scale• understanding interactions among large numbers of
neurons simultaneously
• Coding• finding meaning in patterns of activity in single neurons
and ensembles of neurons
• note also modulatory signaling
• Plasticity• neuron function changes over time
• Heterogeneity• many types of neurons even within one brain area
Final thoughts
• Bridging the gap between biology/behavior-based neuroscience and computational neuroscience
• Train next generation of multidimensional quantitative neuroscientists
• However, there is a need for translation between statistical models and experimental data sets – what do the results “mean”?• In the context of biology
• This is going to get even more complicated as data sets get larger and larger