Neurophysiology and Behavior: Spike Trains and Fields...Final thoughts •Bridging the gap between biology/behavior-based neuroscience and computational neuroscience •Train next

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)

• So approximately 10^20 “switches” per brain

Stephen Smith

How many neurons per voxel?

https://cfn.upenn.edu/aguirre/wiki/public:neurons_in_a_voxel

Lent et al., 2012

• Approximately 1000 Trillion synapses

• Approximately 10^5 “switches” per synapse (channels, receptors, transporters)

• So approximately 10^20 “switches” per brain

Stephen Smith

Each medium spiny neuron receives input from several thousand excitatory cortical neurons

Striatal medium spiny neuron

Lynn Raymond, UBC

Logothetis 2008

Logothetis 2008







• EEG (briefly)





theremino.com

http://www.mtchs.org/BIO/text/chapter28



https://www.studyblue.com/notes/note/n/chapter-48-nervous-system/deck/4169450

techlab.bu.edu

http://www.interactive-biology.com/99/the-isoelectric-point-and-how-it-leads-to-an-action-potential/

Action potential (spikes) are the output from cell bodies/axons

http://www.qbi.uq.edu.au/brain-facts/neuroscience-basics-action-potentials-and-synapses

www.ruhr-uni-bochum.de

syntheticneurobiology.org

Extracellular electrophysiological recording

Moorman and Aston-Jones, 2010

Dendrite (input)

Axon (output)

Soma (cell body)

newton.umsl.edu

Rolston et al., 2009

Raw electrical signals are filtered into different types of neural activity

http://lifesciences.ieee.org/publications/newsletter/april-2012 - M. Mollazadeh

Einevoll et al., 2012

Example of four neurons recorded from one electrode wire in rat prefrontal

cortex

Courtesy of Lex Kravitz

Recording the activity of more than one neuron or field location

Chapter 1, State-of-the-Art Microwire Array Design for Chronic Neural Recordings in Behaving Animals

Methods for Neural Ensemble Recordings. 2nd edition.

Nicolelis MAL, editor.

Electrodes are implanted in the brain and connected to amplifiers and

filters

Rolston et al., 2009

Wireless recording of multiple neurons

Szutz et al., 2011

From: Chapter 5, Chronic Recordings in Transgenic Mice

Methods for Neural Ensemble Recordings. 2nd edition.

Nicolelis MAL, editor.

Stuart Layton, Wikipedia

Tetrodes are used to precisely isolate multiple neurons

Voigts et al., 2013

Einevoll et al., 2013

Local field potentials are a summation of synaptic

input (dendrites) and neuronal population activity

Increases in LFP power at specific frequencies underlies different behavioral/cognitive functions

Wang 2010

Hopkinsmedicine.org

backyardbrains.org

backyardbrains.org

Adjamian 2014







• EEG (briefly)





How does fMRI relate to electrical activity?

Logothetis et al., 2001

Using optogenetics to dissect BOLD signalsuggests spiking activity may contribute

Leopold – comment on Lee et al., 2010

Optogenetics as a tool to study neuronal function

http://neurobyn.blogspot.se/2011/01/controlling-brain-with-lasers.html Deisseroth Lab

Excitatory and inhibitory light-sensitive proteins

Deisseroth Lab

Optogenetic excitation and inhibition of neurons

Jones et al., 2015

Optogenetic control of behavior

Liang et al., 2015

Optogenetic circuit mapping in awake and

anesthetized animals







• EEG (briefly)





Data analysis methods - spikes

• Action potentials = spikes

• Patterns of spikes = spike trains

• Spike trains are point processes• Though can be smoothed

• LFPs are continuous

• Most basic forms of analysis:• Peristimulus time histogram: PSTH

• Population averages

backyardbrains.org

Baranauskas 2015

Rate codes and temporal codes

Relationship of spikes to behavior: perievent histograms and rasters

Hernandez and Moorman, in prep

Multi-neuron population histograms

• Average together all the neurons that you recorded from to characterize what the brain area “does”

• This is unappealing for a number of reasons• Heterogeneity



Even when we characterize populations of single neurons, we look for trends


Characterizing increased/decreased LFP power at specific frequencies during behavior

Ito et al., 2014

Issues with averaging/combining neural signals

• Neurons in a brain area don’t all do the same thing

• The same neuron may do multiple things

• Neuronal activity varies from trial to trial

• Neurons work together in populations

• Ensemble encoding may provide more information

Harris and Mrsic-Flogel 2013

What do we want to know?

• How do neurons encode information?• Reliably?• Flexibly?

• How do neurons interact with one another?

• How do ensembles of neurons interact with one another?

• Can we place a causal role on the relationships among neurons/ensembles?

More sophisticated ways of characterizing neural interactions

• Correlation• Cross correlogram, JPSTH

• Pattern analysis• E.g., triplets, more complex

patterns (e.g., synfirechains)

• Frequency analysis

• Principal/independent components analysis

Brown et al., 2004

Cross correlation

JPSTH

Maximum

likelihood

models

Cross coherence

Similarly LFPs can be analyzed for spatial and temporal correlation

Alain Destexhe and Claude Bedard (2013), Scholarpedia, 8(8):10713.

Population decoding

• Population vectors

• Reverse correlation

• Bayesian decoding

• Pattern classifier

Population vectors in the motor cortex

A. Georgopoulos

Population vector decoding of reward value in orbitofrontal cortex

Van Duuren et al., 2008

Pattern classifier decoding of prefrontal cortex information

Meyers et al., 2012

Higher level analyses over distance and time

• Granger causality

• Graph theory/rich club analysis

• Dynamic correlation

Coherence and Granger causality to show cross-structure relationships

Sirota et al., 2016

Evidence of rich club networks in neuronal activity

Nigam et al., 2016

Semiparametric models to dynamically characterize correlated activity across multiple

neurons

Shahbaba et al., 2014, Zhou et al., 2015

Scaling analyses to cope with the frontier of big data

Brain Activity Map

AKA BRAIN Initiative

Record from EVERY neuron in the brain

Neuron 2012

Science 2013

ACS Nano 2013

Neuroscience + Nanoscience

*all 86 billion of them

Can we record from every neuron?

Very large scale integration (VLSI) electrophysiology

Blanche et al., 2004Alivisatos et al., 2012

1000+ recording sites

LeafLabs/Boyden

Cellular optical imaging(2-photon calcium or voltage imaging)

http://biology.ucsd.edu/faculty/komiyama.html

Cellular optical imaging

Alivisatos et al., 2012

• Top: Conformational change

produced by Ca2+ binding.

• Middle: Diagram illustrating imaging

setup.

• Bottom: Blue lines plot movement of

an individual mouse in square and

circular arenas. Red dots mark the

animal’s position during Ca2+ events in

a specific CA1 neuron. Lower pairs of

figures show Gaussian-smoothed data.

Imaging “all” neurons from a zebrafish larva

Ahrens and Keller 2013

Nanomachines

• “Smart” wireless nanoscale transmitters

• Multiferroic antennas

• Ultrasmall nanoelectronicchips

• Nanoparticle labeling and reporting

Seo et al., 2013

Neurophysiological signals are used to control sensory and motor neural prosthetics in humans

http://www.stanford.edu/~shenoy/GroupResearchOverview.htm

Clinical applications of motor neural coding

Data analysis?

• Large Hadron Collider • ~10 petabytes/year

• 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

Documents

Neurophysiology and Behavior: Spike Trains and Fields...Final thoughts •Bridging the gap between biology/behavior-based neuroscience and computational neuroscience •Train next