•  Write  a  small  adv    about  your  self  

Gestalt  

Ambiguous figures such as the face-vase illusion (a) and Necker cube (b) pose points at which the brain must make a decision or choice about how to perceive and interpret sensory input.

Brains  ~  Computers  •  1000  opera:ons/sec  •  100,000,000,000  units  •  10,000  connec:ons/  •  graded,  stochas:c  •  embodied  •  fault  tolerant  •  evolves  •   learns  

•  1,000,000,000    ops/sec  •  1-­‐100  processors  •  ~  4  connec:ons  •  binary,  determinis:c  •  abstract  •  crashes  •  designed  •   programmed  

Does  the  brain  create  the  mind?  •  Prop  1.  Brain  creates  mind,    prob.    x    

•  Prop  2.  Brain  is  “antenna,”  prob.    1  –  x            

region or network? behavior or function? population? spatial or temporal resolution?

Clinical: “Does this treatment alleviate a particular symptom?” Translational: “Is activity in this region related to some feature of a disorder/disease?” Basic: “How does the brain accomplish some function?”

Different approaches have different strengths/weaknesses, and are suited to different kinds of problems. Electrophysiology (EEG) - high temporal resolution - low spatial resolution Analysis approaches - event related potentials (ERPs) - topographic/source analysis - continuous EEG

Different approaches have different strengths/weaknesses, and are suited to different kinds of problems. fMRI - low temporal resolution - high spatial resolution Analysis approaches - block designs - event-related designs - correlation analyses - fancy stuff we won’t have time for

256-­‐Channel  Geodesic  Sensor  Net  

EEG Signals

ERPs EEG is averaged Time locked to a stimulus

event May also be averaged to a

response event (Response Potential)

Increases signal-to-noise

Development Print Processing in the first 200 milliseconds

Difference

Words

Symbols

Age 8.3 220 ms

Age 6.5 220 ms

-7/14.0 µV 7/14.0 µV 0 µV

-11.4 11.4 0t p<0.01 p<0.01

< <

Age 26 150 ms

Maurer et al. (2007)

Graphics from http://www.mrc-cbu.cam.ac.uk/EEG/img/Physiological_basis_EEG.gif http://ww2.heartandstroke.ca/images/english/english_brain.jpg

Locating activity can be tricky! Orientation plays a role in what you see at the scalp…

The Inverse problem

Given a dipole source inside the head, we can solve for what it would look like on the scalp.

But the pattern of activity at the scalp has a one-to-many mapping back onto possible dipole sources.

So, what’s the upside?

A more “direct” measure of neural activity. Lets you look at neat stuff like oscillation frequency:

Alpha - strong in relaxed, awake states.

Theta - may be largely driven by hippocampus, prominent in short term memory tasks

Figures from Wikipedia entry on EEG, of all places

Figure from: Klimesch, W. (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis, Brain Research Reviews, Volume 29,169-195.

Temporal resolution allows fine-grained inferences about the timing of neural events.  

 

Molholm, S., Ritter, W., Murray, M.M., Javitt, D.C., Schroeder,C.E., and Foxe, J.J. (2002) Multisensory auditory-visual interactions during early sensory processing in humans: a high-density electrical mapping study, Cognitive Brain Research, 14, 115-128.

Some considerations for designing EEG studies: How important is spatial resolution?

Maybe the process you care about is related to a general “brain state,” e.g. a stage of sleep.  

Some considerations for designing EEG studies: Can you get enough data to do ERPs? (typically ~100 trials) Interestingly, kids and infants, with their thin skulls and little heads, give better EEG data and need fewer trials (but they wiggle around more).

Some considerations for designing EEG studies: How important is temporal resolution?

Here, the argument that multisensory integration happens early depends on rapid responses very short stimuli. But what if your stimuli are naturally long, or vary in duration?

Brain Oscillations

•  Oscillations emerge from the interaction between intrinsic cellular properties and circuit properties. –  individual oscillatory neurons –  individual non-oscillating neurons that are

hooked up in a specific network that produces oscillation.

•  In many systems electrical coupling by gap junctions tend to produce oscillatory synchronization.

Characteristics of Mammalian Oscillations

1.  Phylogenetic preservation in mammals. 2.  In mammals, frequencies cluster around specific

peaks - unlike vertebrates who show much more distributed frequencies.

3.  Power density of these oscillations is inversely proportional to frequency (1/f)

4.  This (1/f) relationship implies that perturbations occurring at slow frequencies cause a cascade of energy dissipation at higher frequencies (i.e., slow oscillations modulate faster local events).

Types of Oscillations

•  Brain networks oscillate and their oscillations are behavior dependent.

•  Oscillations range from 0.05 Hz to 500 Hz so the range is many orders of magnitude.

–  Delta 1-4 Hz –  Theta 4-8 Hz –  Alpha 8-12 Hz –  Beta 13-20 Hz –  Gamma—40HZ

Role of Thalamocortical Loops

•  Many of the rhythmic oscillations produced arise from thalamocortical re-entrant interactions and pacemaker cells that comprise thalamic nuclei.

 

   

Ventral  PMC/  Posterior  IFG  Sensorimotor  cortex  

 Rostral  IPL  Human  PF/PFG  

 Posterior  STS  

Visual  informa:on    external  cues  

Thalamus

The  Role  of  Thalamocor:cal  Loops  

Local and Global Networks

Choe, Y. IEEE Trans. Neural Net., 2003, 15(5): 1480-1485

Local  network    Motor  

             Mu  rhythm    

Local  network  Visual  

Classic  alpha  

Thalamus  

Mathema:cal  Models  of  Real  Systems  

Real  World  

Model  World  1  

Model  World  2  

Model  World  N    Black  Swans  

 The  Black  Swan,  Nassim  Taleb,  2007.  Rare  events  with  large  impacts.  

 Hierarchical  Dynamics  of  Human  Interac:ons  

Neocortex

Macrocolumn

Module

Minicolumn

Neuron

???

Global population

Nation

City

Neighborhood

Individual

Equality  of  :me  scales?  Top-­‐down,  mul:-­‐scale  neocor:cal  dynamic  plas:city?  

Biochemistry    Quantum  Fields    ???  PL  Nunez,    Behavioral  and  Brain  Sciences,  2000  

Tower of Babel

Working Assumptions

Starting simple: receptors, pathways, and circuits

Six working assumptions: 1.  Neurons work using an integrate-and-fire action 2.  Connections are either excitatory or inhibitory 3.  Idealized neurons are used in artificial neural nets to model brain function 4.  Neurons typically form two-way pathways, providing the basis for re-

entrant connectivity 5.  The nervous system is formed into arrays or maps of neurons 6.  Hebbian cell assemblies underlie the change from transient to stable,

lasting connections

Donald Hebb

Arrays and Maps

Maps flow into other maps: The nervous system often uses layers of neurons in giant arrays.

Arrays and Maps

Neuronal arrays usually have two-way connections

It’s rare to find brain ‘traffic’ that is not two-way. It makes sense to think of visual arrays, for example, as layers in a two-way network rather than a one-way path.

Arrays and Maps

Sensory and motor systems work together

Sensory and motor hierarchies Fuster (2004) suggests that all of cortex can be thought about in terms of sensory and motor hierarchies, with information flowing between the posterior sensory regions and the frontal motor and planning regions.

Arrays and Maps

Temporal codes: spiking patterns and brain rhythms

Neurons have different spiking codes. These two electrode traces show the voltage of simulated neurons with differing spiking codes

How Neural Arrays Adapt and Learn

Hebbian learning: ‘Neurons that fire together, wire together’

Donald Hebb was one of the most influential theorists for cognitive science and neuroscience. He clarified the notion of the cell assembly and proposed the best-known learning rule for neural networks

How Neural Arrays Adapt and Learn

Neural Darwinism: survival of the fittest cells and synapses

An example of Neural Darwinism in learning: stages of encoding a neural activation pattern until dynamic synaptic activity allows permanent connections to

be strengthened, allow memories to be stored

How Neural Arrays Adapt and Learn

Symbolic processing and neural nets

A network which represents a large set of propositions such as ‘a robin is a bird’ and ‘a rose has petals’.

Canonical  circuit  

Hebb’s  Assembly  

O  

O  

A  Proposed  Marriage  of  Hebbian  Neurophysiology  to  Gestalt  Psychology  

•  Cell  assemblies  (neural  networks)  embedded  in  global  synap:c  ac:on  fields  

•  Metaphor—Social  networks  embedded  in  a  culture  

•  Top  down  and  bocom  up  interac:ons  between  networks  and  global  fields  (“circular  causality”)    

Neuromodulation

-­‐   signal-­‐to-­‐noise  -­‐   switching  -­‐   burst/single  spike  -­‐   oscilla:ons  

Neuromodulators alter intrinsic properties of networks

Marder and Thilumalai, 2002

Synchronization of Inputs

At least three types of synchronies have their electrogenesis in cortex.

1. Those created locally between neighboring columns, which produce

high frequency components above 30 Hz (gamma rhythms). 2.  Intermediate or “regional” oscillations between cortical columns

separated by several centimeters, which produce intermediate frequency components (high alpha/mu : > 10 Hz; and beta: 12-20 Hz).

3.  Global synchronies between cortical regions that are significantly far apart, such as frontal and parietal or occipital and frontal regions. These are related to slow frequency components - delta (1-4 Hz), theta (4-8 Hz), and low alpha/mu (8-10 Hz).  

Specific Functions

1. Bias input selection (regulate flow of information) 2. Temporally link neurons into assemblies (“binding” –

contribute to the representation of information) 4. Facilitate synaptic plasticity 5. Support temporal representations and long-term

consolidation of information (assist in the storage and retrieval of information)

δΨe (r,t) = δΨ0 (r,t)+ dv

0

∞

∫ Γ(r,r1,v)δΘ r1,t −r − r1

v

⎛

⎝⎜⎜

⎞

⎠⎟⎟

d2r1cortex∫∫

Synaptic action field Action potential field

Velocity  integral                    Cor:co-­‐cor:cal  fibers  (nonlocal)  

A  linear  “brain  wave  equa:on”  in  2  dependent  variables:  Standing  waves  in  the  brain.  

Resonant  Interac:ons  Between  Semi-­‐autonomous  Oscillators  

•  Weakly  connected  oscillators  substan:ally  interact  only  when  certain  resonant  rela:ons  exist  between  the  characteris:c  frequencies  of  the  autonomous  oscillators  (a  mathema:cal  statement  largely  independent  of  neural  oscillator  models)  

 

•  Eugene  Izhikevich,  SIAM  J  App  Math,  1999  

   Resonant  Interac:ons  Between  Oscillators  2  

0f

1f 2f

Global  Synap:c  Field  (x,  t)  

 Summary:  The  following  physical  proper:es  may  be  

cri:cal  for  consciousness  to  occur      •  Nested  hierarchical  interac:ons  across  spa:al  scales.  Minicolumns  

within  cor:co-­‐cor:cal  columns  within  macrocolumns  within  lobes,  etc.  

 •  Non-­‐local  interac:ons  by  cor:co-­‐cor:cal  fibers,  allowing  for  much  

more  complex  dynamics.  

•  Resonant  interac:ons  between  networks  and  between  networks  and  global  fields  or  “binding  by  resonance”  analogous  to  chemical  bonds.    

•  This  raises  the  specula5on  that  consciousness  depends  cri5cally  on  resonance  phenomena  and  only  properly  tuned  brains  can  orchestrate  the  beau5ful  music  of  sen5ence.  

References  

PL  Nunez,  Oxford  U  Press,  2009  ?Mind,  Brain,  and  the  Emergence  of  Consciousness  

?Science,  Religion,  and  the  Mysteries  of  Consciousness        

PL  Nunez  and  R  Srinivasan,  Electric  Fields  of  the  Brain:  The  Neurophysics  of  EEG,  Oxford  U  Press,  2006    

www.electricfieldsohhebrain.com      

PL  Nunez,  Neocor5cal  Dynamics  and  Human  EEG  Rhythms,  Oxford  U  Press,  1995