brain and mind

Download brain and mind

Post on 16-Apr-2017

435 views

Category:

Education

0 download

Embed Size (px)

TRANSCRIPT

  • 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 wont 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 V 7/14.0 V 0 V

    -11.4 11.4 0t p

  • 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, whats 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 Gamma40HZ

  • 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

    Its 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

  • Hebbs Assembly

  • O

  • O

  • A Proposed Marriage of Hebbian Neurophysiology to Gestalt Psychology

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

    MetaphorSocial 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