EM

Cognitive Neuroscience Flashcards

Overview

  • The brain is described as a highly complex, non-mechanical organ, often likened to an electric jelly with intricate electrical and biochemical signaling.

  • Historically, brain study began with a physiological/medical lens and has expanded dramatically with mid-20th-century imaging advances, leading to cognitive neuroscience and cognitive science as major fields.

  • Cognitive neuroscience merges what the brain is doing with where it is doing it, giving rise to both interdisciplinary study and specialized degree programs.

  • Brain study can be approached through multiple lenses: macro (system-level) vs micro (cellular), regional specialization, development, and molecular signaling. The chosen lens depends on the question and goals of the researcher.

  • Key idea: All levels of analysis are valid and interlinked; the brain remains mysterious, with ongoing questions about the origins and nature of neural activity and consciousness.

Brain as a mysterious, multi-level system

  • The brain is unlike other organs in the body in its complexity and integration of electrical and biochemical processes.

  • It involves neural impulses (electrical signaling) and a broad biochemical information transduction system that underpins thought, language, memory, planning, and perception.

  • Electromagnetic signaling is fast, but actual information transmission involves a transfer from electrical signals to chemical signals at synapses, which introduces delays and complexity.

  • We understand macro-level brain function (regions, lobes, networks) and micro-level processes (neurons, synapses, neurotransmitters), but many questions remain about the exact mechanisms governing cognition.

Lenses for studying the brain (macro vs micro)

  • Macro lens: How well does a brain run as a system? E.g., overall speed, capacity, and functional outcomes (e.g., gaming, internet access, general processing speed).

  • Mid-level lens: What region (lobe) is involved in a decision or processing task? Which brain areas light up during a task?

  • Micro lens: What neural impulses, neurotransmitters, and cellular interactions underlie processing? What are the properties of specific cells and synapses?

  • The same issue can be analyzed with different lenses, leading to complementary insights.

Neurons and neural networks: building blocks of cognition

  • Neuron: the fundamental signaling unit of the brain. Neurons form networks that specialize in tasks and fire in parallel or sequential patterns to produce cognition.

  • Early view: neurons were seen as a single interconnected web; later, technologies revealed individual cells and gaps between them (synapses).

  • Golgi staining revealed that neurons are discrete cells that interconnect with spaces (synapses) rather than forming a continuous mesh.

  • Neuronal networks: signals travel across connected neurons via dendrites → cell body (soma) → axon → axon terminals; neurotransmitters cross the synapse to reach receptors on the next neuron.

  • Typical neuron components:

    • Soma (cell body): contains nucleus and protein production machinery.

    • Dendrites: receive inputs from other neurons (Greek for “tree”).

    • Axon: long fiber transmitting the nerve impulse.

    • Myelin sheath: fatty insulation around the axon that speeds transmission.

    • Axon terminals: release neurotransmitters into the synapse.

  • Some cells have receptors on their membranes (e.g., touch receptors) and may fire directly when stimulated.

Neuron signaling and the action potential

  • Transduction: the transformation of energy forms (e.g., light photons) into chemical signals and then into electrical signals, progressively enabling neural communication.

  • Resting potential: neurons have a baseline electrical potential, typically around V_{rest} \approx -70\ \text{mV}.

  • Threshold and all-or-none firing: when inputs reach a sufficient threshold, the neuron fires an action potential; firing is all-or-none and of a fixed amplitude.

  • Phases of the action potential:

    • Depolarization: membrane potential becomes more positive.

    • Repolarization: returns toward the resting potential, often overshooting toward hyperpolarization.

    • Hyperpolarization: membrane potential becomes more negative than the resting potential.

    • Refractory period: a brief period after firing when the neuron is less excitable, limiting the rate of firing.

  • Saltatory conduction: myelinated axons speed up signal transmission; unmyelinated conduction is slower.

    • Unmyelinated speed: roughly v\approx 10\ \text{m/s}.

    • Myelinated speed: roughly v\ge 100\ \text{m/s}.

  • Despite electrical signaling, each neuron’s signal often ends at the synapse where neurotransmitters are released and must be received by downstream receptors before the signal can continue, keeping the transmission process inherently chemical and not instantaneous.

  • Relative speed of neural information: even with myelination, neural information is roughly about 3\times 10^{6} times slower than the speed of electricity in a wire, due to chemical synaptic transmission and slow diffusion processes.

  • Plasticity and redundancy: many neurons fire in parallel; even if some neurons are in a refractory state, others can drive the network, contributing to robust, parallel processing.

Glial cells: the other half of the brain’s support system

  • Glial cells provide structural support, nutrition, and protection, and play active roles in signaling and homeostasis.

  • Major types:

    • Microglia: cleanup cells that remove dead neurons and debris; involved in diagnosing injuries (e.g., stroke) via their accumulation.

    • Oligodendrocytes: insulate axons with myelin, speeding signal transmission.

    • Astrocytes: regulate the chemical environment, support the blood-brain barrier (BBB), and coordinate neuronal signaling; help maintain the brain’s chemical pool.

  • Cerebrospinal fluid (CSF): glial activity helps create and regulate CSF, which the brain effectively floats in.

  • Blood-brain barrier (BBB): glial and other supporting cells help form a barrier protecting brain tissue from foreign substances.

  • Glial-to-neuron ratio: historically thought to be roughly 10:1, though imaging suggests variability across individuals.

  • Clinical correlations: damaged or dysfunctional glia are linked to diseases like Alzheimer's, depression, and schizophrenia; healthier glial function supports neural health and cognitive potential.

  • Einstein note: postmortem studies suggested higher glial density in Einstein’s brain, hypothesized to relate to cognitive prowess, though this remains correlational and not causal.

Neural coding: how features and information are represented

  • Specificity vs population coding:

    • Specificity coding (one neuron per stimulus) is an oversimplified view; real brains use distributed representations.

    • Jennifer Aniston neuron: a neuron that fired selectively to Jennifer Aniston’s image, illustrating the idea of highly selective coding in some contexts.

    • Similar demonstrations extended to other famous stimuli (e.g., Star Wars); suggests conceptual rather than purely stimulus-specific coding.

  • Population coding: information is represented by patterns across many neurons; broader, more robust representations emerge from distributed activity across occipital or other cortices.

  • Sparse coding: a small subset of neurons is active for a given stimulus, yet the pattern across a region uniquely identifies the stimulus;

    • Different patterns can encode similar stimuli; memory recall can activate similar patterns without current perception (recall vs perception).

  • Visual cortex research (Hubel & Wiesel-inspired): orientation-selective cells discovered in the visual cortex; responses are tuned to specific orientations (e.g., vertical vs horizontal) in animals like cats; orientation-specific neurons become active as a stimulus approaches a preferred angle.

  • Concept of plasticity: brain networks rewire based on experience; “neurons that fire together wire together” (Hebb’s rule) as a mechanism for learning and network formation.

Developmental plasticity and experiential shaping

  • Early-life plasticity demonstrates the brain’s responsiveness to environmental input.

    • Blakemore & Cooper kitten studies: rearing kittens in environments with only vertical or horizontal lines leads to heightened sensitivity to the exposed orientation and reduced sensitivity to others; demonstrates environment-driven specialization.

  • Monkeys studied by Gross and colleagues extended orientation exploration to shapes (circles, squares, hexagons, etc.), revealing specialized processing areas and the limits of early assumptions about simple orientation maps.

  • The hand area discovery: incidental observation revealed a brain region highly responsive to hand stimuli, illustrating how functional specialization can be uncovered via serendipitous experimentation.

Language and higher cognitive functions: distributed networks

  • Language processing involves multiple parallel pathways for production and comprehension, including dorsal and ventral streams that operate together.

  • Functional networks underpinning cognition: the brain can be viewed as a giant network (connectome) with hubs and pathways.

  • The connectome concept: structural connections (anatomical wiring) define how regions communicate; signaling follows shortest or most efficient routes via hubs and pathways.

  • Corpus callosum: major commissural tract connecting the two hemispheres; not essential for survival but facilitates interhemispheric communication; surgical sectioning can reduce severe seizures by limiting abnormal cross-hemisphere spread.

  • Functional connectivity vs structural connectivity: functional connectivity concerns correlations in activity across regions during tasks; structural connectivity concerns physical connections (axons, tracts) that enable communication.

  • Six major connectivity networks implicated in task performance:

    • Visual network

    • Somato-motor network

    • Dorsal attention network

    • Executive control network

    • Salience network

    • Default mode network

  • When a task engages a network, it demands more blood flow to that region, increasing the radiological signal in imaging studies (e.g., fMRI) and indicating regional involvement.

  • This multi-network activation explains how complex behaviors recruit widespread, interconnected brain regions rather than isolated, single-area centers.

Imaging and evidence: linking brain activity to function

  • Imaging tools and basic principles:

    • EEG (electroencephalography): scalp electrodes measure electrical activity and infer active regions during tasks.

    • fMRI (functional MRI): detects blood flow/oxygenation changes as a proxy for neural activity; stronger signal indicates greater activity in a region.

    • Some notes on imaging claims: correlations do not imply causation; active regions during a task indicate involvement but not necessarily primary cause.

  • Classical findings linking brain regions to functions:

    • Broca’s area: language production

    • Wernicke’s area: language comprehension

    • Fusiform face area (in the fusiform gyrus): face recognition; prosopagnosia can occur with damage here, causing recognition difficulties despite seeing facial features.

  • Facial recognition and case studies:

    • Prosopagnosia: difficulty recognizing faces while still able to recognize features; exemplar case of brain-behavior dissociation.

    • Phineas Gage literature referenced as a classic example of how lesions alter personality and behavior, illustrating brain-behavior links.

Brain anatomy: structure and organization

  • Gyri and sulci: the brain’s folded surface creates bumps (gyri) and grooves (sulci); these folds increase surface area and host diverse neural circuits.

  • The corpus callosum and interhemispheric communication: a crucial pathway for cross-hemispheric signaling; its integrity influences coordinated bilateral processing.

  • Functional vs structural mapping:

    • Structural: physical wiring and anatomical regions (e.g., Broca’s/Wernicke’s areas, fusiform gyrus).

    • Functional: regions that co-activate during tasks; dynamic connectivity patterns.

The brain as a network: hubs, pathways, and redundancy

  • Conceptualizing the brain as a network helps explain resilience and complexity:

    • Hubs function like major cities—high connectivity nodes that coordinate information flow.

    • Pathways are like highways—routes along which information travels to connect different regions.

    • Redundancy ensures robustness: multiple pathways can support the same function, contributing to seamless conscious experience even if some routes are compromised.

  • The connectome serves as a map of these connections, guiding our understanding of how networks underlie cognition and behavior.

Key takeaways and implications

  • The brain operates on multiple levels simultaneously: molecular signaling, cellular networks, regional specialization, and large-scale networks all contribute to cognition.

  • There is no single “neural center” for complex tasks; rather, distributed and dynamic networks coordinate perception, action, and thought.

  • Plasticity demonstrates the brain’s adaptability to environment and experience; early experiences can shape perceptual and cognitive development.

  • Imaging and electrophysiological techniques provide powerful insights but must be interpreted with caution due to correlational limitations and the complexity of brain networks.

  • Practical/ethical implications: understanding brain networks informs treatment approaches (e.g., epilepsy surgery, neurodegenerative disease management) and raises questions about privacy, cognitive enhancement, and the representation of personhood in neural terms.

Connections to foundational principles and real-world relevance

  • Foundational concepts: neurons as signaling units, synaptic transmission, plasticity, functional specialization, and the connectome align with core neuroscience and psychology principles.

  • Real-world relevance: knowledge of brain networks guides clinical diagnoses (e.g., stroke, epilepsy), informs rehabilitation strategies, and underpins educational approaches to learning and perception.

  • Ethical considerations: advances in imaging and neural manipulation raise questions about consent, cognitive enhancement, and the limits of neuroscience in explaining identity and behavior.

Quick glossary and key terms

  • Resting potential: V_{rest} \,=\, -70\ \text{mV}

  • Action potential: the all-or-none electrical impulse that travels along the axon; involves depolarization, repolarization, and sometimes hyperpolarization.

  • Myelin sheath: lipid-rich layer that speeds conduction; increases signal velocity to approximately v\ge 100\ \text{m/s} for myelinated fibers.

  • Synapse: the junction between neurons where neurotransmitters are released and received.

  • Glial cells: non-neuronal cells supporting neurons; include microglia, oligodendrocytes, astrocytes; contribute to CSF, BBB, nutrition, and repair.

  • BBB (blood-brain barrier): protects the brain from potentially harmful substances.

  • Transduction: conversion of one form of energy to another (e.g., light to chemical signal to electrical signal).

  • Plasticity: the brain’s ability to reorganize itself by forming new neural connections.

  • Population coding: representation of information by patterns of activity across a population of neurons.

  • Sparse coding: selective activation of a small subset of neurons to represent information.

  • Connectome: comprehensive map of neural connections in the brain.

  • Six functional networks: Visual, Somato-motor, Dorsal attention, Executive control, Salience, Default mode.

Prepared for review and study tips

  • Relate macro-level network concepts to micro-level cellular processes (e.g., how synapses and glia support network dynamics).

  • Use real-world cases (prosopagnosia, epilepsy surgery, mirror neuron concepts) to anchor abstract ideas.

  • Compare different coding schemes (specificity vs population vs sparse coding) to understand how the brain represents information.

  • Distinguish correlation from causation in imaging data and consider how converging evidence strengthens inferences about brain function.

  • Reflect on ethical implications of neuroscience research as you study brain networks and their applications.

Next class teaser

  • Perception: how sensory input becomes interpreted experience, and how neural networks transform raw data into meaningful representations.