Cognitive Neuropsychology

Recommended Prerequisites

Elementary knowledge of Neuropsychology, Cognitive Experimental Psychology, Linear Algebra, Mathematical Analysis and Neuron Biology.

Teaching Methods

Lecturing; presentation and discussion of individual work assignment regarding the analysis of electroencephalographic data using sLORETA algorithm and the synthesis of scientific papers related to the syllabus contents.

Learning Outcomes

Objectives:

Knowing the complementarities and the specificities of cognitive experimental neuropsychology and clinical neuropsychology.

Knowing main data concerning the relationships between brain structures and functional modules of attention, language and episodic memory.

Knowing main data related to temporal dynamics and brain activity markers.

Knowing basic models of neural networks that can be applied to the modeling of cognitive processes.

 

Competencies:

Be able to articulate data from cognitive experimental neuropsychology and clinical neuropsychology.

Be able to analyze knowledge upon brain structures underlying attention, language and episodic memory processes and respective brain markers.

Be able to understand the topology of simple neural networks and their effect on data and information processing.

Work Placement(s)

No

Syllabus

Cognitive Experimental and Clinical Neuropsychology: specificities and complementarities. Cognitive models and Neuropsychology: Research strategies and types of data (lesion studies, MRI, fMRI, PET, EEG and derivative techniques – ERP, sLORETA). Neuropsychology of Attention and Neuropsychology of Language.

Episodic memory system: common and distinctive characteristics with semantic memory, related brain structures, HERA model, data from functional neuroimaging studies about successful retrieval, synergistic ecphory model, encoding specificity.
Neuron model: dendrites, axons and synapses. Hebb rule. Introduction to linear Algebra (e.g., vectors inner product and cross product, length of a vector, weights of synapses). Single-layer perceptrons and respective limitations. Introduction to non-linear networks. Local and non-local learning. Neural networks models: the case of the pattern associative memory. Concepts of distributed representation, sparse and local representation.

Assessment Methods

Evaluation
two syntheses of scientific papers : 50.0%
the analysis of electroencephalographic data using sLORETA algorithm: 50.0%

Bibliography

Cannon, R.(2012)Low resolution brain electromagnetic tomography (LORETA): Basic concepts and clinical applications. Corpus Christi, TX: BMED Press LLC.

Code, C. (Ed.). (2006). The representation of language in the brain. Hove: Psychology Press.

Kolb, B., & Whishaw, I. (2003). Fundamentals of human Neuropsychology. New York: Worth Publishers.

Nyberg, L.(2008). Structural basis of episodic memory. In H. Eichenbaum (Ed.), Learning and memory: A comprehensive reference (vol. 3, pp. 99-112). San Diego, CA: Academic Press.

Rolls, E. T. (2008). Memory, attention and decision-making. Oxford: OUP.

Squire, L. R., & Schacter, D. L.(2003) (Eds.), Neuropsychology of memory (3rd edition, pp. 166-173). New York: Guilford.

Tulving, E.(2005). Episodic memory and autonoesis: Uniquely human? S. Terrace & J. Metcalfe (Eds.), The missing link in cognition (pp. 4-56). New York: OUP.

Vidyasagar,M. (2010). Learning and generalization: With applications to neural networks (2nd edition). New York: Springer.