Computational Neuroscience

6.

6.1.

What is a Neural Code

6.1.1.

Definition and significance

6.1.1.1.

Information representation

6.1.1.2.

Neural computation

6.1.2.

Types of neural codes

6.1.2.1.

6.1.2.2.

6.1.2.3.

Population codes

6.1.2.4.

6.1.3.

Coding principles

6.1.3.1.

6.1.3.2.

6.1.3.3.

6.2.

6.2.1.

Firing Rate as Information Carrier

6.2.1.1.

Spike count codes

6.2.1.2.

Time window selection

6.2.1.3.

Rate estimation methods

6.2.2.

6.2.2.1.

6.2.2.1.1.

Orientation tuning

6.2.2.1.2.

Direction tuning

6.2.2.1.3.

Frequency tuning

6.2.2.2.

6.2.2.2.1.

Movement direction

6.2.2.2.2.

6.2.2.3.

Tuning curve shapes

6.2.2.3.1.

Gaussian tuning

6.2.2.3.2.

6.2.2.3.3.

Sigmoidal tuning

6.2.3.

Limitations of rate coding

6.2.3.1.

Temporal resolution

6.2.3.2.

Information loss

6.2.3.3.

Metabolic costs

6.3.

Temporal Coding

6.3.1.

6.3.1.1.

6.3.1.2.

Timing reliability

6.3.1.3.

Temporal precision limits

6.3.2.

6.3.2.1.

Spike synchronization

6.3.2.2.

Correlation strength

6.3.2.3.

Synchrony detection

6.3.3.

Phase-of-Firing Codes

6.3.3.1.

Oscillatory phase

6.3.3.2.

Phase precession

6.3.3.3.

Phase coding capacity

6.3.4.

Temporal precision

6.3.4.1.

Millisecond precision

6.3.4.2.

Jitter analysis

6.3.4.3.

Information content

6.3.5.

6.3.5.1.

Burst detection

6.3.5.2.

Burst information

6.3.5.3.

Tonic vs burst modes

6.4.

Population Coding

6.4.1.

Encoding by Ensembles

6.4.1.1.

Distributed representation

6.4.1.2.

Population responses

6.4.1.3.

Ensemble dynamics

6.4.2.

Population Vectors

6.4.2.1.

Vector summation

6.4.2.2.

Preferred directions

6.4.2.3.

Decoding accuracy

6.4.3.

6.4.3.1.

Sparsity measures

6.4.3.2.

Lifetime sparsity

6.4.3.3.

Population sparsity

6.4.4.

6.4.4.1.

Distributed activity

6.4.4.2.

Redundant coding

6.4.4.3.

Noise tolerance

6.4.5.

Redundancy and decorrelation

6.4.5.1.

Information redundancy

6.4.5.2.

Noise correlations

6.4.5.3.

Efficient coding

6.5.

Decoding Neural Activity

6.5.1.

Spike-Triggered Average

6.5.1.1.

Reverse correlation

6.5.1.2.

Receptive field mapping

6.5.1.3.

6.5.2.

Bayesian Decoding

6.5.2.1.

Posterior probability

6.5.2.2.

Prior knowledge

6.5.2.3.

Likelihood functions

6.5.3.

Linear Classifiers

6.5.3.1.

Support vector machines

6.5.3.2.

Linear discriminant analysis

6.5.3.3.

Perceptron algorithm

6.5.4.

Maximum likelihood decoding

6.5.4.1.

6.5.4.2.

Decoding accuracy

6.5.4.3.

Confidence intervals

6.5.5.

Neural decoding applications

6.5.5.1.

Brain-machine interfaces

6.5.5.2.

Prosthetic control

6.5.5.3.

Cognitive state decoding

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7. Modeling Neural Networks