The journal of the Bishop of Montreal : during a visit to the Church Missionary Society's north-west America mission; to which is prefixed, by the secretaries, an introduction, giving an account of the formation of the mission and its progress to August 1848.

http://www.archive.org/details/journalofthebish00mounuoft

The journal of the Bishop of Montreal the Church Missionary Society's North-west America Mission. To which is added, by the secretaries, an appendix, giving an account of the formation of the mission, and its progress to the present time.

http://www.archive.org/details/bishopofmontreal00mounuoft

Christian missions : their agents and their results (vol. 1) / by T.W.M. Marshall.

http://books.google.com/books?vid=ISBN0665456816&id=sipohllLjKQC&dq=protestant+missions&a_sbrr=1 View book via Google

Christian missions : their agents and their results (vol. 2) / by T.W.M. Marshall.

http://books.google.com/books?vid=ISBN0665456816&id=sipohllLjKQC&dq=protestant+missions&a_sbrr=1 View book via Google

Orientational Harmonic Model for Rotation, Translation, and Scale Invariant Pattern Representation in Biological Vision

The proposed model, called the combinatorial and competitive spatio-temporal memory or CCSTM, provides an elegant solution to the general problem of having to store and recall spatio-temporal patterns in which states or sequences of states can recur in various contexts. For example, fig. 1 shows two state sequences that have a common subsequence, C and D. The CCSTM assumes that any state has a distributed representation as a collection of features. Each feature has an associated competitive module (CM) containing K cells. On any given occurrence of a particular feature, A, exactly one of the cells in CMA will be chosen to represent it. It is the particular set of cells active on the previous time step that determines which cells are chosen to represent instances of their associated features on the current time step. If we assume that typically S features are active in any state then any state has K^S different neural representations. This huge space of possible neural representations of any state is what underlies the model's ability to store and recall numerous context-sensitive state sequences. The purpose of this paper is simply to describe this mechanism.