Visual language recognition with a feed-forward network of spiking neurons
- Los Alamos National Laboratory
- GALOIS
- INDIANA UNIV.
An analogy is made and exploited between the recognition of visual objects and language parsing. A subset of regular languages is used to define a one-dimensional 'visual' language, in which the words are translational and scale invariant. This allows an exploration of the viewpoint invariant languages that can be solved by a network of concurrent, hierarchically connected processors. A language family is defined that is hierarchically tiling system recognizable (HREC). As inspired by nature, an algorithm is presented that constructs a cellular automaton that recognizes strings from a language in the HREC family. It is demonstrated how a language recognizer can be implemented from the cellular automaton using a feed-forward network of spiking neurons. This parser recognizes fixed-length strings from the language in parallel and as the computation is pipelined, a new string can be parsed in each new interval of time. The analogy with formal language theory allows inferences to be drawn regarding what class of objects can be recognized by visual cortex operating in purely feed-forward fashion and what class of objects requires a more complicated network architecture.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1024382
- Report Number(s):
- LA-UR-10-02784; LA-UR-10-2784; TRN: US201119%%344
- Resource Relation:
- Conference: IADIS Multi Conference on Computer Science and Information Systems ; July 29, 2010 ; Freiburg, Germany
- Country of Publication:
- United States
- Language:
- English
Similar Records
Learning in structured connectionist networks. Technical report
Picture analysis by graph transformation