Content deleted Content added
→The binary space N: Fixed typo (form --> from) |
m linking |
||
Line 75:
The relative importance of a synapse to the firing of neuron is called ''synaptic weight'' (or ''input coefficient''). There are two kinds of synapses: [[excitatory]] that trigger neuron to ''fire'' and [[inhibitory]] that hinder firing. The neuron is either excitatory or inhibitory according to the kinds of synapses its axon makes.<ref>Kandel, Eric R., James H. Schwartz, and Thomas M. Jessell, eds. Principles of neural science. Vol. 4. New York: McGraw-Hill, 2000.</ref>
A neuron fires when the sum of inputs exceed a specific ''threshold''. The higher the threshold the more important it is that excitatory synapses have input while inhibitory ones do not.<ref>Eccles, John G. [https://link.springer.com/chapter/10.1007/978-1-4684-6817-5_9 "Under the Spell of the Synapse."] The Neurosciences: Paths of Discovery, I. Birkhäuser Boston, 1992. 159-179.</ref> Whether a recovered neuron actually fires depends on whether it received sufficient excitatory input (beyond the threshold) and not too much of inhibitory input within a certain period.
The formal model of neuron makes further simplifying assumptions.<ref>{{cite journal|doi=10.1007/bf02478259|title=A logical calculus of the ideas immanent in nervous activity|journal=Bulletin of Mathematical Biophysics|volume=5|issue=4|pages=115–133|year=1943|last1=McCulloch|first1=Warren S.|last2=Pitts|first2=Walter}}</ref> An ''n''-input neuron is modeled by a ''linear threshold function'' <math>F: \{0,1\}^n -> \{0,1\}</math> as follows :
Line 263:
==Implementation==
* C Binary Vector Symbols (CBVS): includes SDM implementation in [[C (programming language)|C]] as a part of [[vector symbolic architecture]]<ref>{{cite journal|doi=10.1016/j.bica.2014.11.015|title=Vector space architecture for emergent interoperability of systems by learning from demonstration|journal=Biologically Inspired Cognitive Architectures|volume=11|pages=53–64|year=2015|last1=Emruli|first1=Blerim|last2=Sandin|first2=Fredrik|last3=Delsing|first3=Jerker}}</ref> developed by EISLAB at [[Luleå University of Technology]]: http://pendicular.net/cbvs.php<ref>{{cite journal | last1 = Emruli | first1 = Blerim | last2 = Sandin | first2 = Fredrik | year = 2014 | title = Analogical mapping with sparse distributed memory: A simple model that learns to generalize from examples | url = http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-14994| journal = Cognitive Computation | volume = 6 | issue = 1| pages = 74–88 | doi=10.1007/s12559-013-9206-3| s2cid = 12139021 }}</ref>
* CommonSense ToolKit (CSTK) for realtime sensor data processing developed at the [[Lancaster University]] includes implementation of SDM in [[C++]]: http://cstk.sourceforge.net/<ref>Berchtold, Martin. [https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.421.768&rep=rep1&type=pdf "Processing Sensor Data with the Common Sense Toolkit (CSTK)."] *(2005).</ref>
*[[Julia (programming language)|Julia]] implementation by [[Brian Hayes (scientist)|Brian Hayes]]: https://github.com/bit-player/sdm-julia <ref>The Mind Wanders by B. Hayes, 2018. url: http://bit-player.org/2018/the-mind-wanders</ref>
* [[LIDA (cognitive architecture)|Learning Intelligent Distribution Agent (LIDA)]] developed by [[Stan Franklin]]'s lab at the [[University of Memphis]] includes implementation of SDM in [[Java (programming language)|Java]]: http://ccrg.cs.memphis.edu/framework.html
| |||