Sparse distributed memory: Difference between revisions

Revision as of 21:26, 2 May 2022

Revision as of 21:26, 2 May 2022

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Some progress has been made in 2014 by [[Gero Miesenböck]]'s lab at the [[University of Oxford]] analyzing [[Drosophila]] [[Olfactory system]].<ref>A sparse memory is a precise memory. Oxford Science blog. 28 Feb 2014. http://www.ox.ac.uk/news/science-blog/sparse-memory-precise-memory</ref>

In Drosophila, sparse odor coding by the [[Kenyon cell]]s of the [[Mushroom bodies|mushroom body]] is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. Lin et al.<ref>{{cite journal | last1 = Lin | first1 = Andrew C. |display-authors=etal | year = 2014 | title = Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination | journal = Nature Neuroscience | volume = 17 | issue = 4| pages = 559–568 | pmc=4000970 | pmid=24561998 | doi=10.1038/nn.3660}}</ref> demonstrated that sparseness is controlled by a negative feedback circuit between Kenyon cells and the [[GABAergic]] anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit show that Kenyon cells activate APL and APL inhibits Kenyon cells. Disrupting the Kenyon cell-APL feedback loop decreases the sparseness of Kenyon cell odor responses, increases inter-odor correlations, and prevents flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor-specificity of memories. A 2017 publication in [[Science_(journal)|Science]]<ref>{{cite journal|doi=10.1126/science.aam9868|pmid=29123069|title=A neural algorithm for a fundamental computing problem|journal=Science|volume=358|issue=6364|pages=793–796|year=2017|last1=Dasgupta|first1=Sanjoy|last2=Stevens|first2=Charles F.|last3=Navlakha|first3=Saket|bibcode=2017Sci...358..793D|doi-access=free}}</ref> showed that fly olfactory circuit implements an improved version of binary [[locality sensitive hashing]] via sparse, random projections.

==Quantum-mechanical interpretation==

[[Quantum superposition]] states that any physical system simultaneously exists in all of its possible [[quantum state|states]], the number of which is exponential in the number of entities composing the system. The strength of presence of each possible state in the superposition{{snd}} i.e., the probability with which it would be observed if measured{{snd}} is represented by its [[probability amplitude]] coefficient. The assumption that these coefficients must be represented physically disjointly from each other, i.e., locally, is nearly universal in the [[quantum mechanics|quantum theory]]/[[quantum computing]] literature.

Specifically, If we consider an SDR model in which the overall population consists of Q clusters, each having K binary units, so that each coefficient is represented by a set of Q units, one per cluster. We can then consider the particular world state, X, whose coefficient's representation, R(X), is the set of Q units active at time t to have the maximal probability and the probabilities of all other states, Y, to correspond to the size of the intersection of R(Y) and R(X). Thus, R(X) simultaneously serves both as the representation of the particular state, X, and as a probability distribution over all states. When any given code, e.g., R(A), is active, all other codes stored in the model are also physically active in proportion to their intersection with R(A). Thus, SDR provides a classical realization of quantum superposition in which probability amplitudes are represented directly and implicitly by sizes of [[set intersection]]s. If algorithms exist for which the time it takes to store (learn) new representations and to find the closest-matching stored representation ([[Bayesian inference|probabilistic inference]]) remains constant as additional representations are stored, this would meet the criterion of [[quantum computing]].{{cn}} (Also see [[Quantum cognition]] and [[Quantum neural network#Quantum associative memory|Quantum associative memory]])

==Applications==