?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Learning+attractors+in+an+asynchronous%2C+stochastic+electronic+neural+network&rft.creator=Del+Giudice%2C+P.&rft.creator=Fusi%2C+S.&rft.creator=Badoni%2C+D.&rft.creator=Dante%2C+V.&rft.creator=Amit%2C+Daniel+J.&rft.subject=Computational+Neuroscience&rft.subject=Machine+Learning&rft.subject=Neural+Modelling&rft.description=LANN27+is+an+electronic+device+implementing+in+discrete+electronics+a+fully+connected+(full+feedback)+network+of+27+neurons+and+351+plastic+synapses+with+stochastic+Hebbian+learning.+Both+neurons+and+synapses+are+dynamic+elements%2C+with+two+time+constants+-+fast+for+neurons+and+slow+for+synapses.+Learning%2C+synaptic+dynamics%2C+is+analogue+and+is+driven+in+a+Hebbian+way+by+neural+activities.+Long-term+memorization+takes+place+on+a+discrete+set+of+synaptic+efficacies+and+is+effected+in+a+stochastic+manner.+The+intense+feedback+between+the+nonlinear+neural+elements%2C+via+the+learned+synaptic+structure%2C+creates+in+an+organic+way+a+set+of+attractors+for+the+collective+retrieval+dynamics+of+the+neural+system%2C+akin+to+Hebbian+learned+reverberations.+The+resulting+structure+of+the+attractors+is+a+record+of+the+large-scale+statistics+in+the+uncontrolled%2C+incoming+flow+of+stimuli.+As+the+statistics+in+the+stimulus+flow+changes+significantly%2C+the+attractors+slowly+follow+it+and+the+network+behaves+as+a+palimpsest+-+old+is+gradually+replaced+by+new.+Moreover%2C+the+slow+learning+creates+attractors+which+render+the+network+a+prototype+extractor%3A+entire+clouds+of+stimuli%2C+noisy+versions+of+a+prototype%2C+used+in+training%2C+all+retrieve+the+attractor+corresponding+to+the+prototype+upon+retrieval.+Here+we+describe+the+process+of+studying+the+collective+dynamics+of+the+network%2C+before%2C+during+and+following+learning%2C+which+is+rendered+complex+by+the+richness+of+the+possible+stimulus+streams+and+the+large+dimensionality+of+the+space+of+states+of+the+network.+We+propose+sampling+techniques+and+modes+of+representation+for+the+outcome.&rft.date=1998&rft.type=Journal+(Paginated)&rft.type=PeerReviewed&rft.format=application%2Fpostscript&rft.identifier=http%3A%2F%2Fcogprints.org%2F62%2F2%2Flann27.ps&rft.identifier=++Del+Giudice%2C+P.+and+Fusi%2C+S.+and+Badoni%2C+D.+and+Dante%2C+V.+and+Amit%2C+Daniel+J.++(1998)+Learning+attractors+in+an+asynchronous%2C+stochastic+electronic+neural+network.++%5BJournal+(Paginated)%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F62%2F