{"id":"https://openalex.org/W7224995448","doi":"https://doi.org/10.5281/zenodo.23835818","title":"Engineer's View of the Brain: From Noisy Inputs to Internal Activation Perturbation","display_name":"Engineer's View of the Brain: From Noisy Inputs to Internal Activation Perturbation","publication_year":2026,"publication_date":"2026-10-31","ids":{"openalex":"https://openalex.org/W7224995448","doi":"https://doi.org/10.5281/zenodo.23835818"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.23835818","doi":"https://doi.org/10.5281/zenodo.23835818","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835818","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.23835818","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160245842","display_name":"Emmanuel Watila","orcid":"https://orcid.org/0009-0009-3445-1620","observed_orcids":["https://orcid.org/0009-0009-3445-1620"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Watila, Emmanuel","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0009-3445-1620","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5160245842"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2705000042915344,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2705000042915344,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.24629999697208405,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.09860000014305115,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/internal-representations","display_name":"internal representations","score":0.3240000009536743},{"id":"https://openalex.org/keywords/pretrained-neural-networks","display_name":"pre-trained neural networks","score":0.5730000138282776},{"id":"https://openalex.org/keywords/input-disturbances","display_name":"input perturbation","score":0.6309999823570251},{"id":"https://openalex.org/keywords/language-model-generation","display_name":"language model generation","score":0.48100000619888306},{"id":"https://openalex.org/keywords/image-representation","display_name":"image representation","score":0.45399999618530273},{"id":"https://openalex.org/keywords/dinov2","display_name":"DINOv2","score":0.8820000290870667},{"id":"https://openalex.org/keywords/smollm2","display_name":"SmolLM2","score":0.777999997138977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5059000253677368},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48829999566078186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46549999713897705},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4219000041484833},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.4009999930858612},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39579999446868896},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.38449999690055847},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3133000135421753},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29899999499320984},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.28769999742507935},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.2766999900341034},{"id":"https://openalex.org/C2984485829","wikidata":"https://www.wikidata.org/wiki/Q100766421","display_name":"Noise level","level":3,"score":0.27619999647140503},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.2752000093460083},{"id":"https://openalex.org/C77265313","wikidata":"https://www.wikidata.org/wiki/Q879844","display_name":"Rest (music)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C120515352","wikidata":"https://www.wikidata.org/wiki/Q2564580","display_name":"Image plane","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.23835818","doi":"https://doi.org/10.5281/zenodo.23835818","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835818","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.23835819","doi":"https://doi.org/10.5281/zenodo.23835819","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835819","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.23835818","doi":"https://doi.org/10.5281/zenodo.23835818","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835818","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2906579788","https://openalex.org/W7155039456","https://openalex.org/W6941501955","https://openalex.org/W6944262871","https://openalex.org/W7196949365","https://openalex.org/W7161575685","https://openalex.org/W2906226166","https://openalex.org/W420297899","https://openalex.org/W3196681588","https://openalex.org/W6903455201"],"abstract_inverted_index":{"Version":[0],"1":[1],"(exploratory":[2],"preprint).":[3],"Revisions":[4],"in":[5,23,29,185,200],"progress.":[6],"Summary":[7],"When":[8],"a":[9,12,90,97,100,148,191,205],"system":[10],"produces":[11],"different":[13,133],"output":[14],"than":[15,278],"the":[16,20,24,32,123,128,137,141,156,163,165,169,181,216,219,281,304],"one":[17,230,236],"intended,":[18],"does":[19],"cause":[21],"lie":[22],"information":[25],"it":[26,199],"receives,":[27],"or":[28,248,274],"disturbances":[30],"inside":[31,136],"system?":[33],"This":[34],"paper":[35],"explores":[36],"that":[37],"question":[38],"with":[39,229],"two":[40],"small":[41,149,237],"experiments":[42],"on":[43,162,268,299],"pretrained":[44],"neural":[45],"networks.":[46],"It":[47,239],"starts":[48],"from":[49],"an":[50,83,226],"everyday":[51],"slip":[52],"(\u201cI\u2019d":[53],"like":[54,174],"to":[55,78,110,115,122,147,175,302],"park":[56,176],"my":[57],"cat\u201d":[58],"instead":[59],"of":[60,187,202,218,263],"\u201ccar\u201d)":[61],"and":[62,76,80,89,99,127,144,168,235,250,259],"asks":[63],"what":[64],"can":[65],"be":[66],"measured.":[67,255],"What":[68,221],"was":[69],"done":[70],"Noisy":[71],"inputs.":[72],"I":[73,139],"added":[74,145],"noise":[75,146,166,193],"blur":[77],"images":[79],"tracked":[81],"how":[82],"image":[84,102,119,231],"model\u2019s":[85,151],"internal":[86,152],"representation":[87],"(DINOv2)":[88],"separate":[91],"classifier\u2019s":[92],"predictions":[93],"changed.":[94],"Blurring":[95],"both":[96],"cat":[98],"car":[101],"brought":[103],"their":[104],"representations":[105],"closer":[106],"together":[107],"(distance":[108],"66.8":[109],"36.4;":[111],"cosine":[112],"similarity":[113],"0.02":[114],"0.69).":[116],"Neither":[117],"blurred":[118],"came":[120],"close":[121],"other\u2019s":[124],"original":[125,182],"representation,":[126],"classifier":[129],"still":[130],"gave":[131],"them":[132],"labels.":[134],"Noise":[135],"model.":[138,238],"kept":[140,180],"input":[142],"fixed":[143],"language":[150],"activations":[153],"(SmolLM2-135M).":[154],"Whether":[155],"predicted":[157],"next":[158],"word":[159],"changed":[160,198],"depended":[161],"layer,":[164],"size,":[167],"prompt.":[170],"For":[171],"\u201cI":[172],"would":[173],"my\u201d,":[177],"layer":[178,196],"29":[179],"prediction":[183,288],"(\u201ccar\u201d)":[184],"930":[186],"1,000":[188],"trials":[189,203],"at":[190,204,284],"moderate":[192],"level,":[194],"while":[195],"28":[197],"96.8%":[201],"higher":[206],"level.":[207],"A":[208,261,287],"one-step":[209],"disturbance":[210],"during":[211],"text":[212],"generation":[213,282],"also":[214],"redirected":[215],"rest":[217],"sentence.":[220],"this":[222],"is":[223,225],"notThis":[224],"exploratory":[227],"study":[228],"pair,":[232],"three":[233],"prompts,":[234],"makes":[240],"no":[241,251],"claim":[242],"about":[243],"human":[244],"speech":[245],"errors,":[246],"stress,":[247],"exhaustion,":[249],"brain":[252],"data":[253],"were":[254],"What\u2019s":[256],"nextMore":[257],"prompts":[258],"models.":[260],"test":[262,303],"whether":[264],"noise-induced":[265],"errors":[266],"land":[267],"related":[269],"words":[270],"(such":[271],"as":[272],"truck":[273],"van)":[275],"more":[276],"often":[277],"chance.":[279],"Repeating":[280],"experiment":[283],"several":[285],"points.":[286],"written":[289],"down":[290],"before":[291],"running":[292],"each":[293],"new":[294],"test.":[295],"Feedback":[296],"welcome,":[297],"especially":[298],"better":[300],"ways":[301],"related-words":[305],"question:":[306],"hi@devsgnr.xyz":[307],"Code:":[308],"Input-perturbation":[309],"Notebook":[310,313],"\u00b7":[311],"System-perturbation":[312]},"counts_by_year":[],"updated_date":"2026-11-02T09:45:03.842371","created_date":"2026-11-01T00:00:00"}