prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: When did KSTP switch to a sports radio format?

Read the following text and answer briefly.

Context 1: KSTP (1500 AM; SKOR North) is a commercial AM radio station licensed to Saint Paul, Minnesota  It is the flagship AM radio station of Hubbard Broadcasting, which also owns several other television and radio stations across the United States   KSTP has a sports radio format and is the ESPN Radio Network affiliate for Minneapolis-St  Paul   The radio studios are  on University Avenue in Minneapolis, shared with sister stations KSTP-FM, KSTP-TV, KTMY, and KSTC-TV  On weekdays, KSTP airs local sports shows from 9 a m  to 9 p m  and carries ESPN programming weekday mornings, late nights and weekends  Some KSTP shows are simulcast on other sports radio stations in the region 

KSTP runs the maximum power for AM stations, 50,000 watts   It shares clear-channel, Class A status on 1500 AM with WFED in Washington, D C   KSTP broadcasts a directional signal at night, using a three-tower array, with its transmitter on U S  Route 61 at Beam Avenue in Maplewood   Programming is also heard on 250 watt FM translator K235BP at 94 9 MHz in Bemidji 

History

WAMD and KFOY
KSTP's start in 1928 was the product of a merger between two pioneering Twin Cities stations: WAMD ("Where All Minneapolis Dances") in Minneapolis, first licensed on February 16, 1925 to Stanley E  Hubbard, and KFOY in St  Paul, first licensed on March 12, 1924 to the Beacon Radio Service in St  Paul 

Following a few test transmissions, WAMD made its formal debut broadcast on February 22, 1925  (In later interviews Stanley Hubbard traced WAMD's start to April 1924 ) It was located at the Marigold Dance Garden, and featured nightly "Midnight Frolics" broadcasts by the ballroom's orchestra  It is claimed that WAMD was the first radio station to be completely supported by running paid advertisements  Effective June 15, 1927, WAMD was assigned to 1330 kHz 

On November 11, 1927 WAMD's transmitter site at Oxboro Heath on Lyndale Avenue South burned down, two weeks after the station had been sold to the National Battery Company  An initial arrangement was made to carry WAMD's programs over WRHM (now WWTC), transmitting on WAMD's 1330 kHz frequency  Beginning on November 24, 1927 the WAMD broadcasts, still on 1330 kHz, were shifted to KFOY's facility in St  Paul  (At this time KFOY was assigned to 1050 kHz)  The next day it was announced that National Battery had purchased KFOY, and as of December 1, 1927 both KFOY and WAMD were reassigned to 1350 kHz  WAMD continued making regular broadcasts until the end of March 1928, while KFOY, although it continued to be licensed for a few more months on a time-sharing basis with WAMD, ceased operations at this point 

National Battery Company
In mid-December 1927, the National Battery Company announced it had received permission from the Federal Radio Commission (FRC) to build a new station, with the call letters KSTP, operating from a transmitter site to be constructed three miles south of Wescott  The next month it was reported that the new station, still under construction, had been assigned to 1360 kHz  KSTP made its debut broadcast on March 29, 1928  Although technically it was a separate station from WAMD and KFOY, both of which were formally deleted on April 30, 1928, overall KSTP was treated as the direct successor to a consolidated WAMD and KFOY 

Hubbard became the merged station's general manager, acquiring controlling interest in 1941  A month after the merger, KSTP became an affiliate for the NBC Red Network   It remained with NBC for 46 years  On November 11, 1928, under the provisions of the FRC's General Order 40, KSTP was assigned to a "high-powered regional" frequency of 1460 kHz  The only other station assigned to this frequency was WTFF in Mount Vernon Hills, Virginia (later WJSV, now WFED, Washington, D C )  On February 7, 1933, the FRC authorized KSTP to increase its daytime power to 25 KW  In 1938 and 1939 KSTP also operated a high-fidelity AM "experimental audio broadcasting station" Apex station, W9XUP, originally on 25,950 kHz and later on 26,150 kHz  In 1941, as part of the implementation of the North American Regional Broadcasting Agreement, KSTP was assigned to its current "clear channel" frequency of 1500 kHz, with the provision that it and WJSV, as "Class I-B" stations, had to maintain directional antennas at night in order to mutually protect each other from interference  An FM station, KSTP-FM, was founded in 1946 but shut down in 1952 

Hubbard reportedly acquired an RCA TV camera in 1939, and started experimenting with television broadcasts   But World War II put a hold on the development of television   In 1948, with the war over, KSTP-TV became the first television station in Minnesota   With KSTP 1500 already associated with NBC Radio, KSTP-TV became an NBC Television Network affiliate   From 1946 to 1952, KSTP also had an FM counterpart   KSTP-FM 102 1 was only on the air four years   There were few radios equipped to receive FM signals in that era, and management decided to discontinue FM broadcasts 

MOR and Top 40
As network programming moved from radio to television, KSTP programmed a full service Middle of the Road (MOR) radio format, in the shadow of its chief competitor, CBS Radio affiliate 830 WCCO  In 1965, a new FM station, reviving the KSTP-FM call sign, was put on the air, largely simulcasting the AM station   But by the late 1960s, KSTP-FM began a separate format of beautiful music   KSTP was the radio home of the Minnesota Vikings football team from 1970 to 1975  

In 1973, KSTP broke away from its longtime adult MOR sound and became one of four area stations at the time to program a Top 40 format  "15 KSTP, The Music Station" competed with Top 40 AM rivals WDGY, KDWB and later, WYOO  The competition would eventually shake itself out, with outrageous rocker WYOO dropping out after being sold in 1976, and then the staid WDGY switching to country music the following year   As for uptempo hits station 15 KSTP, it went from a tight Top 40 format to leaning adult rock in 1978, to leaning adult contemporary in 1979, to evolving into adult contemporary/talk by 1980   In 1982, it officially shifted to talk   Most Top 40 rock music, by this time, had moved to the FM band 

Past Personalities

Notable hosts who have been on KSTP include John Hines, Jesse Ventura, Larry Carolla, Tom Barnard, Big Al Davis, Don Vogel, John MacDougall, Griff, Mike Edwards, Geoff Charles, Joe Soucheray, James Lileks, Leigh Kamman, Barbara Carlson, Peter Thiele, Tom Mischke, Jason Lewis, Chuck Knapp, Machine Gun Kelly, Charle Bush, Mark O'Connell and Paul Brand  These broadcasters were supported by producers such as Bruce Huff, Rob Pendleton, Alison Brown, Jean Bjorgen, David Elvin (who Vogel dubbed the "Steven Spielberg of Talk Radio"), Mitch Berg and others 

The station has, for the most part, emphasized local hosts over the years   But in 1988, KSTP was one of Rush Limbaugh's first affiliates when his conservative talk show was rolled out for national syndication   (Clear Channel-owned KTLK-FM took over rights to Limbaugh's show in January 2006)  Other syndicated hosts previously heard on KSTP include Sean Hannity, Bruce Williams, Larry King, and Owen Spann 

Sports Radio
KSTP switched to Sports Radio on February 15, 2010   As the station had to wait for ESPN's contract with rival KFAN and its sister station KFXN to expire, it did not become an ESPN Radio affiliate until April 12, the same day that the Minnesota Twins were scheduled to play the first game in their new ball park, Target Field, against the Boston Red Sox   As a result Coast to Coast AM and Live on Sunday Night, it's Bill Cunningham were retained during this period   One ESPN Radio network program, The Herd with Colin Cowherd, was picked up by KSTP immediately following the format change 

In 2018, the station was approved for an FM translator on 94 1 FM, broadcasting from a transmitter atop the IDS Center in downtown Minneapolis  The two-watt signal threw most of its power to the west, preventing interference to low powered FM stations on the same channel including WFNU-LP in St  Paul  With only two watts of power, however, the signal was limited to the immediate downtown area surrounding the IDS Center   It later acquired a 250 watt translator, K235BP at 94 9 MHz   The original translator was discontinued 

On January 15, 2019, KSTP rebranded as "SKOR North" (a reference to the Vikings team song/chant, "Skol, Vikings"), with local programming between 12 noon and 7 pm  About a year later, in May of 2020, KSTP suspended most of its local programming and laid off nearly all of its local staff  Station management cited the economic toll of the coronavirus for the changes  Sports broadcasting continues, primarily composed of ESPN radio network broadcasts 

Sports Teams

KSTP-AM served as the radio flagship for the Minnesota Vikings football team from 1970 to 1975 

On August 1, 2006, the station announced that it would be the new flagship station for the Minnesota Twins baseball team, effective with the start of the 2007 season  The Twins had been on rival WCCO since arriving in Minnesota in 1961   KSTP served as the flagship for the Twins until the end of the 2012 season, when games moved to 96 3 KTWN-FM (now KMWA)   The Twins have since returned to WCCO 830 

The switch to a fairly weak FM station caused dissent among some listeners, particularly in communities that had trouble picking up KSTP 1500   Although KSTP is the state's second most powerful AM station, it must operate directionally at night, delivering a reduced signal to parts of the market  WCCO, by comparison, offers a signal with a wider coverage area during the day than KSTP does, with WCCO's non-directional 50,000 watt signal  In response, the Twins have expanded the number of affiliates 

On March 9, 2011, KSTP announced it would be the new flagship for the University of Minnesota Golden Gophers men's and women's basketball and men's ice hockey, ending a 68-year run on WCCO  The rights have since moved to KFXN-FM, which already aired Gopher football 

On March 2, 2017, KSTP announced it would be the first radio broadcaster for Minnesota United FC   The move brings live soccer action to 1500 AM 

Previous logos

References

External links
KSTP website

FCC History Cards for KSTP (covering 1928-1980)
Radiotapes com Historic Minneapolis/St  Paul airchecks dating back to 1924 including KSTP and other Twin Cities radio stations 
Rick Burnett's TwinCitiesRadioAirchecks com has additional airchecks of KSTP and other Twin Cities radio stations from the '60s and '70s, including Chuck Knapp's 2nd show on KSTP 

Hubbard Broadcasting
ESPN Radio stations
Peabody Award winners
Radio stations in Minneapolis–Saint Paul
Radio stations established in 1925
1925 establishments in Minnesota
Minnesota Kicks
Sports radio stations in the United States
Clear-channel radio stations
Context 2: July | 2012 | Chico Taxpayers Association
Keep a Knockin’ but you can’t come in! Come back next Tuesday night and try it again! And be sure to bring plenty of your friends 
Toby Schindelbeck has finally been rewarded for his persistence – he’s been going before Chico City Council, asking that Finance MisDirector Jennifer Hennessy comply with city code and give a budget report at every meeting  City clerk Debbie Presson has informed him that this subject will be “discussed” at the August 7 council meeting 
But we know, it won’t be a very good “discussion” unless a bunch of people come in and demand some action  Toby has observed that issues like Corporate Personhood and the “single-use” plastic bag ban have drawn fairly small crowds – he estimates 25 – 30 people, and I’d say he’s being generous  The city has acted on these issues, with only that small fraction of the population in support  So, Toby believes there needs to be an even stronger presence to get a decent discussion on this matter, and I agree 
Like Toby and Stephanie Taber and others have been saying, the city code calls for a monthly budget report, with sticky details like receipts, etc, and Jennifer Hennessy admits she has not made such a report in the seven years she’s been with the city of Chico  Try not paying your taxes for seven years – you’ll get the same treatment as the man from Touch of Class Florist – 68 years old, and he’s being sent to PRISON  But Jennifer Hennessy and her boss Dave Burkland, and their overseer, Mayor Ann Schwab, get to flog the law right in front of everybody, and Ann just steps right into that little red convertible and drives off to her palatial estate in Forest Ranch 
The law is a piece of paper  It takes people to demand law enforcement  We’ve got a serious law enforcement problem in our town  The police say they aren’t paid enough to enforce the laws in the streets, and now Dave Burkland says, he just doesn’t have to 
And your mayor won’t make him either  He’s retiring, on more than $150,000 a year, for the rest of his life, but she’s up for election in November – time to take out the trash 
That meeting is scheduled for August 7, the usual time, the usual place  I’ll keep you posted 
Tags: Ann Schwab Chico CA, Ann Schwab for city council, Dave Burkand Chico Ca, Friends of Ann Schwab, Jennifer Hennessy Chico Ca
Stephanie Taber answers Quentin Colgan’s letter to the News and Review
I get complaints from friends and strangers, and it has also been my own experience, that the editor of the Chico News and Review is not always objective in deciding which letters received from the public will be printed in the paper and which ones won’t  Robert Speer has offered me excuses, but I have always found him to be disingenuous  For example – he told me he would only run letters that referenced an article or letter recently printed in the paper – untrue a million times over  He also told me he wouldn’t print letters that had already run in the Enterprise Record – also untrue a million times over  The man has his own reasons for running or not running letters 
David Little is more objective, but he’s got his faults too – once he threw out a letter from my husband and later admitted he had thought I’d written it and used my old man’s name  He just threw it out without even calling the phone number or e-mailing, just assumed I’d do something like that when I’d never done anything like that before, because he was mad at me over a snit we were having at the time 
I think Little gets his nose out at people personally, and Hell hath no fury, know what I mean? With Speer it can personal but I think it’s most often political  Suffice to say, they both carry what my dad used to call a “Shit List,” and if you’re on it, you don’t get ink in their rag 
Of course either paper is equally likely to print a total wad of lies or misinformation without so much as a google fact check  I will never forget the time Dave Little printed a letter saying the cops had been called to my house on a dog complaint  The letter writer insinuated that this was why I often wrote letters complaining about the cop contracts  I called Little and told him the letter was false, nothing like that had ever happened – but he wouldn’t retract it  I had to look the old man up in the phone book and call him myself, tell him he had been misinformed, and ask him to write a retraction  He apologized profusely and the apology was in the paper within three days  He wouldn’t tell me where he got the information, but later I found out he was a member of VIPS, and he still is  I think that’s something Dave Little could have looked into before he printed a story like that about me and my family, not to mention my dogs, but he didn’t see it that way  Poor journalism, is how I see it, and that’s what I’ve come to expect out of both the daily and the weekly 
So, pardon me if I was not surprised when my friend Stephanie mentioned to me that she didn’t think Speer would run her response to a letter from Quentin Colgan, regarding our current fiscal morass  QC made an argument he has been swinging around town lately – that Fire Station 5 had to be closed recently because the Tea Party forced the city to have a $150,000 election over Measure A 
The first problem I have with this argument is, the city is out a heck of a lot more than $150,000  The second problem I have is, I happen to know that over 8,000 Chicoans signed that petition, and there’s not more than 600 active members of the Tea Party  I also know the Tea Party didn’t sponsor the petition drive, nor were they the only people that marched out with those petitions  Colgan’s argument doesn’t make sense to me, but it’s amazing what kind of “facts” the general populace will believe if you just keep repeating them 
Some folks are trying to use the Tea Party as a target to rile up their peanut gallery, using Measure A as their rally call  They keep banging the same old drum  They refuse to have a rational discussion about the situation we’re facing, because it’s going to mean some sour beans for them and their trough-dwelling friends 
So, it’s up to a rational person like Stephanie Taber to lay it out straight for those who like facts  Stephanie attends the meetings, she reads the reports, she goes to the trouble of putting questions in writing for $taff, and then waiting persistently for an answer that practically has to be deciphered by a lawyer  She has followed this budget conversation since the day then-city-manager and first rat to jump, Greg Jones, expressed his grave concerns that we were headed straight for bankruptcy  She has followed the figures and checked the facts until she has forced these rats right to the wall – they have lately begun to dig their feet in and refuse to obey the sunshine laws, refusing to give the fiscal reports demanded by the city charter  Some people can try to run their little smokescreen of repetitive nonsense, but more rational people are finding out the truth  Thanks to Stephanie Taber for writing this letter below, which may or may not run in the Chico News and Review:
I’d like to take this opportunity to respond to Quentin Colgan’s letter of July 12th; primarily because the costs surrounding the Special Election held regarding Measure A have been distorted  Yes, it did cost $150,000, but why? That’s the elephant in the room  The progressives on the City Council chose the method by which the election would be held  Per the City Charter (which is the City’s Constitution) Section 501 clearly states “The City Council may determine that any Special Election shall be held by mailed ballot” etc  That would have cut the cost by half, at least  But the Council chose the most expensive means possible, voting at the precinct  They were afraid that just telling the students they were being disenfranchised, which was an obvious lie, would not be sufficient to defeat it 
As to “it’s all the Tea Party’s fault”; I was the only signature to the Measure  I felt no need to consult the Tea Party before I took that action; but did enlist the help of many concerned citizens to gather the more than 8,000 signature required to put it on the ballot 
Toby Schindelbeck has called upon our Finance Director to adhere to Section 908 of the City’s Charter which states “(the) Finance Director shall submit to the Council through the City Manager monthly statements of receipts, disbursements and balances in such form as to show the exact financial condition of the City”  It does not state when you may want to or if you have time to; it says “shall”  No one on the Council or otherwise can remember when that may have happened last  If it was being done as the Charter states it would have been recognize that the City was facing a financial Armageddon and steps could have been taken much earlier in the fiscal year to avoid the closing of Fire Station 5 
Tags: Ann Sc hwab Chico Ca, Ann Schwab for city council, Chico Enterprise Record, Chico News and Review, Chico Tea Party Patriots, City of Chico, David Little, Friends of Ann Schwab, Quentin Colgan, Robert Speer, Stephanie Taber
City Art Director Mary Gardner is foisting a new “Art Tax” on us to pay her own salary
To mgardner@ci chico ca us, gerimahood@yahoo com, mcbergarts@gmail com
(Mary Gardner, city of Chico public arts director, city of Chico, Geraldine Mahood and Monica Berg of the Arts Commission)
I recently read your memo here
Chico-Arts-Building-Tax pdf
I think it’s despicable Ms  Gardner that you are trying raise revenues for your own salary by foisting a new “Art Tax” on new development 
Ms  Mahood, Ms  Berg, nobody wants eggsuckers like you telling them how to spend their money or what’s “art”  You people make me sick 
The Chico Taxpayers Association will fight this grab, as will other civic groups through the area  That’s why you’ve kept your efforts “under the radar” I assume – you don’t want people to know about this, because you don’t want to hear what they think about it  Or YOU!
You people need to get real jobs and quit sucking off the public teat 
http://www norcalblogs com/adhoc/
Sincerely, Juanita Sumner, Chico CA
Tags: Ann Schwab Chico CA, Ann Schwab for city council, Chico Arts Commission, City of Chico "Art Tax", City of Chico Arts Policy Manual, Friends of Ann Schwab, Geraldine Mahood, Mary Gardner, Monica Berg
Jennifer Hennessy is incompetent – she can’t do her job and Burkland says she doesn’t have to
I’ll never forget my first real job – a clerical position at a manufacturing plant  I would compare it to the story of the miller’s daughter  On the first day, I was told that the employee I was to be replacing would stick around for a week to train me  At noon that day, having shown me where everything was and how to use the coffee maker, she got up from her chair, smiled, and told me she thought I could “handle it,” then left  At one o’clock, the plant manager came over to my desk followed by several “production” workers  They brought cart loads of microfilm, on rolls, in little white boxes  I was to label all of those boxes, three carts, piled high  This job had gotten held up, he explained, it would be “great!” if it could go out today  Did I think I could get them done by 4 o’clock? I wanted to make everybody happy, so said I yes without thinking, and set to work loading the labels into the typewriter 
It was a disaster  I had never typed anything like those labels before – typing class had been all about letters and envelopes, columns and reports  The labels skittered all over the platen, getting glue all over the inside of the typewriter  About every 50 or so labels, the platen had to be taken out and cleaned with alcohol  I typed and typed  By 3 o’clock I knew I was in trouble  The production workers had come over to my desk to help me affix the sticky labels  We were nervous, labels were getting screwed up  At 3:30 the office manager and receptionist came back to my desk to help with the labels  I typed and typed, and tried not to cry 
We didn’t make it  The plant manager was flustered  The salesman who’d promised the job was really pissed off, he said mean things  I apologized again and again, they told me it wasn’t all my fault, but could I please be more careful what I committed myself to in future  I could tell they also expected me to get a hell of a lot faster, but they were just trying to be nice 
So, I got faster  I came in early in the morning and worked through lunch until I got better at my job  I had signed up for a typing job, nobody had described all the weird stuff they expected me to type  It started with typing and labeling, not only sticky labels, but microfiche jackets  They have a little quarter inch tall label strip across the top that chips and peels if you aren’t careful loading them into the typewriter, and strips or frames of 35 and 16 mm film that falls out in your typewriter  Then there were the three-part work orders, with carbon paper, and the three-part shipping labels, also with carbon paper  There were the mistakes – whole orders that had been indexed incorrectly, and therefore typed incorrectly, and therefore had to be corrected and typed all over again  I won’t describe what I had to go through to correct microfiche labels, it was too stupid  I hated doing that, so I asked for my own little “eye-loup” – a little magnifier that you hold up to a light to look at the tiny little page numbers on the film – to make sure the cards had been indexed correctly before I typed them 
I’m not perfect, but I know I’m competent, cause I kept that job for five years while I watched others get fired, for everything from showing up late to breaking expensive equipment to stealing  I was given new jobs and increased responsibility as time went by  I got good job reviews from my supervisors, and good raises  Morale was high, we liked our co-workers and our managers, we felt like a team  Our customers were nice to us too  We worked for cities and counties, hospitals, banks – anybody who needed to keep records  We were trusted to handle confidential records, like people’s medical records  As we handled these confidential files we were simply told, “Don’t look at them,” so we didn’t 
I left in 1984 in finish school  Over the next decade computers killed the microfilm industry, and the company went out of business 
Excuse me if I compare my experiences in the private sector with stuff I’ve seen coming out of our city $taff  I keep waiting for some professional behavior, some professional accountability out of the people who run our town, and I start to wonder if I will ever get it  For a couple of months now, Toby Schindelbeck and Stephanie Taber, among others, have been asking council and Finance MisDirector Jennifer Hennessy to provide a simple accounting of city finances, as is required by the city charter, and she just plain refuses to give it  City Mangler Dave Burkland won’t make her 
Last month she actually admitted, she is UNABLE to do it  At the June 5 meeting she admitted that she is incompetent to follow the city charter  She said that when she came to her position seven years ago, she “struggled” with doing such a report – something every house wife does – and went whining to then-city-manager Tom Lando, who apparently patted her on the head and told her she didn’t have to do it anymore 
I don’t know about you guys, but I go over my check book every month, just to make sure everything is straight  I’ve found big, dumb mistakes, in the 100’s column even, that could have caused big, dumb problems down the road  I’m no math instructor, like Mary Goloff, but it’s not exactly rocket science – you just add your deposits and subtract your checks and withdrawals  I’ll admit, when my kids were little, I felt like I never had time to do that, and stuff would get screwed up  So now that I’ve got time, I make it a regularly scheduled event, and it’s amazing how much easier it is  And, I can keep the figures in my head, I know essentially how much I can afford to spend when I’m at the grocery store, or what kind of activities we can plan  My husband and son are enjoying a weekend trip right now that is already paid for, thankyouverymuch 
But Jennifer Hennessy is unable to do that? And she has expectable stuff – over 80 percent of her budget is payroll  She doesn’t have that many emergencies  The biggest emergency she’s had lately, is that the state has taken back the fund she’s been mis-using – the RDA  She was paying salaries and benefits out of a fund that’s supposed to be reserved for emergency public works projects  In other words, she’s been dipping into the till to pay her own salary!
The mayor is to blame here, she’s the captain of our ship  Unfortunately, like the captain of the Costa Concordia, she’s abandoned ship for a party onshore  While she and her college chums bully their bag ban down our throats, our ship is sinking  We have less than $200,000 in our reserve fund, we have un-secured pension obligations totaling in the millions and growing every day, and we have $taff who are using blackmail to get their way – they are just refusing to do their jobs  Hennessy won’t give the report she’s required to give because it’s BAD  I think the mayor is completely behind her on this – Ann Schwab doesn’t want us to hear that report either  Would you?
Please write a letter to council demanding that Hennessy do her job, or get out 
Tags: Ann Schwab Chico CA, Ann Schwab for city council, bankruptcy, City of Chico, Dave Burkland, embezzlement, Friends of Ann Schwab, Jennifer Hennessy, malfeasance
Scranton, Pennsylvania cuts workers to minimum wage – only $130,000 in their cash reserves
I finally got a chance to watch the video of last Tuesday’s council meeting  It cut on me during the meeting, just after Walker and Goloff were mopping up their attack on Sorensen, and I didn’t get it back til yesterday  I have watched the video in bits and snatches  I made it to the noise ordinance conversation last night, but had to turn it off after Jessica Allen and a couple of her friends got up to demand their rights to be bad neighbors 
One thing I learned is that the city of Chico has less than $200,000 in the reserve fund  No, I did not forget a zero on that figure, that’s it – less than $200,000  Read it and weep – and then call them to ask what they did with that property tax check you just sent in 
You can look at the budget report here: http://www chico ca us/finance/budget asp
You see the millions the city takes in, in sales tax (over $17 million) property tax (over $11 million), even taxes on your PG&E, phone and water (almost $7 million), and your visitors’ motel rooms (over $2 million)  To me that seems petty – “bed tax”? Some people think it’s a good idea to shake down the visitors of your town, as if it’s not enough that they spend money on your motels, restaurants and shopping centers  It’s a common grab all over California, every city does it  A lot of distasteful things become “common” when no decent person stands up to say “enough is enough ”
In Chico, as has been oft repeated, over 80 percent of our budget is in salaries and benefits  That’s the elephant in the room, and everybody’s getting pretty hip deep in elephant shit around here  It’s a simple concept, no matter how convoluted $taff and council try to make it: if they spend all the money on salaries, benefits, and the Great Pension Stock Market Disaster, there’s no money left to pay for supplies to say, clean up leaks in the sewer and water lines that are causing the state to fine us by the day, widen the roads that we are required to widen because of the permitting of Meriam Park, etc  And you can just get used to those pot holes in the street out front of your house  Got bad neighbors? Get a lawyer 
What’s really frustrating are the reactions of the cops and fire – they act like they don’t get paid at all  Those guys take most of the 80 percent  They get overtime written into their schedules  According to Hennessy, both fire and the cops are over budget on their workman’s comp claims for at least the third year in a row  The city just slammed another cop contract past us without public review, and signed the new chief’s contract three days before it was made available to the public, and then only by request and a direct visit to the clerk’s office Downtown 
So, we will get another year of poor response times, bitching and moaning from cops and fire  Get ready for your homeowners and your car insurance to go up – the insurance companies know when your local police and fire departments are a pile of shit 
And don’t think I’m not wondering about all those suspicious house fires 
You can just forget about any of the services a city is supposed to offer  Try to get something out of the city clerk these days – if you can catch her in the office!
Well, here’s the story of Scranton, Pennsylvania – home of Michael Scott!
http://bottomline msnbc msn com/_news/2012/07/10/12659748-scranton-pa-slashes-workers-pay-to-minimum-wage?lite
The mayor of Scranton, when faced with a situation similar to Chico’s mess, did what needed to be done  Unfortunately, he waited until it was too late to do something rational  I’m afraid it’s come to that with our city council – if you think that scene between Goloff and Sorensen was rational, well, you deserve to live here 
Tags: Ann Schwab for city council, Bob Evans for city council, Chico City council eletions 2012, cities declare bankruptcy, Friends of Ann Schwab, pensions, phone tax, salaries, sales tax increase
Marysville council rejects sales tax ploy by retiring city administrator – where’s Chico’s knight in shining armor?
I am not a member of the Chico Chamber of Commerce, but I check in to their website regularly to see what they’re up to  Sometimes I believe, they are the real Chico City Council  While our elected leaders frolic and cavort in their stupid committee meetings, the Chamber is working on a “Top 10 Economic Development Action List” 
Yeah, sounds great, until you consider, one of their “Top 10” is a proposal to raise the local sales tax 
One prominent member of the Chamber who might be able to fill us in on the discussion is Bob Evans  I’ve asked Bob where he stands on this tax increase, but he just keeps saying he hasn’t seen a proposal yet  Lately I have asked him if he would require Lando and the other sales tax increase proponents to get the legal number of signatures on a petition before he votes to put this proposal on the ballot, but he won’t answer me  His downright refusal to discuss the tax increase is frustrating to me – I want to believe Bob is a “fiscal conservative ” After all, he had some high and mighty things to say about his opposition to the phone tax  But, he knew the phone tax didn’t need his support to get on the ballot  It’s easy to posture as the good guy when you know others will achieve the end result you really want  Evans’ resistance to making a pledge against a sales tax increase is screaming in my ear like a fire alarm 
In Marysville, Mayor Bill Harris had no trouble making himself clear when his city mangler proposed a half-cent sales tax increase: “This will be viewed as the City Council coming to them wanting more money again ”
Well, the article mentioned, the city mangler is retiring, so I would also see it as his way of securing his f-ing pension, but nobody mentions that 
City councilwoman Christina Billeci echoed a sentiment I’ve been hearing increasingly in Chico – “We need to balance the budget with the revenues we have,” she said 
Other council members cited lack of support from citizens, including one councillor who claimed to have got “angry reactions” to the proposal  One council member said he might have supported the move before the June election, “But the cigarette tax was voted down, and that should have been a slam dunk,” he said  “I would see this as a waste of effort and money ”
The only council member who supported the notion, Head Start administrator Ricky Samayoa, made some pretty disparaging remarks about the town 
“There’s a lot of people that know there’s a lack of resources here for us to have a proper city and manage it,” he said  Oooo! A “proper city”! What a bitch! Does he have letters from constituents to support this statement, or is he just using “a lot of people” to describe himself and his co-workers? Not enough drive through coffee stands for you Ricky? Not enough 5 Star restaurants or pink boutiques? Sorry, we’ve never been ones for putting on the Ritz here in the North State, better get in your zip car and drive back to the Bay Area 
In the Enterprise Record story, Samoyoa further claimed that “continued cuts to maintenance and other aspects of the city’s budget hurt chances for an economic recovery ” I imagine Marysville has the same problem Chico has – too many $100,000+ salaries and not enough $20,000 – $50,000 workers  While he’s sitting down there under the air conditioner vent at Head Start in a fresh shirt and manicure, the streets are going unmaintained, the classrooms overcrowded, the police and fire departments underfunded – is that the problem Mr  Samayoa?
“The way we’re continuing to go, it’s just going to be a dying city, even if the economy picks up,” he said  Now, that statement doesn’t even make sense  This is a typical example of scare tactics  “The way we’re continuing to go…” You mean, paying $100,000+ salaries to fat bureaucrats, while cutting services to the public? Somehow I don’t think that’s what he’s talking about  ” …it’s just going to be a dying city…” Wow, what an idiot – obviously no knowledge of local history  Marysville has been through so many booms and busts, it ought to be called “Bouncyville ” If you get to know Marysville, you see it has everything needed to be a wonderful place to live, in good times and bad, regardless of carpetbaggers like Samayoa 
“Give folks the opportunity to have this debate,” Mr  Samayoa suggests  Sounds like the rhetoric coming from Andy Holcombe and the rest of the sales tax increase proponents  Hey, that’s a swell idea! People should talk about these things, hash them out  And then, if enough of them sign a petition to put such a proposal on a legal ballot, well, they can VOTE on it! But that costs alot of money – best for those who really believe in this cockamamie idea to get the petition first, show the need to spend all that money on an election  That’s what rational people would do, anyway 
But if you ask Holcombe to discuss the pending proposal, he denies there is any such thing  The only member of Chico City Council who is willing to discuss this proposal at all has been Mark Sorensen – thanks Mark  At least Mark has been good enough to answer our questions about the mechanics of such a proposal and getting it onto the ballot  Evans and Holcombe have both denied knowing anything about it, although Holcombe has made it good and clear he’d support raising the sales tax and Evans has been seen at Chamber discussions on the matter  The others have been mum to the public, but I’m guessing they will support it  Holcombe, Schwab, Goloff, Walker, Gruendl – and Evans? – are all banking on more revenues to rescue the city from the Shit Creek they’ve floated us up  Evans, while he will admit we’re in deep shit, will not offer so much as a suggestion of a paddle  He seems to be holding back until after he gets himself safely re-elected in November  Then he’s got a year to get that sales tax voted in and three years to make the public forget he had anything to do with it 
Well Bob, is that what you’re up to?
I’ll say, if he were at least honest, I might be able to hold my nose and support him, but this game he’s playing is a real turn-off 
Tags: Ann Schwab Chico CA, Ann Schwab for city council, Bob Evans Chico Ca, Bob Evans for city council, chico city council race 2012, city of Chico bankruptcy, city of Chico sales tax increase, Friends of Ann Schwab, Ricky Samayoa Marysville Ca
Council video feed still not available – $taff seems to have taken the Summer off!
I know, there’s probably a perfectly legitimate explanation for this  Debbie Presson isn’t sure why the feed is off, but she’s got somebody working on it  Not yesterday though, cause she was out of her office 
I’ll tell you what else is interesting – there haven’t been any of those morning meetings lately – in fact, it looks like all the committee meetings for July are CANCELLED  In fact, there hasn’t been an “Economic Development” committee meeting for months that I’m aware  For all intents and purposes, the city of Chico seems to be on Summer Vacation! How nice for them!
But, as you see, the town runs along without them  In fact, I’m wishing the public works department would also take a hike – they’re TOO BUSY right now, tearing up the streets Downtown  Oh well, the college students have “gone home” – what do we need Downtown for when the college students have gone home?
That seems to be the gist of if – the city of Chico is here to serve the college students  The rest of us can just get along – as long as we keep paying our taxes, nobody will bother us!
I just have to wonder, what are these $85,000, $95,000, $134,000 $taffers doing right now, and why do we need to keep paying them?
Tags: Ann Schwab Chico CA, Ann Schwab for city council, City of Chico, embezzlers, Friends of Ann Schwab, malfeasance
New police chief’s contract signed last Tuesday, made available to the public Friday – gotta love that “sunshine”!
Last Tuesday night we got a new police chief – Kirk Trostle  Only a month ago city manager Dave Burkland issued a statement – “police chief candidates not knockouts” according to the Enterprise Record  Trostle is a refugee from the Oroville police department, where, as chief, he certainly had his critics  He came to Chico only about a year and a half ago, from a department that was not without it’s problems  The council made their appointment without any elaboration – he was essentially the best thing they could come up with on short notice 
But shouldn’t we be able to negotiate a better contract with this man? Retiring Chief Porky Mike Maloney is getting over $165,000 a year, just in salary  He will be getting over $100,000 to retire, for the rest of his life, plus medical benefits  Frankly, I predict he’s carrying a colostomy bag within five years 
Have you seen Trostle’s contract? They signed it at council last Tuesday  But when we asked for it, they said we wouldn’t be able to look at it until Friday  I was invited to go down to the clerk’s office, at her convenience, 9 – 5, during MY WORK DAY, to look at a contract that had already been signed  Why in the hell would I want to do that? They don’t even offer you a decent cup of coffee 
So no, I haven’t seen it yet, but I’m guessing, it’s worse than Maloney’s contract  A fellow taxpayer went down Friday and reports he has the contracts, but has not given me any details  I don’t know if he had to pay for paper copies or what, but you can view it for free if you want to go down there  I’ll get back to you when I got something 
Tags: Ann Schwab Chico CA, Ann Schwab for city council, Chico Police Department, Chico Police Officers Association, City of Chico, Friends of Ann Schwab, Kirk Trostle chief of police chico ca, mike maloney retires at 50 what a pig
Mary Goloff and Jim Walker gang jump Mark Sorensen on the dais – just another lovely Chico city council meeting!
I’m sitting here in disbelief of the attack I just watched Mary Goloff and Jim Walker wage on Mark Sorensen at city council tonight  I couldn’t make the meeting, so I have been watching it via computer 
Sorensen had been challenged by a smarmy Jim Walker to list what changes he would make to balance the budget  Sorensen carefully began to explain that city funds had been depleted by millions over the last few years, with escalating costs leaving revenues in the dirt  He also explained that the lion’s share of our expenses are “operating costs,” meaning, salaries  He also carefully explained that there were programs we simply could not afford anymore, meaning, salaries 
Mary Goloff could be heard heckling him off microphone  If you or I did what she was doing we’d be asked to leave the room, possibly with police escort  But Mayor Schwab just sat there looking at Goloff, saying nothing  Goloff finally got on mike, interrupted Sorensen, and asked him to be specific  So, Sorensen offered housing, saying it had been a mistake to undertake so many housing projects, and he also specified the arts programs – such as the requirement that any capital project include one percent of the total cost of that project be added for art 
At this point Goloff began to interrupt Sorensen  She started heckling him about how “we all agree” that the arts are important, yadda, yadda  She just kept at Sorensen, not allowing him to answer any of her out-there questions, until Sorensen asked her to stop interrupting him 
After a quick exchange Walker butted in to attack Sorensen  Out of nowhere, Walker bashed Sorensen about wanting to spend more money on the police department, asking Sorensen where he would get the money to hire more police  This question was off base, Sorensen hadn’t even gotten that far before Goloff had completely derailed him 
Jim Walker is just sitting out his time, he seems to be enjoying himself at all of our expense  He, like so many “public servants,” seems to think he is elected to do what he wants, what seems like “the right thing” in his fairy tale mind, instead of carry out the law 
Mary Goloff seems to think she has been anointed Queen in some farcical aquatic ceremony to lead us all in the light of her cough syrup-induced wisdom  She seems to love the sound of her own voice, while here at my house, it sets off the hounds for blocks 
My computer started failing at this point, and I was unable to watch the rest of the meeting  I am going on vacation tomorrow, I’ll see you folks on the flip flop 
Tags: Ann Schwab Chico CA, Ann Schwab for city council, Friends of Ann Schwab
Turn that S*** UP!
We had a lively discussion down at the library yesterday about how we are going to fight the phone tax increase in November 
The key here is to inform the public  $taff has already done their best to make this measure confusing and deceptive, actually writing into the measure that it will lower taxes  They mean, they are lowering the rate half a cent, but of course, this half-cent will be an ice cube in hell when they apply the tax to all the new stuff this measure allows – starting with cell phones, texting, paging, and adding whatever new technology comes along  All the voter needs to know is, this measure will raise his/her taxes, noticeably 
Even people on welfare will pay this tax, even though they qualify for the rate-assistance plans offered by the phone companies – utility tax is based on the total bill, before the adjustment for the rate assistance  And, this tax includes those prepaid phone cards 
The hardest hit will be commercial customers  A friend of mine who owns a little manufacturing business in town tells me the city of Chico thinks all business owners are “rich sugar daddies” 
My friend always tells me, that while I am in these meetings Downtown, he is in Oroville or Redding or Modesto or some other town, dealing with his business  He says these towns have better, more workable $taff  He is among the business owners who have used the word “hostile” to describe Dave Burkland, and the city business climate in general 
We have to get the word out to people like my friend that NOW IS THE TIME to get involved  I like that band, Rage Against the Machine – they say, “it has to start somewhere, it has to start sometime  What better place than here, what better time than NOW!”
We’re fighting the city, which will use public money to fund this tax increase initiative  For example, they have already used $taff time to research and write the measure, and now council members and $taff will create the “for” argument to be placed on the ballot  Our city attorney makes over $190,000 a year in salary alone – Mark Sorensen figured the cost of an hour of her time, but I forget the figure  More than most people make in a day, is all I remember 
The city will turn over their arguments in favor in August – at that point we can take this dog and pony show on the road  Until then, let’s keep working  Thanks all!

Context 3: Paper Info

Title: Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents
Publish Date: Unkown
Author List: Sina Khajehabdollahi (from Department of Computer Science, University of Tübingen)

Figure

Figure2: An outline of the network controlling the foraging agent The sensor layer receives inputs at each time step (the ingredients of the nearest food), which are processed by the plastic layer in the same way as the static sensory network, Fig 1 The output of that network is given as input to the motor network, along with the distance d and angle α to the nearest food, the current velocity v, and energy E of the agent These signals are processed through two hidden layers to the final output of motor commands as the linear and angular acceleration of the agent
Figure4: The evolved parameters θ = (θ 1 ,      , θ 8 ) of the plasticity rule for the reward prediction (a ) and the decision (b ) tasks, for a variety of parameters (p tr = 0 01, d e ∈ 0, 0 1,      , 1, and σ ∈ 0, 0 1,      , 1 in all 100 combinations) Despite the relatively small difference between the tasks, the evolved learning rules differ considerably For visual guidance, the lines connect θs from the same run 
Figure5: a The trajectory of an agent (blue line) in the 2D environment A well-trained agent will approach and consume food with positive values (green dots) and avoid negative food (red dots) b The learning rate of the plastic sensory network eta p grows with the distance between environments d e c  and decreases with the frequency of environmental change d The fitness of an agent (measured as the total food consumed over its lifetime) increases over generations of the EA for both the scalar and binary readouts in the sensory network e The Pearson correlation coefficient of an evolved agent's weights with the ingredient value vector of the current environment (E 1 -blue, E 2 -red) In this example, the agent's weights are anti-correlated with its environment, which is not an issue for performance since the motor network can interpret the inverted signs of food 

abstract

The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem  We hypothesize that the emergence and exact form of learning behaviors is naturally connected with the statistics of environmental fluctuations and tasks an organism needs to solve 
Here, we study how different aspects of simulated environments shape an evolved synaptic plasticity rule in static and moving artificial agents  We demonstrate that environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity  Interestingly, the form of the emerging plasticity rule is additionally determined by the details of the task the artificial organisms are aiming to solve 
Moreover, we show that coevolution between static connectivity and interacting plasticity mechanisms in distinct sub-networks changes the function and form of the emerging plasticity rules in embodied agents performing a foraging task  One of the defining features of living organisms is their ability to adapt to their environment and incorporate new information to modify their behavior 
It is unclear how the ability to learn first evolved , but its utility appears evident  Natural environments are too complex for all the necessary information to be hardcoded genetically and more importantly, they keep changing during an organism's lifetime in ways that cannot be anticipated ;   The link between learning and environmental uncertainty and fluctuation has been extensively demonstrated in both natural ; , and artificial environments  
Nevertheless, the ability to learn does not come without costs  For the capacity to learn to be beneficial in evolutionary terms, a costly nurturing period is often required, a phenomenon observed in both biological , and artificial organisms   Additionally, it has been shown that in some complex environments, hardcoded behaviors may be superior to learned ones given limits in the agent's lifetime and envi-ronmental uncertainty ; ;  
The theoretical investigation of the optimal balance between learned and innate behaviors in natural and artificial systems goes back several decades  However, it has recently found also a wide range of applications in applied AI systems ;   Most AI systems are trained for specific tasks, and have no need for modification after their training has been completed 
Still, technological advances and the necessity to solve broad families of tasks make discussions about life-like AI systems relevant to a wide range of potential application areas  Thus the idea of open-ended AI agents that can continually interact with and adapt to changing environments has become particularly appealing 
Many different approaches for introducing lifelong learning in artificial agents have been proposed  Some of them draw direct inspiration from actual biological systems ;   Among them, the most biologically plausible solution is to equip artificial neural networks with some local neural plasticity , similar to the large variety of synaptic plasticity mechanisms ; ; that performs the bulk of the learning in the brains of living organisms  
The artificial plasticity mechanisms can be optimized to modify the connectivity of the artificial neural networks toward solving a particular task  The optimization can use a variety of approaches, most commonly evolutionary computation  The idea of meta-learning or optimizing synaptic plasticity rules to perform specific functions has been recently established as an engineering tool that can compete with stateof-the-art machine learning algorithms on various complex tasks ; ; Pedersen and Risi (2021);  
Additionally, it can be used to reverse engineer actual plasticity mechanisms found in biological neural networks and uncover their functions ;   Here, we study the effect that different factors (environ-arXiv:2303 06734v1 [q-bio NC] 12 Mar 2023 mental fluctuation and reliability, task complexity) have on the form of evolved functional reward-modulated plasticity rules 
We investigate the evolution of plasticity rules in static, single-layer simple networks  Then we increase the complexity by switching to moving agents performing a complex foraging task  In both cases, we study the impact of different environmental parameters on the form of the evolved plasticity mechanisms and the interaction of learned and static network connectivity 
Interestingly, we find that different environmental conditions and different combinations of static and plastic connectivity have a very large impact on the resulting plasticity rules  We imagine an agent who must forage to survive in an environment presenting various types of complex food particles  Each food particle is composed of various amounts and combinations of N ingredients that can have positive (food) or negative (poison) values 
The value of a food particle is a weighted sum of its ingredients  To predict the reward value of a given resource, the agent must learn the values of these ingredients by interacting with the environment  The priors could be generated by genetic memory, but the exact values are subject to change  To introduce environmental variability, we stochastically change the values of the ingredients 
More precisely, we define two ingredient-value distributions E 1 and E 2 and switch between them, with probability p tr for every time step  We control how (dis)similar the environments are by parametrically setting E 2 = (1 − 2d e )E 1 , with d e ∈ [0, 1] serving as a distance proxy for the environments; when d e = 0, the environment remains unchanged, and when d e = 1 the value of each ingredient fully reverses when the environmental transition happens 
For simplicity, we take values of the ingredients in E 1 equally spaced between -1 and 1 (for the visualization, see Fig  )  The static agent receives passively presented food as a vector of ingredients and can assess its compound value using the linear summation of its sensors with the (learned or evolved) weights, see Fig   
The network consists of N sensory neurons that are projecting to a single post-synaptic neuron  At each time step, an input X t = (x 1 ,       , x N ) is presented, were the value x i , i ∈ {1,       , N } represents the quantity of the ingredient i  We draw x i independently form a uniform distribution on the [0, 1] interval (x i ∼ U (0, 1)) 
The value of each ingredient w c i is determined by the environment (E 1 or E 2 )  The postsynaptic neuron outputs a prediction of the food X t value as y t = g(W X T t )  Throughout the paper, g will be either the identity function, in which case the prediction neuron is linear, or a step-function; however, it could be any other nonlinearity, such as a sigmoid or ReLU 
After outputting the prediction, the neuron receives feedback in the form of the real value of the input R t   The real value is computed as R t = W c X T t + ξ, where W c = (w c 1 ,       , w c N ) is the actual value of the ingredients, and ξ is a term summarizing the noise of reward and sensing system ξ ∼ N (0, σ)



ground_truth: KSTP switched to a sports radio format on February 15, 2010.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: According to the text, what is Toby Schindelbeck's observation about the police?

Read the following text and answer briefly.

Context 3: 
Figure : An outline of the static agent's network  The sensor layer receives inputs representing the quantity of each ingredient of a given food at each time step  The agent computes the prediction of the food's value y t and is then given the true value R t ; it finally uses this information in the plasticity rule to update the weight matrix 
For the evolutionary adjustment of the agent's parameters, the loss of the static agent is the sum of the mean squared errors (MSE) between its prediction y t and the reward R t over the lifetime of the agent  The agent's initial weights are set to the average of the two ingredient value distributions, which is the optimal initial value for the case of symmetric switching of environments that we consider here 
As a next step, we incorporate the sensory network of static agents into embodied agents that can move around in an environment scattered with food  To this end, we merge the static agent's network with a second, non-plastic motor network that is responsible for controlling the motion of the agent in the environment 
Specifically, the original plastic network now provides the agent with information about the value of the nearest food  The embodied agent has additional sensors for the distance from the nearest food, the angle between the current velocity and the nearest food direction, its own velocity, and its own energy level (sum of consumed food values) 
These inputs are processed by two hidden layers (of 30 and 15 neurons) with tanh activation  The network's outputs are angular and linear acceleration, Fig    The embodied agents spawn in a 2D space with periodic boundary conditions along with a number of food particles that are selected such that the mean of the food value distribution is ∼ 0  An agent can eat food by approaching it sufficiently closely, and each time a food particle is eaten, it is The sensor layer receives inputs at each time step (the ingredients of the nearest food), which are processed by the plastic layer in the same way as the static sensory network, Fig   
The output of that network is given as input to the motor network, along with the distance d and angle α to the nearest food, the current velocity v, and energy E of the agent  These signals are processed through two hidden layers to the final output of motor commands as the linear and angular acceleration of the agent re-spawned with the same value somewhere randomly on the grid (following the setup of ) 
After 5000 time steps, the cumulative reward of the agent (the sum of the values of all the food it consumed) is taken as its fitness  During the evolutionary optimization, the parameters for both the motor network (connections) and plastic network (learning rule parameters) are co-evolved, and so agents must simultaneously learn to move and discriminate good/bad food 
Reward-modulated plasticity is one of the most promising explanations for biological credit assignment   In our network, the plasticity rule that updates the weights of the linear sensor network is a rewardmodulated rule which is parameterized as a linear combination of the input, the output, and the reward at each time step:
Additionally, after each plasticity step, the weights are normalized by mean subtraction, an important step for the stabilization of Hebbian-like plasticity rules   We use a genetic algorithm to optimize the learning rate η p and amplitudes of different terms θ = (θ 1 ,       , θ 8 )  The successful plasticity rule after many food presentations must converge to a weight vector that predicts the correct food values (or allows the agent to correctly decide whether to eat a food or avoid it) 
To have comparable results, we divide θ = (θ 1 ,       , θ 8 ) by We then multiply the learning rate η p with θ max to maintain the rule's evolved form unchanged, η norm p = η p • θ max   In the following, we always use normalized η p and θ, omitting norm   To evolve the plasticity rule and the moving agents' motor networks, we use a simple genetic algorithm with elitism  
The agents' parameters are initialized at random (drawn from a Gaussian distribution), then the sensory network is trained by the plasticity rule and finally, the agents are evaluated  After each generation, the bestperforming agents (top 10 % of the population size) are selected and copied into the next generation 
The remaining 90 % of the generation is repopulated with mutated copies of the best-performing agents  We mutate agents by adding independent Gaussian noise (σ = 0 1) to its parameters  To start with, we consider a static agent whose goal is to identify the value of presented food correctly  The static reward-prediction network quickly evolves the parameters of the learning rule, successfully solving the prediction task 
We first look at the evolved learning rate η p , which determines how fast (if at all) the network's weight vector is updated during the lifetime of the agents  We identify three factors that control the learning rate parameter the EA converges to: the distance between the environments, the noisiness of the reward, and the rate of environmental transition 
The first natural factor is the distance d e between the two environments, with a larger distance requiring a higher learning rate, Fig    This is an expected result since the convergence time to the "correct" weights is highly dependent on the initial conditions  If an agent is born at a point very close to optimality, which naturally happens if the environments are similar, the distance it needs to traverse on the fitness landscape is small 
Therefore it can afford to have a small learning rate, which leads to a more stable convergence and is not affected by noise  A second parameter that impacts the learning rate is the variance of the rewards  The reward an agent receives for the plasticity step contains a noise term ξ that is drawn from a zero mean Gaussian distribution with standard deviation σ 
This parameter controls the unreliability of the agent's sensory system, i e , higher σ means that the information the agent gets about the value of the foods it consumes cannot be fully trusted to reflect the actual value of the foods  As σ increases, the learning rate η p decreases, which means that the more unreliable an environment becomes, the less an agent relies on plasticity to update its weights, Fig   
Indeed for some combinations of relatively small distance d e and high reward variance σ, the EA converges to a learning rate of η p ≈ 0  This means that the agent opts to have no adaptation during its lifetime and remain at the mean of the two environments  It is an optimal solution when the expected loss due to ignoring the environmental transitions is, on average, lower than the loss the plastic network will incur by learning via the (often misleading because of the high σ) environmental cues 
A final factor that affects the learning rate the EA will converge to is the frequency of environmental change during an agent's lifetime  Since the environmental change is modeled as a simple, two-state Markov process (Fig  ), the control parameter is the transition probability p tr   When keeping everything else the same, the learning rate rapidly rises as we increase the transition probability from 0, and after reaching a peak, it begins to decline slowly, eventually reaching zero (Fig  ) 
This means that when environmental transition is very rare, agents opt for a very low learning rate, allowing a slow and stable convergence to an environment-appropriate weight vector that leads to very low losses while the agent remains in that environment  As the rate of environmental transition increases, faster learning is required to speed up convergence in order to exploit the (comparatively shorter) stays in each environment 
Finally, as the environmental transition becomes too fast, the agents opt for slower or even no learning, which keeps them ) and the decision (b ) tasks, for a variety of parameters (p tr = 0 01, d e ∈ 0, 0 1,       , 1, and σ ∈ 0, 0 1,       , 1 in all 100 combinations)  Despite the relatively small difference between the tasks, the evolved learning rules differ considerably 
For visual guidance, the lines connect θs from the same run  near the middle of the two environments, ensuring that the average loss of the two environments is minimal (Fig  )  The form of the evolved learning rule depends on the task: Decision vs  Prediction The plasticity parameters θ = (θ 1 ,       , θ 8 ) for the rewardprediction task converge on approximately the same point, regardless of the environmental parameters (Fig  ) 
In particular, θ 3 → 1, θ 5 → −1, θ i → 0 for all other i, and thus the learning rule converges to: Since by definition y t = g(W t X T t ) = W t X T t (g(x) = x in this experiment) and R t = W c X T t + ξ we get: Thus the distribution of ∆W t converges to a distribution with mean 0 and variance depending on η p and σ and W converges to W c  
So this learning rule will match the agent's weight vector with the vector of ingredient values in the environment  We examine the robustness of the learning rule the EA discovers by considering a slight modification of our task  Instead of predicting the expected food value, the agent now needs to decide whether to eat the presented food or not 
This is done by introducing a step-function nonlinearity (g(x) = 1 if x ≥ 1 and 0 otherwise)  Then the output y(t) is computed as: Instead of the MSE loss between prediction and actual value, the fitness of the agent is now defined as the sum of the food values it chose to consume (by giving y t = 1)  Besides these two changes, the setup of the experiments remains exactly the same 
The qualitative relation between η p and parameters of environment d e , σ and p tr is preserved in the changed experiment  However, the resulting learning rule is significantly different (Fig



ground_truth: Toby Schindelbeck's observation is that the police say they aren't paid enough to enforce the laws in the streets.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What factors control the reliance of artificial organisms on plasticity?

Read the following text and answer briefly.

Context 3:  )  The evolution converges to the following learning rule: In both cases, the rule has the form ∆W t = η p X t [α y R t + β y ] 
Thus, the ∆W t is positive or negative depending on whether the reward R t is above or below a threshold (γ = −β y /α y ) that depends on the output decision of the network (y t = 0 or 1)  Both learning rules (for the reward-prediction and decision tasks) have a clear Hebbian form (coordination of preand post-synaptic activity) and use the incoming reward signal as a threshold 
These similarities indicate some common organizing principles of reward-modulated learning rules, but their significant differences highlight the sensitivity of the optimization process to task details  We now turn to the moving embodied agents in the 2D environment  To optimize these agents, both the motor network's connections and the sensory network's plasticity parameters evolve simultaneously 
Since the motor network is initially random and the agent has to move to find food, the number of interactions an agent experiences in its lifetime can be small, slowing down the learning  However, having the larger motor network also has benefits for evolution because it allows the output of the plastic network to be read out and transformed in different ways, resulting in a broad set of solutions 
The fitness of an agent (measured as the total food consumed over its lifetime) increases over generations of the EA for both the scalar and binary readouts in the sensory network  e  The Pearson correlation coefficient of an evolved agent's weights with the ingredient value vector of the current environment (E 1 -blue, E 2 -red) 
In this example, the agent's weights are anti-correlated with its environment, which is not an issue for performance since the motor network can interpret the inverted signs of food  The agents can solve the task effectively by evolving a functional motor network and a plasticity rule that converges to interpretable weights (Fig  ) 
After ∼ 100 evolutionary steps (Fig  ), the agents can learn the ingredient value distribution using the plastic network and reliably move towards foods with positive values while avoiding the ones with negative values  We compare the dependence of the moving and the static agents on the parameters of the environment: d e and the state transition probability p tr  
At first, in order to simplify the experiment, we set the transition probability to 0, but fixed the initial weights to be the average of E 1 and E 2 , while the real state is E 2   In this experiment, the distance between states d e indicates twice the distance between the agent's initial weights and the optimal weights (the environment's ingredient values) since the agent is initialized at the mean of the two environment distributions 
Same as for the static agent, the learning rate increases with the distance d e (Fig  )  Then, we examine the effect of the environmental transition probability p tr on the evolved learning rate η p   In order for an agent to get sufficient exposure to each environment, we scale down the probability p tr from the equivalent experiment for the static agents 
We find that as the probability of transition increases, the evolved learning rate η p decreases (Fig  )  This fits with the larger trend for the static agent, although there is a clear difference when it comes to the increase for very small transition probabil-ities that were clearly identifiable in the static but not the moving agents 
This could be due to much sparser data and possibly the insufficiently long lifetime of the moving agent (the necessity of scaling makes direct comparisons difficult)  Nevertheless, overall we see that the associations observed in the static agents between environmental distance d e and transition probability p tr and the evolved learning rate η p are largely maintained in the moving agents 
Still, more data would be needed to make any conclusive assertions about the exact effect of these environmental parameters on the emerging plasticity mechanisms  A crucial difference between the static and the moving agents is the function the plasticity has to perform  While in the static agents, the plasticity has to effectively identify the exact value distribution of the environment in order to produce accurate predictions, in the embodied agents, the plasticity has to merely produce a representation of the environment that the motor network can evolve to interpret adequately enough to make decisions about which food to consume 
To illustrate the difference, we plot the Pearson correlation coefficient between an agent's weights and the ingredient values of the environment it is moving in (Fig  )  We use the correlation instead of the MSE loss (which we used for the static agents in Fig  ) because the amplitude of the vector varies a lot for different agents and meaningful The evolved parameters of moving agents' plasticity rule for the g(s) = x, identity (a ) and the step function (Eq 
4) (b ) sensory networks (the environmental parameters here are d e ∈ [0, 1], σ = 0 and p tr = 0 001)  The step function (binary output) network evolved a more structured plasticity rule (e g , θ 3 > 0 for all realizations) than the linear network  Moreover, the learned weights for the identity network (c ) have higher variance and correlate significantly less with the environment's ingredient distribution compared to the learned weights for the thresholded network (d )
conclusions cannot be drawn from the MSE loss  For many agents, the learned weights are consistently anti-correlated with the actual ingredient values (an example of such an agent is shown in Fig  )  This means that the output of the sensory network will have the opposite sign from the actual food value 
While in the static network, this would lead to very bad predictions and high loss, in the foraging task, these agents perform exactly as well as the ones where the weights and ingredients values are positively correlated, since the motor network can simply learn to move towards food for which it gets a negative instead of a positive sensory input 
This additional step of the output of the plastic network going through the motor network before producing any behavior has a strong effect on the plasticity rules that the embodied agents evolve  Specifically, if we look at the emerging rules the top performing agents have evolved (Fig  ), it becomes clear that, unlike the very well-structured rules of the static agents (Fig  ), there is now virtually no discernible pattern or structure 
The difference becomes even clearer if we look at the learned weights (at the end of a simulation) of the best-performing agents (Fig  )  While there is some correlation with the environment's ingredient value distribution, the variance is very large, and they do not seem to converge on the "correct" values in any way 
This is to some extent expected since, unlike the static agents where the network's output has to be exactly correct, driving the evolution of rules that converge to the precise environmental distribution, in the embodied networks, the bulk of the processing is done by the motor network which can evolve to interpret the scalar value of the sensory network's output in a variety of ways 
Thus, as long as the sensory network's plasticity rule co-evolves with the motor network, any plasticity rule that learns to produce consistent information about the value of encountered food can potentially be selected  To further test this assumption, we introduce a bottleneck of information propagation between the sensory and motor networks by using a step-function nonlinearity on the output of the sensory network (Eq 
4)  Similarly to the decision task of the static network, the output of the sensory network now becomes binary  This effectively reduces the flow of information from the sensory to the motor network, forcing the sensory network to consistently decide whether food should be consumed (with the caveat that the motor network can still interpret the binary sign in either of two ways, either consuming food marked with 1 or the ones marked with 0 by the sensory network) 
The agents perform equally well in this variation of the task as before (Fig  ), but now, the evolved plasticity rules seem to be more structured (Fig  )  Moreover, the variance of the learned weights in the bestperforming agents is significantly reduced (Fig  ), which indicates that the bottleneck in the sensory network is in-creasing selection pressure for rules that learn the environment's food distribution accurately 
We find that different sources of variability have a strong impact on the extent to which evolving agents will develop neuronal plasticity mechanisms for adapting to their environment  A diverse environment, a reliable sensory system, and a rate of environmental change that is neither too large nor too small are necessary conditions for an agent to be able to effectively adapt via synaptic plasticity 
Additionally, we find that minor variations of the task an agent has to solve or the parametrization of the network can give rise to significantly different plasticity rules  Our results partially extend to embodied artificial agents performing a foraging task  We show that environmental variability also pushes the development of plasticity in such agents 
Still, in contrast to the static agents, we find that the interaction of a static motor network with a plastic sensory network gives rise to a much greater variety of wellfunctioning learning rules  We propose a potential cause of this degeneracy; as the relatively complex motor network is allowed to read out and process the outputs from the plastic network, any consistent information coming out of these outputs can be potentially interpreted in a behaviorally useful way 
Reducing the information the motor network can extract from the sensory system significantly limits learning rule variability  Our findings on the effect of environmental variability concur with the findings of previous studies that have identified the constraints that environmental variability places on the evolutionary viability of learning behaviors 
We extend these findings in a mechanistic model which uses a biologically plausible learning mechanism (synaptic plasticity)  We show how a simple evolutionary algorithm can optimize the different parameters of a simple reward-modulated plasticity rule for solving simple prediction and decision tasks 
Reward-modulated plasticity has been extensively studied as a plausible mechanism for credit assignment in the brain ; ; and has found several applications in artificial intelligence and robotics tasks ;   Here, we demonstrate how such rules can be very well-tuned to take into account different environmental parameters and produce optimal behavior in simple systems 
Additionally, we demonstrate how the co-evolution of plasticity and static functional connectivity in different subnetworks fundamentally changes the evolutionary pressures on the resulting plasticity rules, allowing for greater diversity in the form of the learning rule and the resulting learned connectivity 
Several studies have demonstrated how, in biological networks, synaptic plasticity heavily interacts with and is driven by network topology   Moreover, it has been recently demonstrated that biological plasticity mechanisms are highly redundant in the sense that any observed neural connectivity or recorded activity can be achieved with a variety of distinct, unrelated learning rules  
This observed redundancy of learning rules in biological settings complements our results and suggests that the function of plasticity rules cannot be studied independently of the connectivity and topology of the networks they are acting on  The optimization of functional plasticity in neural networks is a promising research direction both as a means to understand biological learning processes and as a tool for building more autonomous artificial systems 
Our results suggest that reward-modulated plasticity is highly adaptable to different environments and can be incorporated into larger systems that solve complex tasks  This work studies a simplified toy model of neural network learning in stochastic environments  Future work could be built on this basic framework to examine more complex reward distributions and sources of environmental variability 
Moreover, a greater degree of biological realism could be added by studying more plausible network architectures (multiple plastic layers, recurrent and feedback connections) and more sophisticated plasticity rule parametrizations  Additionally, our foraging simulations were constrained by limited computational resources and were far from exhaustive 
Further experiments can investigate environments with different constraints, food distributions, multiple seasons, more complex motor control systems and interactions of those systems with different sensory networks as well as the inclusion of plasticity on the motor parts of the artificial organisms 



ground_truth: Environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity.