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: Can someone sell or modify the Agency Spotter Content?

Read the following text and answer briefly.

Context 1: By purchasing now, you agree to the following terms  You authorize Agency Spotter to store and charge your payment method on file  Your paid account will renew automatically, unless you terminate it, or you notify Customer Service by email ([email protected]) of your decision to terminate your paid account  You must cancel your subscription before it renews in order to avoid billing of subscription fees for the renewal form to your credit card 
Should You object to any of the Terms or any subsequent modifications thereto, or become dissatisfied with the Site in any way, Your only recourse is to immediately discontinue use of the Site  Agency Spotter has the right, but is not obligated, to strictly enforce the Terms through self-help, community moderation, active investigation, litigation and prosecution 
(b) Agency Spotter will use commercially reasonable efforts to make the Services available on a 24 hours a day, 7 days a week, and 365 days a year basis, subject to Section 23 below and to downtime for maintenance purposes 
(c) Agency Spotter may from time to time modify the Services and add, change, or delete features of the Services in its sole discretion, without notice to you  Your continued use of the Service after any such changes to the Service constitutes your acceptance of these changes  Agency Spotter will use commercially reasonable efforts to post information on the Site regarding material changes to the Services 
(d) The contents of the Site, such as text, graphics, images, logos, user interfaces, visual interfaces, photographs, button icons, software, trademarks, sounds, music, artwork and computer code, and other Agency Spotter content (collectively, “Agency Spotter Content”), are protected under both United States and foreign copyright, trademark and other laws  All Agency Spotter Content is the property of Agency Spotter or its content suppliers or clients  The compilation (meaning the collection, arrangement and assembly) of all content on the Site is the exclusive property of Agency Spotter and is protected by United States and foreign copyright, trademark, and other laws  Unauthorized use of the Agency Spotter Content may violate these laws, and is strictly prohibited  You must retain all copyright, trademark, service mark and other proprietary notices contained in the original Agency Spotter Content on any authorized copy You make of the Agency Spotter Content 
(e) You agree not to sell or modify the Agency Spotter Content or reproduce, display, publicly perform, distribute, or otherwise use the Agency Spotter Content in any way for any public or commercial purpose, in connection with products or services that are not those of the Site, in any other manner that is likely to cause confusion among consumers, that disparages or discredits Agency Spotter or its licensors, that dilutes the strength of Agency Spotter’s or its licensor’s property, or that otherwise infringes Agency Spotter’s or its licensor’s intellectual property rights  You further agree to in no other way misuse Agency Spotter Content that appears on this Site  Any code that Agency Spotter creates to generate or display any Agency Spotter Content or the pages making up the Website is also protected by Agency Spotter’s copyright and You may not copy or adapt such code 
2  Site Restrictions  You may not use the Site in order to transmit, post, distribute, store or destroy material, including without limitation, the Agency Spotter Content, (a) in violation of any applicable law or regulation, (b) in a manner that will infringe the copyright, trademark, trade secret or other intellectual property rights of others or violate the privacy, publicity or other personal rights of others, (c) that is defamatory, obscene, threatening, abusive or hateful, or (d) that is in furtherance of criminal, fraudulent, or other unlawful activity  You are also prohibited from violating or attempting to violate the security of the Site and Services, including without limitation, the following activities: (a) accessing or attempting to access data not intended for You or logging into a server or account which You are not authorized to access; (b) attempting to probe, scan or test the vulnerability of a system or network or to breach security or authentication measures without proper authorization; (c) attempting to interfere with service to any other user of the Site or Services, host or network, including, without limitation, via means of submitting a virus to the Website, overloading, “flooding”, “spamming”, “mailbombing” or “crashing”; or (d) forging any TCP/IP packet header or any part of the header information in any e-mail or newsgroup posting  Violations of system or network security may result in civil and/or criminal liability 
3  Specific Prohibited Uses  The Agency Spotter Content and other features of the Site may be used only for lawful purposes  Agency Spotter specifically prohibits any other use of the Site, and You agree not to do any of the following: (a) use the Site for any purpose other than as a platform for connecting businesses and agencies, including but not limited to using the information in the Website to sell or promote any products or services; (b) post or submit to the Website any incomplete, false or inaccurate biographical information or information which is not Your own; (c) post on the Website any franchise, pyramid scheme or “club membership”; (d) send unsolicited mail or e-mail, make unsolicited phone calls or send unsolicited faxes regarding promotions and/or advertising of products or services to any other user(s) of the Website; (e) delete or revise any material posted by any other person or entity; (f) take any action that imposes an unreasonable or disproportionately large load on the Website’s infrastructure; (g) notwithstanding anything to the contrary contained herein, use or attempt to use any engine, software, tool, agent or other automatic device, program, algorithm, methodology or mechanism (including without limitation browsers, spiders, robots, avatars or intelligent agents) to navigate or search the Website other than the search engine and search agents available from Agency Spotter on the Website and other than through generally available third party web browsers (e g , Internet Explorer, Firefox, Safari); (h) decipher, decompile, disassemble or reverse engineer any of the software comprising or in any way making up a part of the Website; or (i) aggregate, copy or duplicate in any manner any of the Agency Spotter Content or information available from the Website, without express written consent from Agency Spotter 
(a) Certain features or services offered on or through the Site to users or agencies may require you to open a user or agency account (“Agency Account”) (including setting up a user ID and password)  You are entirely responsible for maintaining the confidentiality of the information you hold for your account, including your password, and for any and all activity that occurs under your account until you close down your account or prove that your account security was compromised due to no fault of your own  To close your account, please email us at [email protected] You agree to notify Agency Spotter immediately of any unauthorized use of your account or password, or any other breach of security  You may be held liable for losses incurred by Agency Spotter or any other user of or visitor to the Site due to someone else using your Agency Spotter ID, password or account as a result of your failing to keep your account information secure and confidential  You may not use anyone else’s Agency Spotter ID, password or account at any time without the express permission and consent of the holder of that Agency Spotter ID, password or account  Agency Spotter cannot and will not be liable for any loss or damage arising from your failure to comply with these obligations  Agency Spotter may verify Agency Accounts to confirm that such accounts meet Agency Spotter’s minimum requirements to be an agency, as the same may be modified or amended from time to time, and may assign an administrator to such verified Agency Account 
(b) To eligible to use the Site and the Services, you must meet the following criteria and represent and warrant that you: (i) are at least 18 years of age; (ii) are not currently restricted from the Site or Services, and are not otherwise prohibited from having an Agency Spotter account, (iii) are not a competitor of Agency Spotter or are not using the Site or Services for reasons that are in competition with Agency Spotter, (iv) will only maintain one Agency Spotter account at any given time, (v) have full power and authority to enter into this Agreement and doing so will not violate any other agreement to which you are bound, (vi) will not violate any rights of Agency Spotter, including intellectual property rights such as copyright and trademark rights, and (vii) agree to provide at your cost all equipment, software and internet access necessary to use the Site or Services 
6  User Content and Submissions  You understand that all information, data, text, software, music, sound, photographs, graphics, video, advertisements, messages or other materials submitted, posted or displayed by You on or through the Website (“User Content”) is the sole responsibility of the person from which such User Content originated  Agency Spotter claims no ownership or control over any User Content  You or a third party licensor, as appropriate, retain all patent, trademark and copyright to any User Content You submit, post or display on or through Agency Spotter and You are responsible for protecting those rights, as appropriate  By submitting, posting or displaying User Content on or through Agency Spotter, You grant Agency Spotter a worldwide, non-exclusive, royalty-free license to reproduce, adapt, distribute and publish such User Content through Agency Spotter  In addition, by submitting, posting or displaying User Content which is intended to be available to the general public, You grant Agency Spotter a worldwide, non-exclusive, royalty-free license to reproduce, adapt, distribute and publish such User Content for the purpose of promoting Agency Spotter Services  Agency Spotter will discontinue this licensed use within a commercially reasonable period after such User Content is removed from the Site  Agency Spotter reserves the right to refuse to accept, post, display or transmit any User Content in its sole discretion 
You also represent and warrant that You have the right to grant, or that the holder of any rights has completely and effectively waived all such rights and validly and irrevocably granted to You the right to grant, the license stated above  If You post User Content in any public area of the Website, You also permit any user of the Website to access, display, view, store and reproduce such User Content for personal use  Subject to the foregoing, the owner of such User Content placed on the Website retains any and all rights that may exist in such User Content 
Agency Spotter does not represent or guarantee the truthfulness, accuracy, or reliability of User Content or endorse any opinions expressed by users of the Website  You acknowledge that any reliance on material posted by other users will be at Your own risk 
The following is a partial list of User Content that is prohibited on the Website  Prohibited Content includes, but is not limited to, Content that: is implicitly or explicitly offensive, such as User Content that engages in, endorses or promotes racism, bigotry, discrimination, hatred or physical harm of any kind against any group or individual; harasses, incites harassment or advocates harassment of any group or individual; involves the transmission of “junk mail”, “chain letters,” or unsolicited mass mailing or “spamming”; promotes or endorses false or misleading information or illegal activities or conduct that is abusive, threatening, obscene, defamatory or libelous; promotes or endorses an illegal or unauthorized copy of another person’s copyrighted work, such as providing or making available pirated computer programs or links to them, providing or making available information to circumvent manufacture-installed copy-protect devices, or providing or making available pirated music or other media or links to pirated music or other media files; contains restricted or password only access pages, or hidden pages or images; displays or links to pornographic, indecent or sexually explicit material of any kind; provides or links to material that exploits people under the age of 18 in a sexual, violent or other manner, or solicits personal information from anyone under 18; or provides instructional information about illegal activities or other activities prohibited by these Terms and Conditions, including without limitation, making or buying illegal weapons, violating someone’s privacy, providing or creating computer viruses or pirating any media; and/or solicits passwords or personal identifying information from other users 
It is your responsibility to keep your Agency Spotter profile information accurate and updated 
7  User-to-User Communications and Sharing (Agency Spotter Groups, Ratings, Reviews, Updates, Agency Pages, etc )  Agency Spotter offers various forums such as Agency Spotter Groups, Ratings, Reviews, and Updates, where you can post your observations and comments on designated topics  Agency Spotter also enables sharing of information by allowing users to post updates, including links to news articles and other information such as product recommendations, job opportunities, and other content to their profile and other parts of the Site, such as Agency Spotter Groups and Agency Pages  Agency Spotter members can create Agency Spotter Groups and Agency Pages for free; however, Agency Spotter may close or transfer Agency Spotter Groups or Agency Pages, or remove content from them if the content violates these Terms or others’ intellectual property rights  To create an Agency Spotter Agency Page, the Agency must be a company or legal entity that meets Agency Spotter’s minimum requirements for an Agency, and you must have the authority to create the Agency Page on behalf of the third party Agency 
For clarity, only DMCA Notices should go to the Copyright Agent; any other feedback, comments, requests for technical support, and other communications should be directed to: [email protected] You acknowledge that if you fail to comply with all of the requirements of this Section, your DMCA Notice may not be valid 
Upon receipt of a Notice, Agency Spotter will take whatever action, in its sole discretion, it deems appropriate, including removal of the challenged material from the Site and/or termination of the User’s account in appropriate circumstances  Please note that a Complainant may be liable for damages (including costs and attorneys’ fees) if he or she knowingly makes a material misrepresentation that content is infringing 
(i) If you have posted material subject to a DMCA Notice that allegedly infringes a copyright (the “Counterclaimant”), you may send Agency Spotter a written Counter Notice pursuant to Section 512(g), (ii) and 512(g), (iii) of the DMCA  When Agency Spotter receives a Counter Notice, it may, in its discretion, reinstate the material in question not less than ten (10) nor more than fourteen (14) days after receiving the Counter Notice unless Agency Spotter first receives notice from the Claimant that he or she has filed a legal action to restrain the allegedly infringing activity  Please note that Agency Spotter will send a copy of the Counter Notice to the address provided by the Claimant  A Counterclaimant may be liable for damages (including costs and attorneys’ fees) if he or she knowingly makes a material misrepresentation that that material or activity was removed or disabled by mistake or misidentification 
1  Identification of the material that has been removed or to which access has been disabled and the location at which the material appeared before it was removed or access to it was disabled 
2  A statement under penalty of perjury that you have a good faith belief that the material was removed or disabled as a result of mistake or misidentification of the material to be removed or disabled 
3  Your name, address, and telephone number, and a statement that you consent to the jurisdiction of Federal District Court for the judicial district in which the address is located, or if your address is outside of the United States, for any judicial district in which Agency Spotter may be found, and that you will accept service of process from the person who provided notification under subsection (c)(1)(C) of the DMCA or an agent of such person 
(c) AGENCY SPOTTER HAS NO OBLIGATION TO ADJUDICATE CLAIMS OF INFRINGEMENT – EACH USER’S AGREEMENT TO HOLD AGENCY SPOTTER HARMLESS FROM CLAIMS  Claimants, Counterclaimants, and users understand that Agency Spotter is not an intellectual property tribunal  While Agency Spotter may, in its discretion, use the information provided in a DMCA Notice and Counter Notice in order to decide how to respond to infringement claims, Agency Spotter is not responsible for determining the merits of such claims  If a Counterclaimant responds to a claim of infringement by providing a Counter Notice, the Counterclaimant agrees that if Agency Spotter restores or maintains the content, the Counterclaimant will defend and hold Agency Spotter harmless from any resulting claims of infringement against Agency Spotter 
10  Advertisements and Other Potential Sources Of Revenue  Some of the Services may now or in the future be supported by advertising revenue, pay-per-click mechanisms, or other funding, and the Site may display advertisements and promotions  These advertisements may be targeted to the content of information stored via the Site, queries made through the Services, or other criteria  The manner, mode and extent of advertising on the Site are subject to change without specific notice to you  In consideration for Agency Spotter granting you access to and use of the Site and the Services, you agree that the Agency Spotter may place such advertising on the Site and/or incorporate such advertisements into the Services 
11  DISCLAIMERS  THE SITE AND ITS CONTENT AND THE SERVICES ARE PROVIDED “AS IS” AND AGENCY SPOTTER MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, ABOUT THE IMAGES OR SITE INCLUDING, WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT, TO THE FULLEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW  AGENCY SPOTTER DOES NOT WARRANT THAT ACCESS TO THE SITE OR ITS CONTENTS OR THE SERVICES WILL BE UNINTERRUPTED OR ERROR-FREE, THAT DEFECTS WILL BE CORRECTED, OR THAT THIS SITE OR THE SERVERS THAT MAKE IT AVAILABLE ARE FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS  AGENCY SPOTTER DOES NOT WARRANT OR MAKE ANY REPRESENTATIONS REGARDING THE USE OR THE RESULTS OF THE USE OF ANY CONTENT ON THE SITE IN TERMS OF ITS CORRECTNESS, ACCURACY, RELIABILITY, OR OTHERWISE  ACCORDINGLY, YOU ACKNOWLEDGE THAT YOUR USE OF THE SITE IS AT YOUR OWN RISK  YOU (AND NOT AGENCY SPOTTER) ASSUME THE ENTIRE COST OF ALL NECESSARY SERVICING, REPAIR, OR CORRECTION RESULTING FROM COMPUTER MALFUNCTION, VIRUSES OR THE LIKE  APPLICABLE LAW MAY NOT ALLOW THE EXCLUSION OF IMPLIED WARRANTIES, SO THE ABOVE EXCLUSION MAY NOT APPLY TO YOU 
12  Limitation on Liability  Neither Agency Spotter, nor its licensors, representatives, affiliates, employees, shareholders or directors (collectively, “Agency Spotter Affiliates”), shall be cumulatively responsible or liable for (a) any damages in excess of three (3) times the most recent monthly fee that you paid for a Premium Service, if any, or US $100, whichever amount is greater, or (b) any damages of any kind including, without limitation, lost business, profits or data (or the cost to recreate such data), direct, indirect, incidental, consequential, compensatory, exemplary, special or punitive damages that may result from Your access to or use of Website, the Agency Spotter Content, or the Services, or any content or other materials on, accessed through or downloaded from the Site  The allocations of liability in this Section represent the agreed and bargained-for understanding of the parties and the fees herein reflects such allocation  These limitations of liability will apply notwithstanding any failure of essential purpose of any limited remedy, whether your claim is based in contract, tort, statute or any other legal theory, and whether we knew or should have known about the possibility of such damages; provided, however, that this limitation of liability shall not apply if you have entered into a separate written agreement to purchase Premium Services with a separate Limitation of Liability provision that expressly supersedes this Section in relation to those Premium Services 
13  Indemnification  In the event that You use the Website, the Agency Spotter Content, or any portion thereof, in any manner not authorized by Agency Spotter, or if You otherwise infringe any intellectual property rights or any other rights relating to other users, You agree to indemnify and hold Agency Spotter, its subsidiaries, affiliates, licensors and representatives, harmless against any losses, expenses, costs or damages, including reasonable attorneys’ fees, incurred by them as a result of unauthorized use of the Website or the Agency Spotter Content and/or Your breach or alleged breach of these Terms and Conditions 
(a) You agree that Agency Spotter and its licensors own all intellectual property rights in and to the Services, the Site and related Software, including but not limited to the look and feel, structure, organization, design, algorithms, templates, data models, logic flow, text, graphics, logos, and screen displays associated therewith 
(b) You will not reverse engineer, decompile or disassemble the Software, or otherwise attempt to reconstruct or discover the source code for the Software  You further agree not to resell, lease, assign, distribute, time share or otherwise commercially exploit or make the Services available to any third party for such third party’s benefit 
(c) You may make a single copy of the Downloaded Software for backup purposes only; provided that any such copies contain the same proprietary rights notices that appear on the Downloaded Software  Agency Spotter reserves all rights in the Services and Software not expressly granted to you hereunder  As used herein, “Software” means Agency Spotter’s proprietary software used to deliver the Services, made available to you as part of the Site and/or Services, and all updates and associated documentation thereto made available as a part of the Site or Services pursuant to these Terms, including Downloadable Software  The term “Downloadable Software” means client software downloaded by you from the Site that augments your use of the Site and/or Services, including add-ins, sample code, APIs and ancillary programs 
(d) Agency Spotter shall have a perpetual, royalty-free, worldwide, and transferable license to use or incorporate into the Site and Services any suggestions, ideas, enhancements, feedback, or other information provided by you related to the Site or Services 
(e) Agency Spotter may derive and compile aggregated and/or analytical information from your usage of the Site and Services  Such aggregated data and metadata may be used for Agency Spotter’s own purposes without restriction, including, but not limited to, using such data in conjunction with data from other sources to improve Agency Spotter’s products and services and to create new products 
15  Third Party Software and Features; Agency Spotter Applications  (a) Agency Spotter may make software from third-party companies available to You  To download such software, You may be required to agree to the respective software licenses and/or warranties of such third-party software  Each software product is subject to the individual company’s terms and conditions, and the agreement will be between You and the respective company  This means that Agency Spotter does not guarantee that any software You download will be free of any contaminating or destructive code, such as viruses, worms or Trojan horses  Agency Spotter does not offer any warranty on any third-party software You download using the Site  Further, the Site and/or Service may contain features, functionality and information that are provided through or by third-party content, software, websites, and/or system (“Third Party Materials”)  Your use and access of these features and functionality are subject to the terms published or otherwise made available by the third-party providers of Third Party Materials  Agency Spotter has no responsibility for any Third-Party Materials, and you irrevocably waive any claim against Agency Spotter with respect to such Third-Party Materials 
(b) Agency Spotter may also offer the Services through applications built using Agency Spotter’s platform (“Agency Spotter Applications”), including smart phone applications, “Share” and other similar buttons and other interactive plugins distributed on websites across the Internet  Agency Spotter Applications are distinct from Third-Party Materials and applications address in Section 14(a), above  If you use an Agency Spotter application or interact with a website that has deployed a plugin, you agree that information about you and your use of the Services, including, but not limited to, your device, your mobile carrier, your internet access provider, your physical location, and/or web pages containing Agency Spotter plugins that load in your browser may be communicated to us  You acknowledge that you are responsible for all charges and necessary permissions related to accessing Agency Spotter through your mobile access provider  You should therefore check with your provider to find out if the Services are available and the terms for these services for your specific mobile devices  Finally, by using any downloadable application to enable your use of the Services, you are explicitly confirming your acceptance of the terms of the End User License Agreement associated with the application provided at download or installation, or as may be updated from time to time 
16  International Use  Agency Spotter makes no representation that materials on this site are appropriate or available for use in locations outside the United States, and accessing them from territories where their contents are illegal is prohibited  Those who choose to access this site from other locations do so on their own initiative and are responsible for compliance with local laws 
17  Dispute Resolution  These Terms and any claim, cause of action or dispute (“claim”) arising out of or related to these Terms shall be governed by the laws of the State of Georgia, regardless of your country of origin or where you access Agency Spotter, and notwithstanding any conflicts of law principles and the United Nations Convention for the International Sale of Goods  You and Agency Spotter agree that all claims arising out of or related to these Terms must be resolved exclusively by a state or federal court located in Fulton County, Georgia, except as otherwise mutually agreed in writing by the parties or as described in the Arbitration option in Section 16(b), below  You and Agency Spotter agree to submit to the personal jurisdiction of the courts located within Fulton County, Georgia, for the purpose of litigating all such claims  Notwithstanding the foregoing, you agree that Agency Spotter shall still be allowed to seek injunctive remedies (or an equivalent type of urgent legal relief) in any jurisdiction 
18  Arbitration  You agree that any dispute, claim or controversy arising hereunder or relating in any way to the Terms, shall be settled by binding arbitration in Fulton County, Georgia, in accordance with the commercial arbitration rules of Judicial Arbitration and Mediation Services (“JAMS”)  The arbitrator shall issue a written decision specifying the basis for the award made  The party filing a claim or counterclaim in the arbitration proceeding shall pay the deposit(s) determined by JAMS with respect to such claim or counterclaim  All other costs associated with the arbitration and imposed by JAMS shall be paid as determined by the arbitrator(s) and, in absence of such determination, equally by each party to the arbitration  In addition, unless the arbitrator awards payment of reasonable attorney and other fees to a party, each party to the arbitration shall be responsible for its own attorneys’ fees and other professional fees incurred in connection with the arbitration  Determinations of the arbitrator will be final and binding upon the parties to the arbitration, and judgment upon the award rendered by the arbitrator may be entered in any court having jurisdiction, or application may be made to such court for a judicial acceptance of the award and an order of enforcement, as the case may be  The arbitrator shall apply the substantive law of the State of Georgia, without giving effect to its conflict of laws rules 
19  Export Control  You agree to comply with all relevant export laws and regulations, including, but not limited to, the U S  Export Administration Regulations and Executive Orders (“Export Controls”)  You warrant that you are not a person, company or destination restricted or prohibited by Export Controls (“Restricted Person”)  You will not, directly or indirectly, export, re-export, divert, or transfer the Site or Service or any related software, any portion thereof or any materials, items or technology relating to Agency Spotter’s business or related technical data or any direct product thereof to any Restricted Person, or otherwise to any end user and without obtaining the required authorizations from the appropriate governmental entities 
(a) These Terms will continue until terminated in accordance with this Section 
(b) You may cancel your legal agreement with Agency Spotter at any time by (i) notifying Agency Spotter in writing, (ii) ceasing to use the Services, and (iii) closing your accounts for all of the Services which you use, if we have made this option available to you  Your cancellation of the Services will not alter your obligation to pay all charges incurred prior to your effective date of termination 
Agency Spotter may terminate its legal agreement with you if, (i) you have breached any provision of the Terms (or have acted in manner which clearly shows that you do not intend to, or are unable to comply with the provisions of the Terms), or (ii) Agency Spotter is required to do so by law (for example, where the provision of the Services to you is, or becomes, unlawful), or (iii) Agency Spotter is transitioning to no longer providing the Services to users in the country in which you are resident or from which you use the service, or (iv) the provision of the Services to you by Agency Spotter is, in Agency Spotters’ opinion, no longer commercially viable 
(c) The terms provided in Sections 2, 3, 6, 11, 12, 13, 14, 17, 19, 20, 21 and 22 of these Terms shall survive any termination of these Terms 
21  Independent Contractors  The parties are and intend to be independent contractors with respect to the Services contemplated hereunder  You agree that neither you nor any of your employees or contractors shall be considered as having an employee status with Agency Spotter  No form of joint employer, joint venture, partnership, or similar relationship between the parties is intended or hereby created 
22  Assignment and Delegation  You may not assign or delegate any rights or obligations under these Terms  Any purported assignment or delegation shall be ineffective  We may freely assign or delegate all rights and obligations under these Terms, fully or partially, without notice to you  We may also substitute, by way of unilateral novation, effective upon notice to you, Agency Spotter Inc  for any third party that assumes our rights and obligations under these Terms 
The personally identifiable information we collect from you allows us to provide you with the Services and to enable users to navigate and enjoy using the Site  We will also use your personally identifiable information to develop, improve and advertise the Site and Services  We may also use your personally identifiable information for internal purposes such as auditing, data analysis and research to improve our Services and customer communications  We do not rent, sell or otherwise provide your personally identifiable information to third parties without your consent, except as described in this policy or as required by law 
When you register with us through the Site or Services and become a Registered User, or when you wish to contact another Registered User, we will ask you for personally identifiable information  This refers to information about you that can be used to contact or identify you (“Personally Identifiable Information“)  Personally Identifiable Information includes, but is not limited to, your name, phone numbers, email address, home postal address, business address, social media user names, employer/affiliated organization, reasons for accessing the Site, and intended usage of requested information, but does not include your credit card number or billing information  We may also use your email address or phone number (if provided by you) to contact you regarding changes to the Services; system maintenance and outage issues; account issues; or otherwise to troubleshoot problems  In order to process some of your transactions through the Site and Services, we may also ask for your credit card number and other billing information (“Billing Information“; and, together with Personally Identifiable Information, “Personal Information“) 
Information you provide to us also includes your account profile and your contributions to discussion groups and community features Agency Spotter may offer  Do not upload or insert any information to or into the Site or Services that you do not want to be shared or used in the manner described in this section 
In addition, when you use the Site, our servers automatically record certain information that your web browser sends whenever you visit any website  These server logs may include information such as your web request, Internet Protocol address, browser type, browser language, referring/exit pages and URLs, platform type, number of clicks, domain names, landing pages, pages viewed and the order of those pages, the amount of time spent on particular pages, the date and time of your request, and one or more cookies that may uniquely identify your browser 
Information from third party services and other websites 
Do not upload or insert any information to or into the Site or Services that you do not want to be shared or used in the manner described in this section 
Advertisements  Advertisers who present ads on the Site may use technological methods to measure the effectiveness of their ads and to personalize advertising content  You may use your browser cookie settings to limit or prevent the placement of cookies by advertising networks  Agency Spotter does not share personally identifiable information with advertisers unless we get your permission 
Links  When you click on links on Agency Spotter you may leave our site  We are not responsible for the privacy practices of other sites, and we encourage you to read their privacy statements 
If we are requested to disclose your information to a government agency or official, we will do so if we believe in good faith, after considering your privacy interests and other relevant factors, that such disclosure is necessary to: (i) conform to legal requirements or comply with a legal process with which we are involved; (ii) protect our rights or property or the rights or property of our affiliated companies; (iii) prevent a crime or protect national security; or (iv) protect the personal safety of Site users or the public  Because Agency Spotter is a United States limited liability company and information collected on our Site is stored in whole or in part in the United States, your information may be subject to U S  law 
We also reserve the right to disclose Personally Identifiable Information and/or other information about users that Agency Spotter believes, in good faith, is appropriate or necessary to enforce our agreements, take precautions against liability, investigate and defend itself against ay third-party claims or allegations, assist government enforcement agencies, protect the security or integrity of our Site or Services, and protect the rights, property or personal safety of Agency Spotter, our users and others 
Cookies allow us to (i) manage, present and keep track of temporary information, such as data you upload onto the Site for use with the Services; (ii) register you as a Registered User on the Site or in other various programs associated with the Site; (iii) remember you when you log in to the places on the Site that require you to be a Registered User of the Site; (iv) help us understand the size of our audience and traffic patterns; (v) collect and record information about what you viewed on the Site; and (vi) deliver specific information to you based on your interests 
When you access the Site, the Site automatically collects certain non-personally identifiable information through the use of electronic images known as web beacons (sometimes called single-pixel gifs) and log files  Such information may include your IP address, browser type, the date, time and duration of your access and usage of the Site and whether you opened emails You received from us 
This information is collected for all visits to the Site and then analyzed in the aggregate  This information is useful for, among other things, tracking the performance of our online advertising, such as online banner ads, and determining where to place future advertising on other websites 
Editing your profile  You may review and change or remove your personal information or the settings for your Agency Spotter account at any time by going to your account profile  You can edit your name, email address, password and other account information here  Please be aware that even after your request for a change is processed, Agency Spotter may, for a time, retain residual information about you in its backup and/or archival copies of its database 
Deactivating or deleting your account  If you want to stop using your account you may deactivate it or delete it  When you deactivate an account, no user will be able to see it, but it will not be deleted  We save your profile information in case you later decide to reactivate your account  Many users deactivate their accounts for temporary reasons and in doing so are asking us to maintain their information until they return to Agency Spotter  You will still have the ability to reactivate your account and restore your profile in its entirety  When you delete an account, it is permanently deleted from Agency Spotter  You should only delete your account if you are certain you never want to reactivate it  You may deactivate your account or delete your account within your account profile 
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Context 2: UT College of Liberal Arts: College of Liberal Arts University of Texas at Austin Departments Graduate Resources Undergraduate Resources Courses Online Courses Dean's Office Alumni & Giving Faculty by Department Search the College of Liberal Arts
next profile Jeffrey Tulis Associate Professor — Ph D ,
E-mail: tulis@austin utexas edu
Office: MEZ 3 152
Political Theory and American Politics
Professor Tulis's interests bridge the fields of political theory and American politics, including more specifically, American political development, constitutional theory, political philosophy and the American presidency  His publications include The Presidency in the Constitutional Order (LSU, 1981; Transaction, 2010), The Rhetorical Presidency (Princeton, 1987), The Constitutional Presidency (Johns Hopkins 2009), The Limits of Constitutional Democracy (Princeton, 2010) and recent journal articles and chapters on constitutional interpretation, the logic of political change, and the meaning of political success  Four collections of essays on The Rhetorical Presidency with responses by Tulis have been published, including a special double issue of Critical Review: An Interdisciplinary Journal of Politics and Society, (2007), where his book is described as "one of the two or three most important and perceptive works written by a political scientist in the twentieth century "
He has served as President of the Politics and History Section of the American Political Science Association  He received the President's Associates Teaching Excellence Award at the University of Texas  He has held research fellowships from NEH, ACLS, Olin Foundation, Harvard Law School, and the Mellon Preceptorship at Princeton University, where he taught before moving to Texas  He has held visiting positions at Notre Dame and Harvard  He has served as associate chair of the Department of Government from 1989-2001 and was acting chair during 1992-93  and for part of each year between 1989 and 2001  During the academic year 2008-09, he was a Laurance S  Rockefeller Visiting Fellow at the University Center for Human Values at Princeton  During Spring 2016, he was a Dahrendorf Visiting Fellow at the London School of Economics and Political Science 
His forthcoming books include: Legacies of Losing in American Politics, with Nicole Mellow (University of Chicago Press, Fall 2017), and an expanded edition of The Rhetorical Presidency in the Princeton Classics series (Princeton, Fall 2017)  For two decades he served as co-editor of the Johns Hopkins Series in Constitutional Thought, and he currently co-edits (with Sanford Levinson) Constitutional Thinking, a Series at the University Press of Kansas 
GOV 370L • Pres In Constitutional Ord 38840 • Spring 2017 Meets MW 2:30PM-4:00PM CAL 221 show description
GOV 370 Seminar: The Presidency in the Constitutional Order
Spring 2017 Unique # 38840
MW 2:30 to 4pm GDC 2 402
Jeffrey K  Tulis
In this Seminar we will discuss a series of constitutional problems including: the problem of executive energy in the American Constitution; presidential selection and the problem of political legitimacy; separation of powers; delegation of powers, the constitutional status of war and foreign affairs, administration and bureaucracy and the meaning of leadership in the constitutional order 
Seminar will meet twice a week and regular attendance and thorough preparation for discussion is expected  Unexcused absence from more than three classes will result in failure of the participation component of the course  There will also be pop quizzes on the reading that will count as part of your participation grade  In addition to class participation, course requirements include four short analytic essays, and one in-class test  The course grade will be calculated as follows:
Seminar participation: 20%
In-class test: 20%
Three analytic essays 60% (20% each)
Class participation is especially important  Preparation for seminar and for your in-class test will be enhanced by careful note taking on the readings  If students appear to be unprepared, pop quizzes will be given and the grades on them will affect the participation component of your course grade 
Texts: (tentative)
Joseph M  Bessette and Jeffrey K  Tulis, The Constitutional Presidency
Michael Nelson, The Presidency in the Political System (tenth edition)
Richard Ellis and Michael Nelson, Debating the Presidency (third edition)
The Federalist (any edition, or online) GOV 310L • American Government-Honors 38335 • Fall 2016 Meets TTH 3:30PM-5:00PM BEN 1 106 show description
GOV 310 (Honors) (38335) Fall 2016
TTH 3:30-5:00pm, BEN 1 106
This honors seminar offers an introduction to American politics that emphasizes the confluence of ideas, mores, institutions, and interests, in the constitutional system  This course covers more theory, and the readings are more demanding, than other versions of GOV 310  One of the main objectives of the course is to deepen your understanding of the practical aspects of contemporary public affairs by developing your ability to understand the theoretical foundations of American politics  Although we cover the nuts and bolts of politics there is much more theory in this version of GOV 310  If you have registered for this section mainly because 310 is a legislative requirement that you need to fulfill, this is not the right version for you  There is a substantial workload in this class 
Regular attendance, thorough and timely preparation, and active participation are all necessary to do well 
Four essays (approximately 1000 words each)  Three of these will be assigned analytic essay topics  The last will be a book review of a title chosen by the student from a long list of provided possibilities  (15% each essay, 60% of total course grade)
Two in-class tests  These will count 15% each, 30% of total course grade 
Class participation  (10% of course grade)  Both informed participation and occasional leadership of the seminar will be graded 
No make-up exams or late papers, except for documented medical or other emergencies 
Mark Landy and Sidney M  Milkis, American Government: Enduring Principles, Critical Choices, Third Edition
Mary Nichols and David Nichols, Readings in American Government, Ninth Edition
Thomas Mann and Norman Ornstein, Its Even Worse Than It Looks: How the American Constitutional System Collided With the New Politics of Extremism
Bruce Ackerman,Before the Next Attack: Preserving Civil Liberties in an Age of Terrorism GOV 381L • Constitutional Conflict 38660 • Fall 2016 Meets W 3:30PM-6:30PM BAT 5 102 show description
GOV 381L Fall 2016
Constitutional Conflict
W 3:30-6:30pm, BAT 5 102
Many of the most important debates regarding the nature and character of contemporary American politics are essentially arguments regarding the structure of separation of powers  In this seminar we will consider such questions as whether the American system is prone to deadlock of stalemate in the construction of national policy; whether conflict is a hindrance to institutional responsibility or an essential attribute of responsibility; whether there are “political questions” especially suitable to resolution between President and Congress; how one can distinguish salutary from pathological conflict, and whether it is truly possible to harness the ambition of office holders to the duties of their office 
More specifically, we will review literature and arguments regarding constitutional reform; divided government; separation of powers theory; and case studies of Supreme Court appointments; the budget process; and war powers and foreign affairs  In these contexts we will also discuss current controversies surrounding war authorization, intelligence and secrecy, sequestration, government shut downs and budget resolutions, and debt ceiling politics 
The course is designed to accommodate two different student needs: it will provide a good overview of important literature relevant to the comprehensive examination in American politics and it will provide opportunities for research  This subject area is a treasure trove of “hot” topics, publication possibilities, subjects for MA theses and Ph D  dissertations  I will tailor the written requirements to the objectives of individual students 
1  All students will prepare a short analytic essay early in the semester, and an annotated bibliography at mid-semester  These assignments will count (30%) of the grade 
2  Students interested primarily in exam preparation will complete an examination near the end of the semester based on study questions assigned in advance  OR
Students interested in research will write a 20-25 page paper  (60%)
3  A basic requirement of the course is that students prepare for each seminar by carefully reading the material assigned for that week  Class discussion is an essential component of the course  (10%)
Tentative Texts:
Jones, Separate But Equal Branches
Silverstein, Imbalance of Powers
Wilson & Schram, Separation of Powers and Good Government
Burgess, Contest for Constitutional Authority
Farrier, Passing the Buck: Congress, the Budget and Deficits
Weissman, A Culture of Deference
Zeisberg, War Powers: The Politics of Constitutional Authority
Fisher, Congressional Abdication on War and Spending
Lowi, The End of Liberalism GOV 379S • Regime Persp Amer Poltc-Honors 38105 • Spring 2016 Meets TH 3:30PM-6:30PM GAR 1 134 (also listed as CTI 335, LAH 350) show description
GOV 379S Regime Perspectives on American Politics
This is a seminar on American politics and culture  Two purposes govern the selection of texts for the course and guide our discussion of them  All of our texts attempt to look at American politics as a whole  Most books and courses on America look at only a part, such as the Presidency, or elections, or popular culture  Here we attempt to think about how the parts of America fit together  Even when these texts speak about a part, for example an institution such as the presidency or the Congress, they present the topic from a vantage point on the whole polity  To see the polity as a whole also means that we will have to revisit and rethink aspects of our political life that we take for granted – that we don’t examine because those parts have become so natural or familiar to us  Seeing the polity whole enables us to render the familiar unfamiliar, to make what we take for granted strange and new 
To see the polity as a whole requires that we get some distance from our subject, much as to see the planet earth as a whole requires one to look at it from outer space  Just as it is difficult to get visual perspective on a place living within it, it is difficult to understand the promise or pathologies of a regime from within  To get critical distance from our politics, we will closely study three sets of texts that look at American politics from a distance  The first part of the course will recover the perspective of the founding debate between Federalists and Anti-federalists  This fundamental debate reveals what is a stake in the basic architecture of the American regime  The second part of the course is a close study of Tocqueville’s Democracy in America  Regarded by many as the best book ever written on democracy and the best book written on America, Tocqueville sees our polity whole because he looks at it from the vantage point of Europe, in general, and France, in particular  In the third part of the seminar we think about American politics from the perspective of thoughtful commentators who feel only nominally included in the polity  Half in and half out, these extraordinary black American writers reveal fissures and fault lines in the American regime  We end the class with a discussion of America’s place in the world today – examining a speech by a writer who articulately raises challenges to our self-understanding that are inarticulately expressed today in rage and ranting from enemies of the United States 
Three take home analytic essays, chosen from a list of topics I provide, each weighted 25% of the course grade  Late essays will not be accepted, except with a doctor’s excuse or a Dean’s excuse for family emergency 
OR as an option: you may write the two short essays (both together weighted 25%) and do a longer 15 page paper on a topic of your choice in consultation with me (weighted 50% of your course grade)  Government honors students who are thinking of doing an honors thesis next year may prefer this option to begin to develop research and writing skills for longer work  Students who prefer this option will need to designate their preferred third short essay and have discussed with me a topic for their long paper by March 30  Texts:
Selected Anti-Federalist writings
Tocqueville, Democracy in America
Essays, speeches and articles by Frederick Douglass, W E B  Dubois, Booker T  Washington, James Baldwin and Ralph Ellison GOV 382M • Democratic Theory 38120 • Spring 2016 Meets M 3:30PM-6:30PM BAT 1 104 show description
GOV 382M (38120)
Democratic Theory Spring 2016
This is a graduate seminar on contemporary topics in democratic theory  Topics to be covered include: democratic epistemology; deliberative democracy; the meaning of the people; oracular democracy; agonistic democracy; and possibly new theories of republicanism, representation and partisanship 
Texts (tentative)
Helene Landemore, Democratic Reason
Jeffrey Edward Green, The Eyes of the People
Amy Gutmann and Dennis Thompson, Why Deliberative Democracy?
Alan Keenan, Democracy in Question
Jason Frank, Constituent Moments
Jason Frank, Publius and Political Imagination
Nadia Urbanati, Democracy Disfigured
Russell Muirhead, Partisanship in a Polarized Age
Bryan Garsten, manuscript
Active seminar participation; an annotated bibliography or review essay; a research/analytic paper  GOV 310L • American Government-Honors 37615 • Fall 2015 Meets TTH 2:00PM-3:30PM BEN 1 106 show description
TTH 2-3:30/BEN 1 106
Bruce Ackerman,Before the Next Attack: Preserving Civil Liberties in an Age of Terrorism GOV 370L • Presidency In Constitutl Order 37845 • Fall 2015 Meets TTH 5:00PM-6:30PM PAR 310 show description
GOV 370L (37845)
TTH 5-6:30 PAR 310
The Presidency in the Constitutional Order
A study of the place of the presidency in the American political order that stresses tension between power and accountability inherent in the office and the system  Topics include: separation of powers, presidential selection, impeachment, relations with Congress and bureaucracy, emergency powers, presidential character, and leadership 
This is a very demanding writing flag class  If you are enrolling in this class just in order to satisfy the writing flag, you are in the wrong class  Interest in political theory and willingness to work very hard are necessary for success in this class 
Joseph M  Bessette, The Constitutional Presidency
Andrew Rudalevige, The New Imperial Presidency
Bruce Ackerman, The Rise and Decline of the American Republic
Michael Nelson, ed , The Presidency in the Political System
Michael Nelson, ed , The Evolving Presidency
Louis Fisher, Constitutional Conflicts Between Congress and the President
Active and prepared class participation
Regular quizzes on the reading
Four analytic essays (approximately 1200 words) 
One term paper, (approximately 5000 words)  GOV 379S • Regime Persp On Amer Politics 38100 • Spring 2015 Meets T 3:30PM-6:30PM MEZ 1 104 (also listed as LAH 350) show description
Essays, speeches and articles by Frederick Douglass, W E B  Dubois, Booker T  Washington, James Baldwin and Ralph Ellison GOV 382M • Tocqueville 38135 • Spring 2015 Meets M 3:30PM-6:30PM BAT 5 102 show description
This graduate seminar will be devoted to close readings of two principal writings of Tocqueville: Democracy in America and The Ancien Regime and the Revolution  We will also assess some of the best secondary studies of Tocqueville, including work by Sheldon Wolin, Harvey Mansfield, Delba Winthrop, Jon Elster, Francois Furet, and a book by Pierre Manent 
Course requirements will include two very short analytic essays and one seminar paper (20-25 pages)  GOV 310L • American Government-Honors 38722 • Fall 2014 Meets TTH 2:00PM-3:30PM GAR 2 112 show description
Joseph M  Bessette and John J  Pitney, American Government and Politics: Deliberation, Democracy and Citizenship
Mary Nichols and David Nichols, Readings in American Government
Bruce Ackerman,Before the Next Attack: Preserving Civil Liberties in an Age of Terrorism GOV 370L • Presidency In Constitutl Order 38977 • Fall 2014 Meets TTH 9:30AM-11:00AM CBA 4 332 show description
A study of the place of the presidency in the American political order that stresses
tension between power and accountability inherent in the office and the system 
Topics include: separation of powers, presidential selection, impeachment,
relations with Congress and bureaucracy, emergency powers, presidential
character, and leadership 
This is a very demanding writing flag class  If you are enrolling in this class just in order
to satisfy the writing flag, you are in the wrong class  Interest in political theory and willingness
to work very hard are necessary for success in this class 
One term paper, (approximately 5000 words)  GOV 379S • Regime Persp On Amer Politics 39395 • Spring 2014 Meets T 3:30PM-6:30PM MEZ 1 104 (also listed as CTI 335, LAH 350) show description
Essays, speeches and articles by Frederick Douglass, W E B  Dubois, Booker T  Washington, James Baldwin and Ralph Ellison GOV 381L • Constitutional Conflict 39415 • Spring 2014 Meets M 3:30PM-6:30PM BAT 1 104 show description
Lowi, The End of Liberalism GOV 330K • The American President 39140 • Fall 2013 Meets MW 3:00PM-4:30PM MEZ B0 306 show description
This course offers an over view of the place of the presidency in the American political order  Topics covered include: constitutional design of the office; nominations and elections; legislative leadership; leadership of the bureaucracy; staffing and organizing the White House; the presidency and the judiciary; war and emergencies  We will spend extra time this fall on the presidential campaign and election of 2012 
Two in-class examinations (50% of the final grade)
One short (1000 word) take-home essay (30% of the final grade)
Class participation and quizzes (20% of the final grade)
Richard J  Ellis, The Development of the American Presidency (Routledge, 2012)
Richard J  Ellis and Michael Nelson, eds, Debating the American Presidency, (2nd edition, CQ Press, 2009)
Packet of selected primary texts (to be linked or posted on Blackboard)  GOV 330K • The American President 39145 • Fall 2013 Meets MW 5:00PM-6:30PM MEZ B0
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: No.

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 is Professor Tulis's forthcoming book?

Read the following text and answer briefly.

Context 2: 306 show description
Packet of selected primary texts (to be linked or posted on Blackboard)  GOV 381L • American Founding 39040 • Spring 2013 Meets T 6:30PM-9:30PM BAT 1 104 show description
NOTE WELL: Course meets Tuesdays, 6:30 to 9:30pm
Batts Hall 1 104
This is a seminar on American political thought and constitutional design  It is designed for students of American politics and political theory  The principal themes include: 1) the nature of founding and its constitutive significance; 2) the relation of structure and power in American politics; 3) the meaning and significance of the Federalist/Anti-Federalist debate; 4) the philosophic background of the American founding; and 5) the relevance of the founding to debate to prospects for, and pathologies of, American politics today 
We will conduct a close reading of the Madison’s Notes, of The Federalist, and selected Anti-Federalist writings  We will also study a larger and growing body of secondary literature on the constitutional convention, ratification and early American political thought 
James Madison, Notes of the Debates: In the Federal Convention of 1787
The Federalist (Rossiter, ed )
The Anti-Federalist (Storing, ed )
David Brian Robertson, The Constitution and America’s Destiny (2005)
Pauline Maier, Ratification (2012)
Gordon Wood, The Idea of America (2011)
Jack Rakove, Original Meanings: Politics & Ideas in the Making of the Constitution
Herbert Storing, What the Anti-Federalists Were For (1981)
Numerous essays and articles (to be posted on line or gathered in packet)
Grading: Active seminar participation, including three short papers and presentations (40%) and one article-length seminar paper (60%) T C 357 • Amer Founding/Probs Const Des 43095 • Spring 2013 Meets M 3:30PM-6:30PM CRD 007B show description
The American Founding and Problems of Constitutional Design
Jeffrey Tulis, Associate Professor, Department of Government
Sanford Levinson, Professor, School of Law
This Plan II seminar will be built around a close reading of the debates that informed the drafting and ratification of the U S  Constitution  We aim to recover the perspective of these founding thinkers -- their way of thinking -- as much as their concrete ideas, in order to raise fundamental questions about the American political order today  Are some of the most important pathologies of American politics today rooted in design features of our original political architecture? Are the original answers to basic founding questions (such as "how democratic is our Constitution?) still adequate for contemporary circumstances? What features of the Constitution should we preserve and what features should we amend, if possible? Would it be good for the polity as a whole to reconsider these questions in a new constitutional convention today, or would such an event be a political nightmare? Our reading will include notes from the founding conventions, writings by Federalists and Anti-Federalists, and present-day critiques of the American political order  Our aim will be to generate a dialogue between the thought of the founders and some of the best present day critics and supporters of the Constitution 
James Madison, Notes of the Debates in the Federal Convention
The Federalist, ed  Clinton Rossiter
The Anti-Federalist, ed  Herbert Storing
Pauline Maier, Ratification: The People Debate the Constitution, 1787-1788
Sanford Levinson, Framed: America’s 51 Constitutions and the Crisis of Governance
Bruce Ackerman, The Decline and Fall of the American Republic
Robert Goldwin, ed  How Democratic is the Constitution?
a course packet of selected articles, essays, and additional primary materials 
Class participation, including at least one presentation of a short discussion paper 25%
One take-home analytic essay 25%
One term paper 50%
About the Professors:
Professor Tulis's interests bridge the fields of political theory and American politics, including more specifically, American political development, constitutional theory, political philosophy and the American presidency  He received the President's Associates Teaching Excellence Award at the University of Texas  He has held research fellowships from NEH, ACLS, Olin Foundation, Harvard Law School, and the Mellon Preceptorship at Princeton University, where he taught before moving to Texas  He has held visiting positions at Notre Dame and Harvard  During the academic year 2008-09, he was a Laurance S  Rockefeller Visiting Fellow at the University Center for Human Values at Princeton 
Proefessor Levinson holds the W  St  John Garwood and W  St  John Garwood, Jr  Centennial Chair in Law, he joined the University of Texas Law School in 1980  Previously a member of the Department of Politics at Princeton University, he is also a Professor in the Department of Government at the University of Texas  The author of over 350 articles and book reviews in professional and popular journals--and a regular contributor to the popular blog Balkinization  He received the Lifetime Achievement Award from the Law and Courts Section of the American Political Science Association in 2010  He has been a visiting faculty member of the Boston University, Georgetown, Harvard, New York University, and Yale law schools in the United States and has taught abroad in programs of law in London; Paris; Jerusalem; Auckland, New Zealand; and Melbourne, Australia 
GOV 330K • The American President 38675 • Fall 2012 Meets MW 3:00PM-4:30PM MEZ B0 306 show description
Packet of selected primary texts (to be linked or posted on Blackboard)  GOV 330K • The American President 38675 • Fall 2011 Meets MW 3:30PM-5:00PM WAG 420 show description
see syllabus GOV 330K • The American President 38680 • Fall 2011 Meets MW 5:30PM-7:00PM UTC 1 146 show description
see syllabus GOV 379S • Regime Persp On Amer Polit-Hon 39110 • Spring 2011 Meets W 3:30PM-6:30PM BAT 5 102 (also listed as CTI 326, LAH 350) show description
To see the polity as a whole requires that we get some distance from our subject, much as to see the planet earth as a whole requires one to look at it from outer space  Just as it is difficult to get visual perspective on a place living within it, it is difficult to understand the promise or pathologies of a regime from within it  To get critical distance from our politics, we will closely study three sets of texts that look at American politics from a distance  The first part of the course will recover the perspective of the founding debate between Federalists and Anti-federalists  This fundamental debate reveals what is a stake in the basic architecture of the American regime  The second part of the course is a close study of Tocqueville’s Democracy in America  Regarded by many as the best book ever written on democracy and the best book written on America, Tocqueville sees our polity whole because he looks at it from the vantage point of Europe, in general, and France, in particular  In the third part of the seminar we think about American politics from the perspective of thoughtful commentators who feel only nominally included in the polity  Half in and half out, these extraordinary black American writers reveal fissures and fault lines in the American regime  We end the class with a discussion of America’s place in the world today – examining a speech by a writer who articulately raises challenges to our self-understanding that are inarticulately expressed today in rage and ranting from enemies of the United States 
Four take home writing assignments  Analytic essays, each 1000-1500 words  (Grades weighted: 10%, 25%, 25%, and 25%) Late essays will not be accepted, except with a doctor’s excuse or a Dean’s excuse for family emergency  Regular preparation and class participation: 15% 
OR as an option: By prior arrangement with me by the due date of the second analytic essay, students may substitute one longer research paper (15 – 20 pages) for two of the last three analytic papers This paper will be on a topic of the students choosing , if I approve, and the due date will be the same as the last assigned analytic essay  This project would count 50% of the students course grade 
Selected writings by Frederick Douglass, W E B  Dubois, Ralph Ellison, James Baldwin
Solzhenitsyn, “A World Split Apart”
Tocqueville, Democracy in America GOV 382M • Tocqueville 39150 • Spring 2011 Meets T 6:30PM-9:30PM BAT 5 102 show description
See syllabus GOV 370L • President, Congress, And Court 38695 • Fall 2010 Meets TTH 8:00AM-9:30AM UTC 3 112 show description
Course Description: A Study of the political relationship of the President, Congress and Court in the American constitutional order  Has this relationship changed over the course of American history? Is American national politics prone to stalemate or deadlock between the branches regarding major issues of public policy? Do we have a new “imperial presidency?” Should the Court arbitrate disputes between the President and Congress over custody of their respective powers? Has Congress abdicated its constitutional responsibilities? We will examine questions like these in light of practical problems such as executive privilege and secrecy, the war on terror, budget politics and controversies regarding appointments to the Supreme Court  Grading:Three in class essay tests, for which study questions will be distributed in advance  The exam questions will be chosen from the list of study questions  (25% each) One short take home essay (10% each)  Class participation and attendance (15%)  Tentative Texts: The FederalistFisher, Congressional Abdication on War and SpendingRudalevige, The New Imperial PresidencyBessette and Tulis, The Constitutional PresidencySkowronek, Presidency in Political TimeGoldsmith, The Terror PresidencyA course packet of articles and essays GOV 370L • President, Congress, And Court 38700 • Fall 2010 Meets TTH 5:00PM-6:30PM UTC 3 122 show description
Course Description: A Study of the political relationship of the President, Congress and Court in the American constitutional order  Has this relationship changed over the course of American history? Is American national politics prone to stalemate or deadlock between the branches regarding major issues of public policy? Do we have a new “imperial presidency?” Should the Court arbitrate disputes between the President and Congress over custody of their respective powers? Has Congress abdicated its constitutional responsibilities? We will examine questions like these in light of practical problems such as executive privilege and secrecy, the war on terror, budget politics and controversies regarding appointments to the Supreme Court  Grading:Three in class essay tests, for which study questions will be distributed in advance  The exam questions will be chosen from the list of study questions  (25% each) One short take home essay (10% each)  Class participation and attendance (15%)  Tentative Texts: The FederalistFisher, Congressional Abdication on War and SpendingRudalevige, The New Imperial PresidencyBessette and Tulis, The Constitutional PresidencySkowronek, Presidency in Political TimeGoldsmith, The Terror PresidencyA course packet of articles and essays GOV 312L • Iss & Policies In Amer Gov-Hon 38698 • Spring 2010 Meets MW 3:30PM-5:00PM UTC 3 104 show description
Government 312L satisfies the second half of the mandated six hours of government that every UT student must take  Course covers analysis of varying topics concerned with American political institutions and policies, including the United States Constitution, and assumes basic knowledge of government from GOV 310L, which is a prerequiste  May be taken for credit only once  GOV 370L • President, Congress, And Court 38966 • Spring 2010 Meets MW 5:00PM-6:30PM MEZ B0 306 show description
Prerequisite: Six semester hours of lower-division coursework in government 
GOV 370L • President, Congress, And Court 39295 • Fall 2009 Meets TTH 2:00PM-3:30PM UTC 3 112 show description
GOV 370L • President, Congress, And Court 39435 • Spring 2008 Meets MW 3:00PM-4:30PM PAR 203 show description
GOV 312L • Iss & Policies In Am Gov-Hon-W 38615-38620 • Spring 2007 Meets MW 11:00AM-12:00PM MEZ B0 306 show description
Government 312L satisfies the second half of the mandated six hours of government that every UT student must take  Course covers analysis of varying topics concerned with American political institutions and policies, including the United States Constitution, and assumes basic knowledge of government from GOV 310L, which is a prerequiste  May be taken for credit only once  GOV 312L • Iss & Policies In Am Gov-Hon-W 37600-37605 • Spring 2006 Meets MW 11:00AM-12:00PM MEZ B0 306 show description
Government 312L satisfies the second half of the mandated six hours of government that every UT student must take  Course covers analysis of varying topics concerned with American political institutions and policies, including the United States Constitution, and assumes basic knowledge of government from GOV 310L, which is a prerequiste  May be taken for credit only once  GOV 312L • Iss & Policies In Am Gov-Hon-W 34900-34905 • Spring 2004 Meets MW 11:00AM-12:00PM BUR 134 show description
Government 312L satisfies the second half of the mandated six hours of government that every UT student must take  Course covers analysis of varying topics concerned with American political institutions and policies, including the United States Constitution, and assumes basic knowledge of government from GOV 310L, which is a prerequiste  May be taken for credit only once  GOV 312L • Iss & Policies In Am Gov-Hon-W 34495-34500 • Spring 2003 Meets MW 11:00AM-12:00PM UTC 1 130 show description
Government 312L satisfies the second half of the mandated six hours of government that every UT student must take  Course covers analysis of varying topics concerned with American political institutions and policies, including the United States Constitution, and assumes basic knowledge of government from GOV 310L, which is a prerequiste  May be taken for credit only once  Publications
Tulis, JK (2011), "Plausible Futures," in Dunn, Charles W  (ed ) The Presidency in the Twenty-First Century, University Press of Kentucky Tulis, J K  and Macedo, S  (2010) The Limits of Constitutional Democracy, Princeton University Press Tulis, J K  and Macedo, S  (2010) "Constitutional Boundaries," in The Limits of Constitutional Democracy, Princeton University Press Tulis, JK (2010), "The Possibility of Constitutional Statesmanship," in Tulis, JK and Macedo, S (eds ) The Limits of Constitutional Democracy, Princeton University Press Tulis, J  (2009) The Constitutional Presidency  Johns Hopkins University Press Tulis, J  (2009) Impeachment in the Constitutional Order  In J  Tulis & J M  Bessette (Eds ), The Constitutional Presidency  Johns Hopkins University Press Tulis, J  & Bessette, J M  (2009) On the Constitution, Politics, and the Presidency  In J  Tulis & J M  Bessette (Eds ), The Constitutional Presidency  Johns Hopkins University Press Tulis, J (and Bessette, J M) (2010) The Presidency in the Constitutional Order: Historical Perspectives, Reissued Classics Series, Transaction Publishers,Tulis, J and Bessette, J M  (2010, "Introduction to the Transaction Edition," The Presidency in the Constitutional Order: Historical Perspectives, Transaction Publishers 
Tulis, JK, (2009) "The Two Constitutional Presidencies," in Nelson, Michael (ed ) The Presidency in the Political System, Congressional Quarterly Press Tulis, J  & Mellow, N  (2007) Andrew Johnson and the Politics of Failure  In S  Skowronek & M  Glassman (Eds ), Formative Acts: Reckoning with Agency in American Politics  Philadelphia: University of Pennsylvania Press Tulis, J  (2007, September) The Rhetorical Presidency in Retrospect  Critical Review: An Interdisciplinary Journal of Politics and Society, 19(2&3)  Curriculum Vitae
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: Legacies of Losing in American Politics and an expanded edition of The Rhetorical Presidency in the Princeton Classics series.

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: How does the transition probability of the environment affect the learning rate in the static agent?

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: As the transition probability increases, the learning rate initially rises and then declines.