<div dir="ltr"><div class="gmail_quote gmail_quote_container"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><div dir="ltr" class="gmail_attr" style="text-align:center"><img src="cid:ii_l25sulo01" alt="VVTNS.png" width="202" height="202" style="margin-right:0px"><br></div><div dir="ltr" class="gmail_attr" style="text-align:center"><a href="https://streaklinks.com/A9c7PbbpKY7PxB6PaAJWGD3-/https%3A%2F%2Fwww.wwtns.online" target="_blank" style="font-size:large">https://www.wwtns.online</a><span style="font-size:large"> - on twitter: wwtns@TheoreticalWide - on Youtube</span></div><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div style="text-align:center"><br></div><div style="text-align:center"><font size="4">New on the VVTNS YouTube Channel </font></div><div style="text-align:center"><font size="4"><a href="https://www.wwtns.online/past-seminars-2025-2026" target="_blank">https://www.wwtns.online/past-seminars-2025-2026</a></font></div><div style="text-align:center"><b style="background-color:initial;font-family:spinnaker,sans-serif;font-size:20px;letter-spacing:0em"><font color="#000000"><br></font></b></div><div style="text-align:center"><b style="background-color:initial;font-family:spinnaker,sans-serif;font-size:20px;letter-spacing:0em"><font color="#000000">The hippocampus, spatial planning, generative models </font></b><b style="background-color:initial;font-family:spinnaker,sans-serif;font-size:20px;letter-spacing:0em"><font color="#000000">and memory consolidation</font></b></div><div style="text-align:center"><span style="color:rgb(13,13,13);font-family:Roboto,Noto,sans-serif;font-size:large;text-align:start"><br></span></div><div style="text-align:center"><span style="color:rgb(13,13,13);font-family:Roboto,Noto,sans-serif;font-size:large;text-align:start">lecture delivered on March 4,  2026  by</span></div><div style="text-align:center"><div><span style="font-family:Roboto,Noto,sans-serif;text-align:start"><font size="4" color="#000000"><br></font></span></div><div><p style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;padding:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-size-adjust:none;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:18px;line-height:1.6em;font-family:spinnaker,sans-serif;margin:0px"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px;font-family:helvetica-w01-roman,helvetica-w02-roman,helvetica-lt-w10-roman,sans-serif"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px"><font color="#000000">Lior Fox</font></span></span></span></p><p style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;padding:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-size-adjust:none;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:18px;line-height:1.6em;font-family:spinnaker,sans-serif;margin:0px"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px;font-family:helvetica-w01-roman,helvetica-w02-roman,helvetica-lt-w10-roman,sans-serif"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px"><font color="#000000">Gatsby Computational Neuroscience Unit</font></span></span></span></p></div><div><br></div></div><div style="text-align:left"><font color="#000000"><b>Abstract:  </b><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">Useful internal representations should explain the patterns of regularities and </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">dependencies among observations. Probabilistic graphical models promise a </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">principled way to uncover latent factors as such, but they are hard to scale to  </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">handle high-dimensional sensory observations and complicated  </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">dependency structures. Neural-networks, on the other hand, excel at  </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">approximating complicated high-dimensional functions, but their internal  </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">representations do not easily lend themselves to a probabilistic interpretation.  </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">Despite some successes, a general unified approach is still missing for i</span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">ntegrating the two approaches. </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">I will describe a novel approach towards merging adaptive neural-network </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">components into a probabilistic framework, based on three core ideas. The first </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">is to train a set of networks to collectively perform inference, leveraging the </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">ability of pattern-recognition methods to amortise complicated transformations. </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">The second is to constrain the way in which the outputs of these networks are </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">interpreted, transformed, and combined together. These constraints, together </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">with the learning objective itself, are derived directly from probabilistic </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">considerations encoded in a graphical model. Finally, the third core idea is </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">that of recognition-parameterization, allowing the inference ("recognition") </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">procedure to directly define the model itself, without requiring an explicit </span></span><span style="background:0px 0px;vertical-align:baseline;border:0px;outline:0px;margin:0px;padding:0px;font-family:spinnaker,sans-serif;font-size:14px;text-align:justify"><span style="vertical-align:baseline;background:0px 0px;border:0px;outline:0px;margin:0px;padding:0px">"generative" decoder.</span></span></font></div><div style="text-align:left"><span style="font-size:14px;text-align:justify"><font color="#000000">.</font></span></div><div style="text-align:center"><font color="#000000"><br></font></div><div style="text-align:center"><p style="text-align:left;background:transparent;border:0px;outline:0px;padding:0px;vertical-align:baseline;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-size-adjust:none;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;line-height:1.6em;font-family:spinnaker,sans-serif;margin:0px"><i style="background-color:transparent;font-family:Arial,Helvetica,sans-serif"><font color="#000000"><span style="font-family:helvetica-w01-light,helvetica-w02-light,sans-serif;text-align:justify"><b>About V<font color="#500050">VTNS</font><font color="#500050"> :</font></b><font color="#500050"> </font>Launched as the World Wide  Theoretical Neuroscience Seminar (WWTN</span><span style="font-family:helvetica-w01-light,helvetica-w02-light,sans-serif;text-align:justify">S) in</span><span style="color:rgb(80,0,80);font-family:helvetica-w01-light,helvetica-w02-light,sans-serif;text-align:justify"> N</span></font><span style="color:rgb(0,0,0);font-family:helvetica-w01-light,helvetica-w02-light,sans-serif;text-align:justify">ovember 2020 and renamed in homage to Carl van Vreeswijk in Memoriam (April 20, 2022), Speakers have the occasion to talk about theoretical aspects of their work which cannot be discussed in a setting where the majority of the audience consists of experimentalists. The seminars, </span></i><i style="background-color:transparent;font-family:Arial,Helvetica,sans-serif;color:rgb(80,0,80)"><span style="color:rgb(0,0,0);font-family:helvetica-w01-light,helvetica-w02-light,sans-serif;text-align:justify">held on Wednesdays at 11 am ET,</span></i><i style="background-color:transparent;font-family:Arial,Helvetica,sans-serif;color:rgb(80,0,80)"><span style="color:rgb(0,0,0);font-family:helvetica-w01-light,helvetica-w02-light,sans-serif;text-align:justify">  are 45-50 min long followed by a discussion. The talks are recorded with authorization of the speaker and are available to everybody on our YouTube channel.</span></i></p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div id="m_6482959609533230826m_2078987493363524274m_2385834065060985535m_-6046398934232627254m_-5827452734716430257m_7045304362081612403m_4179726376297834025m_5509523003637554824m_-436179444694456296m_-7681628874649059375m_7750187461389672445m_9094229232797745075m_-1462542758380322657m_7936782669738885075m_2132234319318737290m_3082520148829658712m_6732243839182647882m_-287356451416637581m_9199497142594163290m_3123758975499956944m_-4285543598660325014m_3406622003034096663m_-4434917348957094547m_6996923250378584115m_4339855878333796038m_2618317786332564474m_-6182961172059790657m_-6761930163476888199m_-1789248717287360482m_-7753075062499186717mt-signature"><p style="margin:0px;padding:0px;border:0px;outline:0px;vertical-align:baseline;background:transparent;line-height:1.6em"><br></p><p style="margin:0px;padding:0px;border:0px;outline:0px;vertical-align:baseline;background:transparent;font-size:18px;line-height:1.6em;color:rgb(0,0,0);text-align:center"><br></p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
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