https://wiki-spirit.win/api.php?action=feedcontributions&feedformat=atom&user=Cynhadramw 2026-05-28T22:36:10Z User contributions MediaWiki 1.42.3 https://wiki-spirit.win/index.php?title=Secrets_within_the_Client_Guide_to_Event_Organizers_in_Kuala_Lumpur_for_Liquid_State_Machines&diff=2146644 2026-05-28T17:50:35Z

<p>Cynhadramw: Created page with &quot;&lt;html&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Liquid computing systems are not traditional artificial neural networks. Standard neural networks process information in discrete layers. Liquid State Machines process information over time through a liquid filter. The time-varying reservoir is composed of spiking neurons. An LSM summit is not a typical neuromorphic showcase. It needs to cover neural dynamics (leaky integrate-and-fire, Izhikevich), liquid behaviour, output layer...&quot;</p> <hr /> <div>&lt;html&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Liquid computing systems are not traditional artificial neural networks. Standard neural networks process information in discrete layers. Liquid State Machines process information over time through a liquid filter. The time-varying reservoir is composed of spiking neurons. An LSM summit is not a typical neuromorphic showcase. It needs to cover neural dynamics (leaky integrate-and-fire, Izhikevich), liquid behaviour, output layer learning, and pulse representation.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Businesses assessing coordinators in Klang Valley for Liquid State Machine events|for LSM summits|for liquid computing gatherings have specific technical requirements|have particular demonstration needs|must ask targeted questions.&lt;/p&gt;&lt;h2&gt; The Difference between &quot;Spiking&quot; and &quot;Liquid Dynamics&quot;&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Some planners might present neuromorphic computing. An SNN is not automatically an LSM. The key feature of an LSM is the dynamic pool characteristic: the transformation from input to liquid layer has fading memory.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; A representative from once told me: “A vendor claimed a Liquid State Machine demo. They showed spikes. I asked 'what is the liquid filter?' They looked confused. 'We have spikes,' they said. 'That is not enough,' I said. 'A simple feedforward SNN also has spikes. What makes yours a liquid?' They had no answer. They were using 'Liquid State Machine' as a buzzword. Now we ask for a separation property demonstration.”&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Pose these questions to coordinators: Do you validate both the separation and approximation properties of your liquid &lt;a href=&quot;https://www.4shared.com/office/o-s2hEv2fa/pdf-54878-67018.html&quot;&gt;event coordinator&lt;/a&gt; layer.&lt;/p&gt;&lt;p&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/frHt-DmldXE&quot; width=&quot;560&quot; height=&quot;315&quot; style=&quot;border: none;&quot; allowfullscreen=&quot;&quot; &gt;&lt;/iframe&gt;&lt;/p&gt;&lt;h2&gt; The Readout Training: Simple but Powerful&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; In a proper Liquid State Machine, only the output connections are learned. The time-varying reservoir is unchanging and arbitrary.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; A neuromorphic researcher in KL posted: “I attended an LSM event where the presenter trained the entire network using backpropagation through time. I asked 'why are you training the liquid?' He said 'it improves performance.' I said 'then it is not an LSM. It is just a recurrent neural network. You are using the term incorrectly.' He had no response. The event was misleading. Now I always ask: 'Do you train only the readout?'”&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Talk through with your coordinator: Does your LSM learn only the output connections, or does it also adjust liquid parameters.&lt;/p&gt;&lt;p&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/T12mA9h1VRs&quot; width=&quot;560&quot; height=&quot;315&quot; style=&quot;border: none;&quot; allowfullscreen=&quot;&quot; &gt;&lt;/iframe&gt;&lt;/p&gt;&lt;p&gt; &lt;img src=&quot;https://i.ytimg.com/vi/r63eeaKKDSw/hq720_2.jpg&quot; style=&quot;max-width:500px;height:auto;&quot; &gt;&lt;/img&gt;&lt;/p&gt;&lt;h2&gt; The Difference between &quot;Spiking&quot; and &quot;Biologically Plausible&quot;&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; The dynamic pool in liquid computing can use|may employ|might utilize distinct spike-generating models. Leaky Integrate-and-Fire (LIF) is common. Izhikevich neurons provide more biological plausibility.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Inquire with planners: What spiking neuron type does your liquid implement (LIF, Izhikevich, Hodgkin-Huxley, or alternative).&lt;/p&gt;&lt;p&gt; &lt;img src=&quot;https://i.ytimg.com/vi/vFrQzB_BR_4/hq720.jpg&quot; style=&quot;max-width:500px;height:auto;&quot; &gt;&lt;/img&gt;&lt;/p&gt;&lt;h2&gt; The Difference between &quot;Accepts Spikes&quot; and &quot;Accepts Real Data&quot;&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; An LSM operates on spike trains. Real information (visual, auditory, measurement data) must be transformed into pulse sequences.&lt;/p&gt;&lt;p&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/4DNxgPYKJdA&quot; width=&quot;560&quot; height=&quot;315&quot; style=&quot;border: none;&quot; allowfullscreen=&quot;&quot; &gt;&lt;/iframe&gt;&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Kollysphere agency advises showing the complete path from actual input to encoding to liquid to training to result&lt;/p&gt;&lt;p&gt; &lt;img src=&quot;https://i.ytimg.com/vi/ytbkhoi6JiU/hq720_2.jpg&quot; style=&quot;max-width:500px;height:auto;&quot; &gt;&lt;/img&gt;&lt;/p&gt;&lt;/html&gt;</div>

Cynhadramw