https://wiki-spirit.win/api.php?action=feedcontributions&feedformat=atom&user=Hronourovy 2026-06-16T16:02:09Z User contributions MediaWiki 1.42.3 https://wiki-spirit.win/index.php?title=What_Venue_Sourcing_a_Client_Guide_to_Event_Organizers_in_Kuala_Lumpur_for_Liquid_State_Machines_Suggests&diff=2146608 2026-05-28T17:44:00Z

<p>Hronourovy: Created page with &quot;&lt;html&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; LSMs are not conventional deep learning models. Standard neural networks process information in discrete layers. Liquid computing systems convert sequential data through a time-varying reservoir. The dynamic pool is a recurrent SNN. An LSM summit differs from a conventional spiking neural network event. It must address neuron models (LIF, Izhikevich), liquid dynamics, readout training, and spike encoding.&lt;/p&gt;&lt;p class=&quot;ds-markdow...&quot;</p> <hr /> <div>&lt;html&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; LSMs are not conventional deep learning models. Standard neural networks process information in discrete layers. Liquid computing systems convert sequential data through a time-varying reservoir. The dynamic pool is a recurrent SNN. An LSM summit differs from a conventional spiking neural network event. It must address neuron models (LIF, Izhikevich), liquid dynamics, readout training, and spike encoding.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Organizations reviewing planners across the capital 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;p&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/uF4i9_7IQlI&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 Liquid Filter Demonstration: Temporal Integration&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Some planners might present neuromorphic computing. Spiking neurons do not guarantee liquid dynamics. The key feature of an LSM is the time-varying reservoir quality: the conversion from input to internal state has short-term retention.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; A coordinator from Kollysphere agency shared: “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 verify the approximation property (the readout can learn any function of the liquid state).&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 valid liquid computing system, 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?' &lt;a href=&quot;https://pin.it/3SdmM9535&quot;&gt;event coordinator&lt;/a&gt; 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&gt; &lt;iframe src=&quot;https://www.youtube.com/embed/0FNkrjVIcuk&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; Review with your planner: 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/D5p78TyDS8I&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; Why Not All Spiking Neurons Are Equal&lt;/h2&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; The liquid layer in an LSM 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&gt; &lt;img src=&quot;https://i.ytimg.com/vi/RI35E5ewBuI/hq720.jpg&quot; style=&quot;max-width:500px;height:auto;&quot; &gt;&lt;/img&gt;&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Ask event organizers in Kuala Lumpur: What spiking neuron type does your liquid implement (LIF, Izhikevich, Hodgkin-Huxley, or alternative).&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; A liquid state machine processes event sequences. Real inputs (pictures, sound, sensor values) must be encoded as spike trains.&lt;/p&gt;&lt;p class=&quot;ds-markdown-paragraph&quot; &gt; Professional LSM event planners suggest showing the complete path from actual input to encoding to liquid to training to result&lt;/p&gt;&lt;/html&gt;</div>

Hronourovy