(function () { function textOf(el) { return (el && el.textContent || "").replace(/\s+/g, " ").trim(); } function resolveItemUrl() { var match = window.location.search.match(/[?&]item=(\d+)/); if (!match) { return null; } return window.location.origin + "/a-" + match[1] + "/"; } function loadAccordion() { var url = resolveItemUrl(); if (!url) { return Promise.resolve(null); } return new Promise(function (resolve, reject) { var xhr = new XMLHttpRequest(); xhr.open("GET", url, true); xhr.onload = function () { if (xhr.status < 200 || xhr.status >= 400) { reject(new Error("item page " + xhr.status)); return; } var doc = new DOMParser().parseFromString(xhr.responseText, "text/html"); resolve(doc.querySelector(".phywe-item-accordion")); }; xhr.onerror = function () { reject(new Error("item page network error")); }; xhr.send(); }); } function parseItemBlocks(scope, withQuantity) { if (!scope) { return []; } var rows = scope.querySelectorAll(".itemslist .row.itemblocks"); var items = []; for (var i = 0; i < rows.length; i++) { var cols = rows[i].children; if (!cols.length) { continue; } var nameLink = cols[0].querySelector("a"); var numberLink = cols[1] ? cols[1].querySelector("a") : null; var item = { name: textOf(nameLink || cols[0]), mappedTo: textOf(numberLink) }; if (withQuantity && cols[2]) { var qty = parseInt(textOf(cols[2]).replace(/[^\d]/g, ""), 10); item.quantity = isNaN(qty) ? textOf(cols[2]) : String(qty); } if (item.name) { items.push(item); } } return items; } function fillTable(tableId, items, withQuantity) { var table = document.getElementById(tableId); if (!table || !items.length) { return; } var tbody = table.querySelector("tbody"); for (var i = 0; i < items.length; i++) { var tr = document.createElement("tr"); var nameTd = document.createElement("td"); nameTd.style.width = "80%"; nameTd.textContent = items[i].name; tr.appendChild(nameTd); var numberTd = document.createElement("td"); numberTd.textContent = items[i].mappedTo; tr.appendChild(numberTd); if (withQuantity) { var qtyTd = document.createElement("td"); qtyTd.textContent = items[i].quantity || ""; tr.appendChild(qtyTd); } tbody.appendChild(tr); } var wrapper = table.parentElement; while (wrapper && wrapper.className.indexOf("printtablediv") === -1) { wrapper = wrapper.parentElement; } if (wrapper) { wrapper.style.display = ""; } } function stepTitle(stepEl) { if (!stepEl) { return ""; } var titleEl = stepEl.querySelector(".card-header button div") || stepEl.querySelector(".card-header button"); return textOf(titleEl); } function setHeadline(tableId, title) { var headline = document.querySelector("#" + tableId + " .printtableheadline"); if (headline && title) { headline.textContent = title; } } function parseMedia(scope) { if (!scope) { return []; } var rows = scope.querySelectorAll(".itemslist .row.itemblocks"); var items = []; for (var i = 0; i < rows.length; i++) { var cols = rows[i].children; if (!cols.length) { continue; } var nameLink = cols[0].querySelector("a"); var fileLink = cols[1] ? cols[1].querySelector("a") : null; var item = { name: textOf(nameLink || cols[0]), mappedTo: textOf(fileLink || cols[1]), quantity: cols[2] ? textOf(cols[2]) : "" }; if (item.name) { items.push(item); } } return items; } function fillFromAccordion(accordion) { if (!accordion) { return; } var versucheStep = accordion.querySelector('[data-step="versuche"]'); var versuche = parseItemBlocks(versucheStep, false); setHeadline("print-versuche", stepTitle(versucheStep)); fillTable("print-versuche", versuche, false); var lieferumfangStep = accordion.querySelector('[data-step="lieferumfang"]'); var lieferumfang = parseItemBlocks(lieferumfangStep, true); fillTable("print-lieferumfang", lieferumfang, true); var zubehoer = accordion.querySelector('[data-step="zubehoer"]'); var sections = zubehoer ? zubehoer.querySelectorAll(".card-body") : []; var notwendig = []; var empfohlen = []; for (var i = 0; i < sections.length; i++) { var items = parseItemBlocks(sections[i], false); if (sections[i].querySelector(".text-red, .icon-warning-black")) { notwendig = items; } else if (sections[i].querySelector(".text-green, .icon-Mehrwert_black")) { empfohlen = items; } } fillTable("print-zubehoer-notwendig", notwendig, false); fillTable("print-zubehoer-empfohlen", empfohlen, false); var mediaStep = accordion.querySelector('[data-step="media"]'); var media = parseMedia(mediaStep); setHeadline("print-media", stepTitle(mediaStep)); fillTable("print-media", media, true); } function start() { var itemUrl = resolveItemUrl(); if (!itemUrl) { setTimeout(function () { window.print(); }, 2000); return; } loadAccordion() .then(function (accordion) { fillFromAccordion(accordion); }) .catch(function (err) { if (window.console && console.warn) { console.warn("Accordion konnte nicht geladen werden:", resolveItemUrl(), err); } }) .then(function () { setTimeout(function () { window.print(); }, 500); }); } if (document.readyState === "loading") { document.addEventListener("DOMContentLoaded", start); } else { start(); } })();
 

Technical data


Nerve cell interactions with Cobra SMARTsense

Article no: P4010869
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Principle

Interactive learning and teaching system with two neurosimulators for experiments covering nerve cells and nerve cell interactions.

Benefits

  • Experience processes in the nerve cell and between nerve cells "hands-on"
  • Make all properties of a nerve cell easy to understand - action potential, membrane potential, functions of synapses (e.g. synaptic learning and forgetting)
  • Discover how nerve cells interact to study the conditioned reflex and synaptic learning
  • Scale up easily to perform further experiments studying neural networks (e.g. short-term memory) by adding additional modules
  • Ideal for projects at schools and for lab courses in the degree course Neurobiology

Tasks

  • When using one nerve cell: use the nerve function model to study the following aspects of a nerve cell: membrane potential, action potential, the different types of synapses.
  • When using two nerve cells: motoneuron signals with recurrent inhibition by Renshaw cell, motoneuron signals without recurrent inhibition, functional characteristics of Renshaw inhibition, lateral inhibition, contrast improvement, conditioned reflex, reversed stimulus succession does not bring about a conditioned reflex.

Learning objectives

  • When using one nerve cell: comparison between low and high threshold and stimulus levels, membrane time constant and low pass filtering, low-pass filtering, excitatory synapse, depolarisation, temporal summation, spatial summation, synaptic amplification by terminal branches, effect of decreasing stimulus, Hebbian synapse, synaptic learning and forgetting, inhibitory synapse, hyperpolarization, spacial inhibitory-excitatory summation, veto synapse.
  • When using two nerve cells: lateral inhibition, contrast improvement, nerve cell interaction, conditioned reflex, Renshaw inhibition, motoneuron.

The system can be upgraded with one or two additional neurosimulators to perform experiments about

  • neural networks (transient, i.e. phasic responses: focus on visual sense, body clock, short-term memory, special anatomical circuits: cerebral cortex and sensoric learning, functional characteristic of a triad).
  • complex neural networks (direction selectivity by unilateral inhibition, self-calibration of paired sensory channels).

Incl. data acquisition system Cobra SMARTsense for mobile devices (iOS and Android) and for Windows.

An oscilloscope is required to carry out the action potential experiment.

 


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