Reversal potential

<h2 id="equilibrium-potential">Equilibrium potential</h2>
The equilibrium potential for an ion is the <a href="/facts/Membrane_potential/DpNG14c7">membrane potential</a> at which there is no net movement of the ion.<a class="footnote-ref" id="fnref:4" href="#fn:4">4</a><a class="footnote-ref" id="fnref:5" href="#fn:5">5</a><a class="footnote-ref" id="fnref:6" href="#fn:6">6</a> The flow of any inorganic ion, such as <a href="/facts/Sodium/eYuSPu2V">Na+</a> or <a href="/facts/Potassium/FWPwS7Rj">K+</a>, through an <a href="/facts/Ion_channel/A46LARFH">ion channel</a> (since membranes are normally impermeable to ions) is driven by the <a href="/facts/Electrochemical_gradient/IEfjjoIL">electrochemical gradient</a> for that ion.<a class="footnote-ref" id="fnref:7" href="#fn:7">7</a><a class="footnote-ref" id="fnref:8" href="#fn:8">8</a><a class="footnote-ref" id="fnref:9" href="#fn:9">9</a><a class="footnote-ref" id="fnref:10" href="#fn:10">10</a> This gradient consists of two parts, the difference in the concentration of that ion across the membrane, and the voltage gradient.<a class="footnote-ref" id="fnref:11" href="#fn:11">11</a> When these two influences balance each other, the electrochemical gradient for the ion is zero and there is no net flow of the ion through the channel; this also translates to no current across the membrane so long as only one ionic species is involved.<a class="footnote-ref" id="fnref:12" href="#fn:12">12</a><a class="footnote-ref" id="fnref:13" href="#fn:13">13</a><a class="footnote-ref" id="fnref:14" href="#fn:14">14</a><a class="footnote-ref" id="fnref:15" href="#fn:15">15</a><a class="footnote-ref" id="fnref:16" href="#fn:16">16</a> The voltage gradient at which this equilibrium is reached is the equilibrium potential for the ion and it can be calculated from the <a href="/facts/Nernst_equation/bCMCjfqx">Nernst equation</a>.<a class="footnote-ref" id="fnref:17" href="#fn:17">17</a><a class="footnote-ref" id="fnref:18" href="#fn:18">18</a><a class="footnote-ref" id="fnref:19" href="#fn:19">19</a><a class="footnote-ref" id="fnref:20" href="#fn:20">20</a> 

<h3>Mathematical models and the driving force</h3>
We can consider as an example a positively charged ion, such as <a href="/facts/Potassium/FWPwS7Rj">K+</a>, and a negatively charged membrane, as it is commonly the case in most organisms.<a class="footnote-ref" id="fnref:21" href="#fn:21">21</a><a class="footnote-ref" id="fnref:22" href="#fn:22">22</a> The membrane voltage opposes the flow of the potassium ions out of the cell and the ions can leave the interior of the cell only if they have sufficient thermal energy to overcome the energy barrier produced by the negative membrane voltage.<a class="footnote-ref" id="fnref:23" href="#fn:23">23</a> However, this biasing effect can be overcome by an opposing concentration gradient if the interior concentration is high enough which favours the potassium ions leaving the cell.<a class="footnote-ref" id="fnref:24" href="#fn:24">24</a> 
An important concept related to the equilibrium potential is the driving force. Driving force is simply defined as the difference between the actual membrane potential and an ion's equilibrium potential 
 
 
 
 
 V
 
 
 m
 
 
 
 −
 
 E
 
 
 i
 
 
 
  
 
 
 {\displaystyle V_{\mathrm {m} }-E_{\mathrm {i} }\ }
 
where 
 
 
 
 
 E
 
 
 i
 
 
 
  
 
 
 {\displaystyle E_{\mathrm {i} }\ }
 
refers to the equilibrium potential for a specific ion.<a class="footnote-ref" id="fnref:25" href="#fn:25">25</a> Relatedly, the membrane current per unit area due to the type 
 
 
 
 i
 
 
 {\displaystyle i}
 
 ion channel is given by the following equation:

i
          
            
              i
            
          
        
        =
        
          g
          
            
              i
            
          
        
        
          (
          
            
              V
              
                
                  m
                
              
            
            −
            
              E
              
                
                  i
                
              
            
          
          )
        
      
    
    {\displaystyle i_{\mathrm {i} }=g_{\mathrm {i} }\left(V_{\mathrm {m} }-E_{\mathrm {i} }\right)}

where 
 
 
 
 
 V
 
 
 m
 
 
 
 −
 
 E
 
 
 i
 
 
 
  
 
 
 {\displaystyle V_{\mathrm {m} }-E_{\mathrm {i} }\ }
 
 is the driving force and 
 
 
 
 
 g
 
 
 i
 
 
 
 
 
 {\displaystyle g_{\mathrm {i} }}
 
 is the <a href="/facts/Electrical_resistivity_and_conductivity/akyCKvMy">specific conductance</a>, or conductance per unit area.<a class="footnote-ref" id="fnref:26" href="#fn:26">26</a> Note that the ionic current will be zero if the membrane is impermeable to that ion in question or if the membrane voltage is exactly equal to the equilibrium potential of that ion.<a class="footnote-ref" id="fnref:27" href="#fn:27">27</a>

<h2 id="use-in-research">Use in research</h2>
When Vm is at the reversal potential for an event such as a synaptic potential (Vm − Erev is equal to 0), the identity of the ions that flow during an EPC can be deduced by comparing the reversal potential of the EPC to the equilibrium potential for various ions. For instance several excitatory <a href="/facts/Ionotropic/gN9RkNC2">ionotropic</a> ligand-gated <a href="/facts/Neurotransmitter/LlpYFSU3">neurotransmitter</a> <a href="/facts/Receptor_(biochemistry)/tjTAC76a">receptors</a> including <a href="/facts/Glutamate_receptor/MGwpHHUA">glutamate receptors</a> (<a href="/facts/AMPA/jeQWeMVw">AMPA</a>, <a href="/facts/NMDA/7jnfxMBU">NMDA</a>, and <a href="/facts/Kainate/8Rk1cJFV">kainate</a>), <a href="/facts/Nicotinic/k2s2pPHr">nicotinic</a> <a href="/facts/Acetylcholine/v1d3wRUd">acetylcholine</a> (nACh), and <a href="/facts/Serotonin/EzY58q4a">serotonin</a> (5-HT3) receptors are nonselective cation channels that pass Na+ and K+ in nearly equal proportions, giving the reversal potential close to zero. The inhibitory ionotropic ligand-gated neurotransmitter receptors that carry <a href="/facts/Chloride/m9NUaUDR">Cl−</a>, such as <a href="/facts/GABA/vRTeQQUG">GABA</a>A and <a href="/facts/Glycine/3nXI1hqW">glycine</a> receptors, have reversal potentials close to the resting potential (approximately −70 mV) in neurons.<a class="footnote-ref" id="fnref:28" href="#fn:28">28</a>
This line of reasoning led to the development of experiments (by Akira Takeuchi and Noriko Takeuchi in 1960) that demonstrated that acetylcholine-activated ion channels are approximately equally permeable to Na+ and K+ ions. The experiment was performed by lowering the external Na+ concentration, which lowers (makes more negative) the Na+ equilibrium potential and produces a negative shift in reversal potential. Conversely, increasing the external K+ concentration raises (makes more positive) the K+ equilibrium potential and produces a positive shift in reversal potential.<a class="footnote-ref" id="fnref:29" href="#fn:29">29</a> A general expression for reversal potential of synaptic events, including for decreases in conductance, has been derived.<a class="footnote-ref" id="fnref:30" href="#fn:30">30</a>

<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/Electrochemical_potential/UpMRy1qq">Electrochemical potential</a></li>
<li><a href="/facts/Membrane_potential/DpNG14c7">Cell potential</a></li>
<li><a href="/facts/Goldman_equation/DFFHkVBM">Goldman equation</a></li></ul>

<h2 id="external-links">External links</h2>
<ul><li><a href="http://www.nernstgoldman.physiology.arizona.edu/">Nernst/Goldman Equation Simulator</a></li>
<li><a href="http://www.physiologyweb.com/calculators/nernst_potential_calculator.html">Nernst Equation Calculator</a></li>
<li><a href="http://www.physiologyweb.com/calculators/ghk_equation_calculator.html">Goldman-Hodgkin-Katz Equation Calculator</a></li>
<li><a href="http://www.physiologyweb.com/calculators/electrochemical_driving_force_calculator.html">Electrochemical Driving Force Calculator</a></li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1">Squire, Larry; Berg, Darwin (2014). Fundamental Neuroscience (4th ed.). Academic Press. pp. 93–97. ISBN 978-0-12-385870-2. <a href="978-0-12-385870-2" target="_blank">978-0-12-385870-2</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></li>
<li id="fn:2">Purves, Dale; et al. (2017). Neuroscience (6th ed.). Sinauer Associates. pp. 39–106. ISBN 9781605353807. <a href="9781605353807" target="_blank">9781605353807</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></li>
<li id="fn:3">Mark, Bear; Connors, Barry (2016). Neuroscience: Exploring the Brain (4th Enhanced ed.). Jones & Barlet Learning. p. 64-127. ISBN 9781284211283. <a href="9781284211283" target="_blank">9781284211283</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></li>
<li id="fn:4">Squire, Larry; Berg, Darwin (2014). Fundamental Neuroscience (4th ed.). Academic Press. pp. 93–97. ISBN 978-0-12-385870-2. <a href="978-0-12-385870-2" target="_blank">978-0-12-385870-2</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></li>
<li id="fn:5">Purves, Dale; et al. (2017). Neuroscience (6th ed.). Sinauer Associates. pp. 39–106. ISBN 9781605353807. <a href="9781605353807" target="_blank">9781605353807</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></li>
<li id="fn:6">Mark, Bear; Connors, Barry (2016). Neuroscience: Exploring the Brain (4th Enhanced ed.). Jones & Barlet Learning. p. 64-127. ISBN 9781284211283. <a href="9781284211283" target="_blank">9781284211283</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></li>
<li id="fn:7">Squire, Larry; Berg, Darwin (2014). Fundamental Neuroscience (4th ed.). Academic Press. pp. 93–97. ISBN 978-0-12-385870-2. <a href="978-0-12-385870-2" target="_blank">978-0-12-385870-2</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></li>
<li id="fn:8">Purves, Dale; et al. (2017). Neuroscience (6th ed.). Sinauer Associates. pp. 39–106. ISBN 9781605353807. <a href="9781605353807" target="_blank">9781605353807</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></li>
<li id="fn:9">Mark, Bear; Connors, Barry (2016). Neuroscience: Exploring the Brain (4th Enhanced ed.). Jones & Barlet Learning. p. 64-127. ISBN 9781284211283. <a href="9781284211283" target="_blank">9781284211283</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></li>
<li id="fn:10">Alberts, Bruce (2015). Molecular biology of the cell (6th ed.). New York, NY. pp. 615–616. ISBN 978-0-8153-4432-2. OCLC 887605755.{{cite book}}: CS1 maint: location missing publisher (link) <a href="978-0-8153-4432-2" target="_blank">978-0-8153-4432-2</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></li>
<li id="fn:11">Alberts, Bruce (2015). Molecular biology of the cell (6th ed.). New York, NY. pp. 615–616. ISBN 978-0-8153-4432-2. OCLC 887605755.{{cite book}}: CS1 maint: location missing publisher (link) <a href="978-0-8153-4432-2" target="_blank">978-0-8153-4432-2</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></li>
<li id="fn:12">Squire, Larry; Berg, Darwin (2014). Fundamental Neuroscience (4th ed.). Academic Press. pp. 93–97. ISBN 978-0-12-385870-2. <a href="978-0-12-385870-2" target="_blank">978-0-12-385870-2</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></li>
<li id="fn:13">Purves, Dale; et al. (2017). Neuroscience (6th ed.). Sinauer Associates. pp. 39–106. ISBN 9781605353807. <a href="9781605353807" target="_blank">9781605353807</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></li>
<li id="fn:14">Mark, Bear; Connors, Barry (2016). Neuroscience: Exploring the Brain (4th Enhanced ed.). Jones & Barlet Learning. p. 64-127. ISBN 9781284211283. <a href="9781284211283" target="_blank">9781284211283</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></li>
<li id="fn:15">Alberts, Bruce (2015). Molecular biology of the cell (6th ed.). New York, NY. pp. 615–616. ISBN 978-0-8153-4432-2. OCLC 887605755.{{cite book}}: CS1 maint: location missing publisher (link) <a href="978-0-8153-4432-2" target="_blank">978-0-8153-4432-2</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></li>
<li id="fn:16">Abbott, Laurence F. (2001). Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems. Peter Dayan. Cambridge: MIT Press. pp. 158–160. ISBN 978-0-262-31142-7. OCLC 1225555646. <a href="978-0-262-31142-7" target="_blank">978-0-262-31142-7</a> <a href="#fnref:16" class="footnote-back-ref">↩</a></li>
<li id="fn:17">Squire, Larry; Berg, Darwin (2014). Fundamental Neuroscience (4th ed.). Academic Press. pp. 93–97. ISBN 978-0-12-385870-2. <a href="978-0-12-385870-2" target="_blank">978-0-12-385870-2</a> <a href="#fnref:17" class="footnote-back-ref">↩</a></li>
<li id="fn:18">Purves, Dale; et al. (2017). Neuroscience (6th ed.). Sinauer Associates. pp. 39–106. ISBN 9781605353807. <a href="9781605353807" target="_blank">9781605353807</a> <a href="#fnref:18" class="footnote-back-ref">↩</a></li>
<li id="fn:19">Mark, Bear; Connors, Barry (2016). Neuroscience: Exploring the Brain (4th Enhanced ed.). Jones & Barlet Learning. p. 64-127. ISBN 9781284211283. <a href="9781284211283" target="_blank">9781284211283</a> <a href="#fnref:19" class="footnote-back-ref">↩</a></li>
<li id="fn:20">Alberts, Bruce (2015). Molecular biology of the cell (6th ed.). New York, NY. pp. 615–616. ISBN 978-0-8153-4432-2. OCLC 887605755.{{cite book}}: CS1 maint: location missing publisher (link) <a href="978-0-8153-4432-2" target="_blank">978-0-8153-4432-2</a> <a href="#fnref:20" class="footnote-back-ref">↩</a></li>
<li id="fn:21">Alberts, Bruce (2015). Molecular biology of the cell (6th ed.). New York, NY. pp. 615–616. ISBN 978-0-8153-4432-2. OCLC 887605755.{{cite book}}: CS1 maint: location missing publisher (link) <a href="978-0-8153-4432-2" target="_blank">978-0-8153-4432-2</a> <a href="#fnref:21" class="footnote-back-ref">↩</a></li>
<li id="fn:22">Abbott, Laurence F. (2001). Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems. Peter Dayan. Cambridge: MIT Press. pp. 158–160. ISBN 978-0-262-31142-7. OCLC 1225555646. <a href="978-0-262-31142-7" target="_blank">978-0-262-31142-7</a> <a href="#fnref:22" class="footnote-back-ref">↩</a></li>
<li id="fn:23">Abbott, Laurence F. (2001). Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems. Peter Dayan. Cambridge: MIT Press. pp. 158–160. ISBN 978-0-262-31142-7. OCLC 1225555646. <a href="978-0-262-31142-7" target="_blank">978-0-262-31142-7</a> <a href="#fnref:23" class="footnote-back-ref">↩</a></li>
<li id="fn:24">Abbott, Laurence F. (2001). Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems. Peter Dayan. Cambridge: MIT Press. pp. 158–160. ISBN 978-0-262-31142-7. OCLC 1225555646. <a href="978-0-262-31142-7" target="_blank">978-0-262-31142-7</a> <a href="#fnref:24" class="footnote-back-ref">↩</a></li>
<li id="fn:25">Abbott, Laurence F. (2001). Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems. Peter Dayan. Cambridge: MIT Press. pp. 158–160. ISBN 978-0-262-31142-7. OCLC 1225555646. <a href="978-0-262-31142-7" target="_blank">978-0-262-31142-7</a> <a href="#fnref:25" class="footnote-back-ref">↩</a></li>
<li id="fn:26">Abbott, Laurence F. (2001). Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems. Peter Dayan. Cambridge: MIT Press. pp. 158–160. ISBN 978-0-262-31142-7. OCLC 1225555646. <a href="978-0-262-31142-7" target="_blank">978-0-262-31142-7</a> <a href="#fnref:26" class="footnote-back-ref">↩</a></li>
<li id="fn:27">Abbott, Laurence F. (2001). Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems. Peter Dayan. Cambridge: MIT Press. pp. 158–160. ISBN 978-0-262-31142-7. OCLC 1225555646. <a href="978-0-262-31142-7" target="_blank">978-0-262-31142-7</a> <a href="#fnref:27" class="footnote-back-ref">↩</a></li>
<li id="fn:28">Purves, Dale; et al. (2017). Neuroscience (6th ed.). Sinauer Associates. pp. 39–106. ISBN 9781605353807. <a href="9781605353807" target="_blank">9781605353807</a> <a href="#fnref:28" class="footnote-back-ref">↩</a></li>
<li id="fn:29">Purves, Dale; et al. (2017). Neuroscience (6th ed.). Sinauer Associates. pp. 39–106. ISBN 9781605353807. <a href="9781605353807" target="_blank">9781605353807</a> <a href="#fnref:29" class="footnote-back-ref">↩</a></li>
<li id="fn:30">Brown JE, Muller KJ, Murray G (October 14, 1971). "Reversal potential for an electrophysiological event generated by conductance changes: mathematical analysis". Science. 174 (4006): 318. Bibcode:1971Sci...174..318B. doi:10.1126/science.174.4006.318. PMID 5119107. S2CID 34404730. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:30" class="footnote-back-ref">↩</a></li>
</ol>

Reversal potential open-in-new

Reversal potential