UH Biocomputation Group - networkshttp://biocomputation.herts.ac.uk/2019-11-21T10:14:04+00:00Efficient codes and balanced networks2019-11-21T10:14:04+00:002019-11-21T10:14:04+00:00Emil Dmitruktag:biocomputation.herts.ac.uk,2019-11-21:/2019/11/21/efficient-codes-and-balanced-networks.html<p class="first last">Samuel Sutton journal club session where he will talk about the paper "Efficient codes and balanced networks".</p>
<p>This week on Journal Club session Samuel Sutton will talk about the paper "Efficient codes and balanced networks".</p>
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<p>Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition
is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a
millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with
experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and
computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates
corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of
magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to
construct high-dimensional population codes and learn complex functions of their inputs.</p>
<p>Papers:</p>
<ul class="simple">
<li>Sophie Denève & Christian K Machens <a class="reference external" href="https://www.nature.com/articles/nn.4243">"Efficient codes and balanced networks"</a> Nat Neurosci 19, 375–382 (2016) doi:10.1038/nn.4243</li>
</ul>
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<p><strong>Date:</strong> 22/11/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D449</p>