Episode #45
On neuromorphic computing - with Mihai Petrovici - #45
Sep 23, 20261h 42mS1
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science
Hosted by Gaute Einevoll · science · EN · 44 episodes
The podcast focuses on topics in theoretical/computational neuroscience and is primarily aimed at students and researchers in the field.
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Signup to Generate a PitchTheoretical Neuroscience Podcast is a science podcast hosted by Gaute Einevoll, with 44 episodes on record and a Required Pod Score of 81.
Gaute Einevoll hosts Theoretical Neuroscience Podcast, a science show with 44 episodes published.
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Episode #45
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Episode #44
For neurons to function, the appropriate ion-channel proteins must be present where they are needed, including in distal dendrites and axon terminals. T his requires energy, and the guest's group has developed a mechanistic mathematical model to investigate how neurons can reduce this cost by optimizing where proteins are synthesized within the cell—locally or at more distant sites. The model's predictions agree with experimental findings, suggesting that energy optimization is a fundamental operating principle of neurons.
Theoretical Neuroscience Podcast
The prominent and colorful neuroscientist Valentino Braitenberg was born 100 years ago. He co-founded the Max Planck Institute of Biological Cybernetics in Tübingen in Germany, where he made seminal contributions to neuroanatomy, synthetic psychology, and theories for cerebellar, fly vision and cortical function. He was celebrated at the recent Braitenberg*100 symposium which I attended together with today's guest. Ad Aertsen is an outstanding computational neuroscientist and worked with Braitenberg back in the days.
Episode #42
A bursting neuron can maintain its firing-pattern identity throughout an animal's life, even though the ion-channel proteins underlying this identity are turned over on the timescale of days. Today's guest has proposed that neuronal identities are stored in the specific protein production rules, which are regulated by intracellular calcium signaling. And how can animals reliably perform a learned task for weeks, even when the underlying neural representation drifts over time, so-called representational drift?
Episode #41
<p >Most neural network models till date have assumed all neurons to be identical, or at least that all neurons within a population are identical. In reality, no two neurons are completely the same.</p> <p >Is this due to unavoidable "biological noise" that the nervous system has to cope with, or can it be a useful feature included by design?</p> <p >The guest co-wrote the recent paper "How heterogeneity shapes dynamics and computation in the brain" addressing this question.</p>
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