The podcast focuses on topics in theoretical/computational neuroscience and is primarily aimed at students and researchers in the field.
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On the molecular memory code - with Sam Gershman - #14
1:20:44
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The idea that memories are stored in molecules was popular in the middle of the 20th century. However, since the discovery of long-term potentiation (LTP) in the 1970s, the dominant view has been that our memories are stored in synapses, that is, in the connections between neurons. Today, there are signs that the interest in molecular memory is ret…
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On quantum biology - with Johnjoe McFadden - #13
1:17:20
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Is quantum physics important in determining how living systems, including brains, work? Today's guest is a professor of molecular genetics at the University of Surrey in England and explores this question in the book “Life at the edge: The coming of age of quantum biology”. In this “vintage” episode, recorded in late 2019, we talk about how quantum…
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On modeling of signaling pathways inside the neuron - with Avrama Blackwell - #12
1:31:40
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Most computational neuroscientists investigate electric dynamics in neurons or neural networks, but there is also computations going on inside neurons. Here the key dynamical variables are concentrations of numerous different molecules, and the signaling is typically done in cascades of chemical reactions, called signaling pathways. Today’s guest i…
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On synaptic learning rules for spiking neurons - with Friedemann Zenke - #11
1:30:44
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Today’s AI is largely based on supervised learning of neural networks using the backpropagation-of-error synaptic learning rule. This learning rule relies on differentiation of continuous activation functions and is thus not directly applicable to spiking neurons. Today’s guest has developed the algorithm SuperSpike to address the problem. He has a…
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On large-scale modeling of mouse primary visual cortex - with Anton Arkhipov - #10
2:02:08
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Over the last ten years or so, the MindScope project at the Allen Institute in Seattle has pursued an industrylab-like approach to study the mouse visual cortex in unprecedented detail using electrophysiology, optophysiology, optical imaging and electron microscopy. Together with collaborators at Allen, today’s guest has worked to integrate of thes…
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On origins of computational neuroscience and AI as scientific fields - with Terrence Sejnowski (vintage) - #9
1:55:27
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Today’s guest is a pioneer both in the fields of computational neuroscience and artificial intelligence (AI) and has had a front seat during their development. His many contributions include, for example, the invention of the Boltzmann machine with Ackley and Hinton in the mid 1980s. In this “vintage” episode recorded in late 2019 he describes the …
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On reverse engineering of the roundworm C.elegans - with Konrad Kording - #8
1:34:14
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Today’s guest has argued that the present dominant way of doing systems neuroscience in mammals (large-scale electric or optical recordings of neural activity combined with data analysis) will be inadequate for understanding how their brain works. Instead, he proposes to focus on the simple roundworm C.elegans with only 302 neurons and try to rever…
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On topological data analysis and Hopfield-like network models - with Carina Curto - #7
2:14:41
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Over the last decade topological analysis has been established as a new tool for analysis of spiking data. Today’s guest has been a pioneer in adapting this mathematical technique for use in our field and explains concepts and example applications. We also also talk about so-called threshold-linear network model, a generalization of Hopfield networ…
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On central pattern generators in the spinal cord - with Henrik Lindén - #6
1:26:33
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Not all interesting network activity occurs in cortex. Networks in the spinal cord, the long thin tubular structure extending downwards from the neck, is responsible for setting up rhythmic motor activity needed for moving around. How do these so-called central pattern generators work? Today’s guest has, together with colleagues in Copenhagen, deve…
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On how vision works - with Li Zhaoping - #5
1:21:57
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We know a lot about of how neurons in the primary visual cortex (V1) of mammals respond to visual stimuli. But how does the vast information contained in the spiking of millions of neurons in V1 give rise to our visual percepts? The guest’s theory is that V1 acts as a “saliency detector” directing the gaze to the most important object in the visual…
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On multi-area cortex models - with Sacha van Albada - #4
1:32:51
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A key goal of computational neuroscience is to build mathematical models linking single-neuron activity to systems-level activity. The guest has taken some bold steps in this direction by developing and exploring a multi-area model for the macaque visual cortex, and later also a model for the human cortex, using millions of simplified spiking neuro…
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On the neural code - with Arvind Kumar - #3
1:25:46
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It is widely thought that spikes (action potentials) are the main carrier of information in the brain. But what is the neural code, that is, what aspects of the spike trains carry the information? The detailed temporal structure or maybe only the average firing rate? And is there information in the correlation between spike trains in populations of…
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On biophysics of computation – with Christof Koch - #2
1:20:10
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Starting from the pioneering work of Hodgkin, Huxley and Rall in the 1950s and 60s, we have a well-founded biophysics-based mathematical understanding of how neurons integrate signals from other neurons and generate action potentials. Today’s guest wrote the classic book “Biophysics of Computation” on the subject in 1998. We discuss its contents, w…
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On models of the mind - with Grace Lindsay - #1
1:51:39
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The book “Models of the Mind” published in 2021 gives an excellent popular account of the history and questions of interest in theoretical neuroscience. I could think of no other person more suitable to invite for the inaugural episode of the podcast than its author Grace Lindsay. In the podcast we discuss highlights from the book as well as recent…
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