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PODCAST

Theoretical Neuroscience Podcast

The podcast focuses on topics in theoretical/computational neuroscience and is primarily aimed at students and researchers in the field.

science life sciences

All Episodes

01:20:44

On the molecular memory code - with Sam Gershman - #14

Theoretical Neuroscience Podcast ·

2024/06/22

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 returning, and the guest has presented a theory suggesting that molecular and synaptic memory might serve complementary needs for animals.

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01:17:20

On quantum biology - with Johnjoe McFadden - #13

Theoretical Neuroscience Podcast ·

2024/06/08

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 physics is or may be key in photosynthesis, smelling, navigation, evolution and even thinking. And we also touch on development of new antibiotics,

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01:31:40

On modeling of signaling pathways inside the neuron -...

Theoretical Neuroscience Podcast ·

2024/05/25

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 is an expert in this kind of modelling and is particularly interested in the signaling role of calcium.

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01:30:44

On synaptic learning rules for spiking neurons - with...

Theoretical Neuroscience Podcast ·

2024/04/27

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 also recently developed a biologically more plausible learning rule based on self-supervised learning. We talk about both.

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02:02:08

On large-scale modeling of mouse primary visual...

Theoretical Neuroscience Podcast ·

2024/03/30

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 these data into large-scale neural network, and in the podcast he talks about their ambitious endeavor.

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01:55:27

On origins of computational neuroscience and AI as...

Theoretical Neuroscience Podcast ·

2024/03/16

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 joint births of these adjacent scientific fields and outlines how they came about.

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01:34:14

On reverse engineering of the roundworm C.elegans -...

Theoretical Neuroscience Podcast ·

2024/03/02

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 reverse engineer it by means of optical stimulation and recordings, and modern machine-learning techniques.

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02:14:41

On topological data analysis and Hopfield-like...

Theoretical Neuroscience Podcast ·

2024/02/03

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 networks exhibiting a much richer dynamics, where Carina has done some exciting mathematical explorations

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01:26:33

On central pattern generators in the spinal cord -...

Theoretical Neuroscience Podcast ·

2024/01/06

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, developed a neuron-based network theory for how these rhythmic oscillations may arise even without pace-maker neurons driving the collective.

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01:21:57

On how vision works - with Li Zhaoping - #5

Theoretical Neuroscience Podcast ·

2023/12/09

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 scene. Then V1 in collaboration with higher visual areas determines what this object is in an iterative feedforward-feedback loop.

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01:32:51

On multi-area cortex models - with Sacha van Albada - #4

Theoretical Neuroscience Podcast ·

2023/11/18

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 neuron models. We discuss the many design choices, the challenge of running the models, and what has been learned so far.

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01:25:46

On the neural code - with Arvind Kumar - #3

Theoretical Neuroscience Podcast ·

2023/11/04

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 similar neurons? The guest has thought about these and other coding questions throughout his career.

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01:20:10

On biophysics of computation – with Christof Koch - #2

Theoretical Neuroscience Podcast ·

2023/10/28

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, what has changed in the last 25 years, and also touch on his other main research interest: consciousness research.

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01:51:39

On models of the mind - with Grace Lindsay - #1

Theoretical Neuroscience Podcast ·

2023/10/13

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 developments and the future of our field.

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34 results

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