Avsnitt
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In 1982 John Hopfield published the paper "Neural networks and physical systems with emergent collective computational abilities" describing a simple network model functioning as an associative and content-addressable memory.
The paper started a new subfield in computational neuroscience and led to the influx of numerous theoretical scientists, in particular physicists, to the field.
The podcast guest wrote his PhD thesis on the model in the early 1990s, and we talk about the history and present impact of the model.
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The leading theory for learning and memorization in the brain is that learning is provided by synaptic learning rules and memories stored in synaptic weights between neurons.
But this is for long-term memory. What about short-term, or working, memory where objects are kept in memory for only a few seconds?
The traditional theory held that here the mechanism is different, namely persistent firing of select neurons in areas such as prefrontal cortex. But this view is challenged by recent synapse-based models explored by today’s guest and others.
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Saknas det avsnitt?
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In September Paul Middlebrooks, the producer of the podcast BrainInspired, and I were both on a neuro-AI workshop on a coast liner cruising the Norwegian fjords.
We decided to make two joint podcasts with some of the participants where we discuss the role of AI in neuroscience.
In this second part we discuss the topic with Cristina Savin and Tim Vogels and round off with a brief discussion with Mikkel Lepperød, the main organizer of the workshop, about what he learned from the workshop.
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In September Paul Middlebrooks, the producer of the podcast BrainInspired, and I were both on a neuro-AI workshop on a coast liner cruising the Norwegian fjords.
We decided to make two joint podcasts with some of the participants where we discuss the role of AI in neuroscience.
In this first part we talk with Mikkel Lepperod, the main organizer about the goal of the workshop, and with Ken Harris and Andreas Tolias about how AI has affected their research neuroscientists and their thoughts about the future of neuro-AI.
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Most of what we have learned about the functioning of the living brain has come from extracellular electrical recordings, like the measurement of spikes, LFP, ECoG and EEG signals.
And most analysis of these recordings has been statistical, looking for correlations between the recorded signals and what the animal/human is doing or being exposed to.
However, starting with the neuron rather than the data, these electrical brain signals can also be computed from biophysics-based forward models, and this is topic of this podcast.
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The most prominent visual characteristic of neurons is their dendrites.
Even more than 100 years after their first observation by Cajal, their function is not fully understood. Biophysical modeling based on cable theory is a key research tool for exploring putative functions, and today’s guest is one the leading researchers in this field.
We talk about of passive and active dendrites, the kind of filtering of synaptic inputs they support, the key role of synapse placements, and how the inclusion of dendrites may facilitate AI.
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The greatest mystery of all is why a group of atoms, like the ones constituting me, can feel anything. The mind-brain problem has puzzled philosophers for millennia.
Thanks to pioneers like Christof Koch, consciousness studies have recently become a legitimate field of scientific inquiry.
In this vintage episode, recorded in February 2021, we discuss many aspects of the phenomenon, including an intriguing candidate theory: Integrated Information Theory.
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Computational neuroscientists use many software tools, and NEURON has become the leading tool for biophysical modeling of neurons and neural network.
Today’s guest has been the leading developer of NEURON since the infancy almost 50 years ago.
We talk about how the tool got started and the development up until today’s modern version of the software, including CoreNEURON optimized for parallel execution of large-scale network models on multicore supercomputers.
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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 returning, and the guest has presented a theory suggesting that molecular and synaptic memory might serve complementary needs for animals.
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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 physics is or may be key in photosynthesis, smelling, navigation, evolution and even thinking. And we also touch on development of new antibiotics, another expertise of McFadden.
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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 is an expert in this kind of modelling and is particularly interested in the signaling role of calcium.
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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 also recently developed a biologically more plausible learning rule based on self-supervised learning. We talk about both.
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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 these data into large-scale neural network, and in the podcast he talks about their ambitious endeavor.
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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 joint births of these adjacent scientific fields and outlines how they came about.
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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 reverse engineer it by means of optical stimulation and recordings, and modern machine-learning techniques.
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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 networks exhibiting a much richer dynamics, where Carina has done some exciting mathematical explorations
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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, 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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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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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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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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