In preparation for our #CCN2023@CogCompNeuro GAC next week, I’m going to do some polls here this week to take the temperature of the room. 🌡️
Very curious to see the range of answers so please pass it on 🔁🙏 and feel free to elaborate - we'll try to take any discussion into account at the workshop
Reconciling the dichotomy between Sherringtonian and Hopfieldian views on neural computations
Organizers & Speakers at CCN 2023:
Dongyan Lin, McGill University
Arna Ghosh, McGill University
Jonathan Cornford, McGill University
James Whittington, Stanford University
Tatiana Engel, Princeton
Two views on the cognitive brain - Nature Reviews Neuroscience
Neuroscience can explain cognition by considering single neurons and their connections (a ‘Sherringtonian’ view) or by considering neural spaces constructed by populations of neurons (a ‘Hopfieldian’ view). In this Perspective, Barack and Krakauer argue that the Hopfieldian view has the conceptual resources to explain cognition more fully the Sherringtonian view.
The best explanations of cognitive phenomena will involve circuits made up of particular neuron to neuron connections realized by specific neurons with fixed biophysical identities and utilizing particular neurotransmitters to pass signals between them.
The best explanations of cognitive phenomena will involve circuits made up of neuron to neuron connections realized by neurons with biophysical identities and utilizing neurotransmitters to pass signals between them.
The best explanations of cognitive phenomena will involve neural spaces that describe the massed activity of e.g. neural ensembles or brain regions, with a low-dimensional representational manifold embedded within them.
Explanations in terms of computations performed by networks of nodes with weighted connections and explanations in terms of representational spaces are
An explanation for a cognitive phenomenon that appeals to the statistics of neural connections (e.g. low-dimensional connectivity structure) or their intrinsic properties (e.g. mixture of E and I cells) is
Establishing connections between neural connectivity and low-dimensional representational manifolds is possible in _________ of the neural circuits that support cognition.
Manifold and circuit approaches to cognition are inseparable
Agree
Disagree
Poll ended at .
A unifying perspective on neural manifolds and circuits for cognition - PubMed
Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on …
The best explanations of cognitive phenomena will involve accounts of how they are learned, developed, or evolved, without necessarily specifying their implementation.
If learning to perform a cognitive task reliably produces a circuit in which neurons respond to single cognitive or task variables, this would support adopting a ________ view.
If learning to perform a cognitive task reliably produces a similar neural manifold, but possibly with different circuit implementations across networks, this would support adopting a ________ view.
If learning to perform a cognitive task produces circuits that perform similar cognitive operations using different neural manifolds, this would support adopting a ________ view.
If learning to perform a cognitive task reliably produces a similar neural manifold with similar circuit implementations across networks, this would support adopting a ________ view.
If cells that respond to single cognitive variables naturally emerge in neural circuits, and are useful to support cognition, this would support adopting a ______ view.
Sherringtonian
Hopfieldian
Either (depends on?…)
Neither
Poll ended at .
Disentanglement with Biological Constraints: A Theory of Functional Cell Types
Neurons in the brain are often finely tuned for specific task variables.
Moreover, such disentangled representations are highly sought after in machine
learning. Here we mathematically prove that simple biological constraints on
neurons, namely nonnegativity and energy efficiency in both activity and
weights, promote such sought after disentangled representations by enforcing
neurons to become selective for single factors of task variation. We
demonstrate these constraints lead to disentanglement in a variety of tasks and
architectures, including variational autoencoders. We also use this theory to
explain why the brain partitions its cells into distinct cell types such as
grid and object-vector cells, and also explain when the brain instead entangles
representations in response to entangled task factors. Overall, this work
provides a mathematical understanding of why single neurons in the brain often
represent single human-interpretable factors, and steps towards an
understanding task structure shapes the structure of brain representation.
Due to the details of their implementation, some cognitive phenomena will be best-explained from a Hopfieldian view while others will be best-explained from a Sherringtonian one.
Both Sherrintonian and Hopfieldian explanations could (in theory) involve a neural manifold, but differ in their expectations of the relationship between that manifold and individual neurons.
The Sherringtonian and Hopfieldian views 1) reflect competing scientific theories, which 2) could be compared based on their correspondence to data or ability to make accurate predictions.
The Sherringtonian and Hopfieldian views 1) reflect competing scientific theories, which 2) could be compared based on their internal coherence or coherence with other theories.
The Sherringtonian and Hopfieldian views 1) reflect competing scientific theories, which 2) could be compared based on their ease of use by e.g. scientists, engineers, or medical professionals.
Dan Levenstein
