Mirko & Marcus’ paper is out in PLoS Computational Biology!
If grid cells encode non-spatial cognitive variables, they should be able to represent spaces of dimension greater than two.
Can grid cells construct unambiguous representations of higher-dimensional inputs without recurrent rewiring to form higher-dimensional grid responses, a cell-inefficient and inflexible mechanism?
We show how they could do so by combining low-dimensional random projections with “classical” two-dimensional hexagonal grid cell responses.
Without reconfiguration of the recurrent circuit, the same network can flexibly encode multiple variables of different dimensions while maximizing the coding range (per dimension) by automatically trading-off dimension with an exponentially large coding range.
This model achieves high efficiency and flexibility by combining two powerful concepts, modularity and mixed selectivity, in what we call “mixed modular coding”.
Abhranil’s work on the intrinsic difficulty of inferring connectivity from activity in strongly recurrent networks (read: highly correlated variables) just accepted for publication (Nat. Neurosci March 2020; bioRxiv, Jan 2019)!
We show that attempting causal inference in systems with strongly correlated variables will typically result in an overestimation of connectivity, drawing causal connections where there are none.
Even in fully observed systems, this failure to explain away correlations occurs when there is mismatch between the dynamical model generating the data and the statistical model doing the inference, an inevitable situation in the real world.
Thus, causal inference from activity is especially problemmatic in systems with strong correlations.
In short, not only are correlations not causation, correlations hurt causation.
The good news: We find that sampling data shortly after kicking the system far-out-of-equilibrium by low-dimensional suppressive drive could mitigate the inference problem without the need for inducing and tracking detailed high-dimensional (holographic) perturbations.
Manyi’s new paper on the capacity and combinatorics of place cell representations is on bioRxiv. Beautiful theoretical work from Samsonovich, Macnaughton, Battista, Monasson and others shows that recurrent models of place field generation are severely capacity-limited. What if place cells are largely feedforward-driven, computing coincidences between grid cell and landmark inputs? We show through analytical calculations and simulation that feedforward place cells can have a huge capacity. But at the same time, little flexibility or discretion in where to place their multiple fields, suggesting highly constrained firing geometries.
New paper by Rishi, Berk, and Biraj with collaborator Adrien Peyrache: The internal head direction system comprises literally a ring of sates! The ring is attractive and is invariant across waking and sleep, and fluctuations in state are dominated by extrinsic rather than intrinsic noise.
New paper (Nature Neuroscience) by Sean Trettel, John Trimper, Ernie Hwaun, Laura Colgin and Ila. (Also see the complementary paper by Gardner et al. from the Moser lab.) Congrats Sean, John, Ernie, and Laura!
The perils of inferring connectivity from activity:
New bioRxiv paper
(Jan 2019)on the difficulty of/systemic bias in inferring wiring diagrams from activity data in strongly connected networks.
To Leenoy Meshulam, new postdoc.
(Jan 2019) New paper at ICLR on more biologically plausible learning in recurrent neural networks: Kernel RNN Learning (KeRNL). Congrats Chris Roth and Ingmar Kanitscheider!
(Sept 2018) Thanks to UT for its support over 10 amazing years, and much excitement looking ahead to many at MIT.