Fos ensembles encode and shape stable spatial maps in the hippocampus DOI Creative Commons
Noah L. Pettit, Ee-Lynn Yap, Michael E. Greenberg

et al.

Nature, Journal Year: 2022, Volume and Issue: 609(7926), P. 327 - 334

Published: Aug. 24, 2022

Abstract In the hippocampus, spatial maps are formed by place cells while contextual memories thought to be encoded as engrams 1–6 . Engrams typically identified expression of immediate early gene Fos , but little is known about neural activity patterns that drive, and shaped by, in behaving animals 7–10 Thus, it unclear whether Fos-expressing hippocampal neurons also encode correlates with affects specific features code 11 Here we measured CA1 calcium imaging monitoring induction mice performing a hippocampus-dependent learning task virtual reality. We find high form ensembles highly correlated activity, exhibit reliable fields evenly tile environment have more stable tuning across days than nearby non-Fos-induced cells. Comparing neighbouring without function using sparse genetic loss-of-function approach, disrupted less decreased selectivity lower across-day stability. Our results demonstrate Fos-induced contribute codes encoding accurate, spatially uniform itself has causal role shaping these codes. may therefore link two key aspects function: for underlie cognitive maps.

Language: Английский

A deep learning framework for neuroscience DOI
Blake A. Richards, Timothy Lillicrap,

Philippe Beaudoin

et al.

Nature Neuroscience, Journal Year: 2019, Volume and Issue: 22(11), P. 1761 - 1770

Published: Oct. 28, 2019

Language: Английский

Citations

906
Backpropagation and the brain DOI
Timothy Lillicrap,

Adam Santoro,

Luke Marris

et al.

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 21(6), P. 335 - 346

Published: April 17, 2020

Language: Английский

Citations

720
Synaptic Plasticity Forms and Functions DOI
Jeffrey C. Magee, Christine Grienberger

Annual Review of Neuroscience, Journal Year: 2020, Volume and Issue: 43(1), P. 95 - 117

Published: Feb. 20, 2020

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and memory. Computational engineering work corroborate power through directed adjustment weights. Here we review fundamental elements four broadly categorized forms synaptic plasticity discuss their functional capabilities limitations. Although standard, correlation-based, Hebbian primary focus neuroscientists for decades, it is inherently limited. Three-factor rules supplement with neuromodulation eligibility traces, while true supervised types go even further by adding objectives instructive signals. Finally, a recently discovered hippocampal form combines above elements, leaving behind requirement. We suggest that effort to determine neural basis adaptive behavior could benefit from renewed experimental theoretical investigation more powerful plasticity.

Language: Английский

Citations

600
The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions DOI Creative Commons
Joseph J. Paton, Dean V. Buonomano

Neuron, Journal Year: 2018, Volume and Issue: 98(4), P. 687 - 705

Published: May 1, 2018

Language: Английский

Citations

390
Parallel emergence of stable and dynamic memory engrams in the hippocampus DOI
Thomas Hainmueller, Marlene Bartos

Nature, Journal Year: 2018, Volume and Issue: 558(7709), P. 292 - 296

Published: June 1, 2018

Language: Английский

Citations

377
Dentate gyrus circuits for encoding, retrieval and discrimination of episodic memories DOI
Thomas Hainmueller, Marlene Bartos

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 21(3), P. 153 - 168

Published: Feb. 10, 2020

Language: Английский

Citations

377
Towards deep learning with segregated dendrites DOI Creative Commons
Jordan Guerguiev, Timothy Lillicrap, Blake A. Richards

et al.

eLife, Journal Year: 2017, Volume and Issue: 6

Published: Dec. 5, 2017

Deep learning has led to significant advances in artificial intelligence, part, by adopting strategies motivated neurophysiology. However, it is unclear whether deep could occur the real brain. Here, we show that a algorithm utilizes multi-compartment neurons might help us understand how neocortex optimizes cost functions. Like neocortical pyramidal neurons, our model receive sensory information and higher-order feedback electrotonically segregated compartments. Thanks this segregation, different layers of network can coordinate synaptic weight updates. As result, learns categorize images better than single layer network. Furthermore, takes advantage multilayer architectures identify useful representations-the hallmark learning. This work demonstrates be achieved using dendritic compartments, which may explain morphology neurons.

Language: Английский

Citations

361
The connectome of the adult Drosophila mushroom body provides insights into function DOI Creative Commons
Feng Li, Jack Lindsey, Elizabeth C. Marin

et al.

eLife, Journal Year: 2020, Volume and Issue: 9

Published: Dec. 14, 2020

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) well positioned for developing and testing such approach due to its conserved architecture, recently completed dense connectome, extensive prior experimental studies of roles learning, memory, activity regulation. Here, we identify new components MB circuit Drosophila, including visual input output neurons (MBONs) with direct connections descending neurons. We find unexpected structure sensory inputs, transfer information different modalities MBONs, modulation that dopaminergic (DANs). provide insights into circuitry used integrate outputs, between central complex inputs DANs, feedback MBONs. Our results a foundation further theoretical work.

Language: Английский

Citations

348
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules DOI Creative Commons
Wulfram Gerstner, Marco P. Lehmann, Vasiliki Liakoni

et al.

Frontiers in Neural Circuits, Journal Year: 2018, Volume and Issue: 12

Published: July 31, 2018

Most elementary behaviors such as moving the arm to grasp an object or walking into next room explore a museum evolve on time scale of seconds; in contrast, neuronal action potentials occur few milliseconds. Learning rules brain must therefore bridge gap between these two different scales. Modern theories synaptic plasticity have postulated that co-activation pre- and postsynaptic neurons sets flag at synapse, called eligibility trace, leads weight change only if additional factor is present while set. This third factor, signaling reward, punishment, surprise, novelty, could be implemented by phasic activity neuromodulators specific inputs special events. While theoretical framework has been developed over last decades, experimental evidence support traces seconds collected during years. Here we review, context three-factor plasticity, four key experiments role combination with biological implementation neoHebbian learning rules.

Language: Английский

Citations

281
Control of synaptic plasticity in deep cortical networks DOI
Pieter R. Roelfsema, Anthony Holtmaat

Nature reviews. Neuroscience, Journal Year: 2018, Volume and Issue: 19(3), P. 166 - 180

Published: Feb. 16, 2018

Language: Английский

Citations

257