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Rajeswaran P, Payeur A, Lajoie G, Orsborn AL. Assistive sensory-motor perturbations influence learned neural representations. bioRxiv 2024:2024.03.20.585972. [PMID: 38562772 PMCID: PMC10983972 DOI: 10.1101/2024.03.20.585972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.
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Affiliation(s)
| | - Alexandre Payeur
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Guillaume Lajoie
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Amy L. Orsborn
- University of Washington, Bioengineering, Seattle, 98115, USA
- University of Washington, Electrical and Computer Engineering, Seattle, 98115, USA
- Washington National Primate Research Center, Seattle, Washington, 98115, USA
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Harkin EF, Lynn MB, Payeur A, Boucher JF, Caya-Bissonnette L, Cyr D, Stewart C, Longtin A, Naud R, Béïque JC. Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework. eLife 2023; 12:72951. [PMID: 36655738 PMCID: PMC9977298 DOI: 10.7554/elife.72951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/19/2022] [Indexed: 01/20/2023] Open
Abstract
By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.
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Affiliation(s)
- Emerson F Harkin
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Michael B Lynn
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Alexandre Payeur
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
- Department of Physics, University of OttawaOttawaCanada
| | - Jean-François Boucher
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Léa Caya-Bissonnette
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Dominic Cyr
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - Chloe Stewart
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
| | - André Longtin
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
- Department of Physics, University of OttawaOttawaCanada
| | - Richard Naud
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
- Department of Physics, University of OttawaOttawaCanada
| | - Jean-Claude Béïque
- Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of OttawaOttawaCanada
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Payeur A, Guerguiev J, Zenke F, Richards BA, Naud R. Author Correction: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nat Neurosci 2021; 24:1780. [PMID: 34728832 DOI: 10.1038/s41593-021-00970-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Alexandre Payeur
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.,Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada.,Centre for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada.,University of Montréal and Mila, Montréal, QC, Canada
| | - Jordan Guerguiev
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Friedemann Zenke
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Blake A Richards
- Mila, Montréal, QC, Canada. .,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada. .,School of Computer Science, McGill University, Montréal, QC, Canada. .,Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, ON, Canada.
| | - Richard Naud
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada. .,Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada. .,Centre for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada. .,Department of Physics, University of Ottawa, Ottawa, ON, Canada.
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Abstract
The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.
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Affiliation(s)
- Ezekiel Williams
- grid.28046.380000 0001 2182 2255Department of Mathematics and Statistics, University of Ottawa, 150 Louis Pasteur, Ottawa, K1N 6N5 Canada
| | - Alexandre Payeur
- grid.28046.380000 0001 2182 2255University of Ottawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Rd., Ottawa, K1H 8M5 Canada
| | - Albert Gidon
- grid.7468.d0000 0001 2248 7639Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard Naud
- grid.28046.380000 0001 2182 2255University of Ottawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Rd., Ottawa, K1H 8M5 Canada ,grid.28046.380000 0001 2182 2255Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, K1N 6N5 Canada
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Payeur A, Guerguiev J, Zenke F, Richards BA, Naud R. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nat Neurosci 2021; 24:1010-1019. [PMID: 33986551 DOI: 10.1038/s41593-021-00857-x] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 04/15/2021] [Indexed: 01/25/2023]
Abstract
Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.
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Affiliation(s)
- Alexandre Payeur
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.,Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada.,Centre for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada.,University of Montréal and Mila, Montréal, QC, Canada
| | - Jordan Guerguiev
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Friedemann Zenke
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Blake A Richards
- Mila, Montréal, QC, Canada. .,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada. .,School of Computer Science, McGill University, Montréal, QC, Canada. .,Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, ON, Canada.
| | - Richard Naud
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada. .,Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada. .,Centre for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada. .,Department of Physics, University of Ottawa, Ottawa, ON, Canada.
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Abstract
Dendrites are much more than passive neuronal components. Mounting experimental evidence and decades of computational work have decisively shown that dendrites leverage a host of nonlinear biophysical phenomena and actively participate in sophisticated computations, at the level of the single neuron and at the level of the network. However, a coherent view of their processing power is still lacking and dendrites are largely neglected in neural network models. Here, we describe four classes of dendritic information processing and delineate their implications at the algorithmic level. We propose that beyond the well-known spatiotemporal filtering of their inputs, dendrites are capable of selecting, routing and multiplexing information. By separating dendritic processing from axonal outputs, neuron networks gain a degree of freedom with implications for perception and learning.
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Affiliation(s)
- Alexandre Payeur
- Ottawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Neuroscience, University of Ottawa, Canada
| | - Jean-Claude Béïque
- Ottawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Neuroscience, University of Ottawa, Canada
| | - Richard Naud
- Ottawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Neuroscience, University of Ottawa, Canada; Department of Physics, University of Ottawa, 150 Louis Pasteur Pet, Ottawa, ON, K1N 6N5, Canada.
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Payeur A, Maler L, Longtin A. Oscillatorylike behavior in feedforward neuronal networks. Phys Rev E Stat Nonlin Soft Matter Phys 2015; 92:012703. [PMID: 26274199 DOI: 10.1103/physreve.92.012703] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Indexed: 06/04/2023]
Abstract
We demonstrate how rhythmic activity can arise in neural networks from feedforward rather than recurrent circuitry and, in so doing, we provide a mechanism capable of explaining the temporal decorrelation of γ-band oscillations. We compare the spiking activity of a delayed recurrent network of inhibitory neurons with that of a feedforward network with the same neural properties and axonal delays. Paradoxically, these very different connectivities can yield very similar spike-train statistics in response to correlated input. This happens when neurons are noisy and axonal delays are short. A Taylor expansion of the feedback network's susceptibility-or frequency-dependent gain function-can then be stopped at first order to a good approximation, thus matching the feedforward net's susceptibility. The feedback network is known to display oscillations; these oscillations imply that the spiking activity of the population is felt by all neurons within the network, leading to direct spike correlations in a given neuron. On the other hand, in the output layer of the feedforward net, the interaction between the external drive and the delayed feedforward projection of this drive by the input layer causes indirect spike correlations: spikes fired by a given output layer neuron are correlated only through the activity of the input layer neurons. High noise and short delays partially bridge the gap between these two types of correlation, yielding similar spike-train statistics for both networks. This similarity is even stronger when the delay is distributed, as confirmed by linear response theory.
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Affiliation(s)
- Alexandre Payeur
- Department of Physics, University of Ottawa, 150 Louis-Pasteur, Ottawa, Canada K1N 6N5
| | - Leonard Maler
- Department of Cell and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Canada K1H 8M5
| | - André Longtin
- Department of Physics, University of Ottawa, 150 Louis-Pasteur, Ottawa, Canada K1N 6N5 and Department of Cell and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Canada K1H 8M5
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Mejias JF, Payeur A, Selin E, Maler L, Longin A. Gain control via feedforward inhibition in noisy and delayed neural circuits. BMC Neurosci 2014. [PMCID: PMC4124989 DOI: 10.1186/1471-2202-15-s1-p111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Payeur A, Maler L, Longtin A. Brain rhythms from delayed interaction of fluctuations. BMC Neurosci 2014. [PMCID: PMC4124992 DOI: 10.1186/1471-2202-15-s1-p114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Mejias JF, Payeur A, Selin E, Maler L, Longtin A. Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays. Front Comput Neurosci 2014; 8:19. [PMID: 24616694 PMCID: PMC3934558 DOI: 10.3389/fncom.2014.00019] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 02/08/2014] [Indexed: 11/30/2022] Open
Abstract
The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry-also known as "open-loop feedback"-, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different regimes of gain control: subtractive, divisive, and non-monotonic. Subtractive gain control was obtained when noise is very low in the network. Also, it was possible to change from divisive to non-monotonic gain control by simply modulating the strength of the feedforward inhibition, which may be achieved via long-term synaptic plasticity. The particular case of divisive gain control has been previously observed in vivo in weakly electric fish. These gain control regimes were robust to the presence of temporal delays in the inhibitory feedforward pathway, which were found to linearize the input-to-output mappings (or f-I curves) via a novel variability-increasing mechanism. Our findings highlight the feedforward-induced gain control analyzed here as a highly versatile mechanism of information gating in the brain.
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Affiliation(s)
- Jorge F. Mejias
- Department of Physics, University of OttawaOttawa, ON, Canada
- Center for Neural Science, New York UniversityNew York, NY, USA
| | | | - Erik Selin
- Department of Physics, University of OttawaOttawa, ON, Canada
| | - Leonard Maler
- Department of Cell and Molecular Medicine, University of OttawaOttawa, ON, Canada
| | - André Longtin
- Department of Physics, University of OttawaOttawa, ON, Canada
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Mejias JF, Payeur A, Selin E, Maler L, Longin A. Divisive and non-monotonic gain control in open-loop neural circuits. BMC Neurosci 2013. [PMCID: PMC3704409 DOI: 10.1186/1471-2202-14-s1-p248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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