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Majumder S, Hirokawa K, Yang Z, Paletzki R, Gerfen CR, Fontolan L, Romani S, Jain A, Yasuda R, Inagaki HK. Cell-type-specific plasticity shapes neocortical dynamics for motor learning. bioRxiv 2023:2023.08.09.552699. [PMID: 37609277 PMCID: PMC10441538 DOI: 10.1101/2023.08.09.552699] [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: 08/24/2023]
Abstract
Neocortical spiking dynamics control aspects of behavior, yet how these dynamics emerge during motor learning remains elusive. Activity-dependent synaptic plasticity is likely a key mechanism, as it reconfigures network architectures that govern neural dynamics. Here, we examined how the mouse premotor cortex acquires its well-characterized neural dynamics that control movement timing, specifically lick timing. To probe the role of synaptic plasticity, we have genetically manipulated proteins essential for major forms of synaptic plasticity, Ca2+/calmodulin-dependent protein kinase II (CaMKII) and Cofilin, in a region and cell-type-specific manner. Transient inactivation of CaMKII in the premotor cortex blocked learning of new lick timing without affecting the execution of learned action or ongoing spiking activity. Furthermore, among the major glutamatergic neurons in the premotor cortex, CaMKII and Cofilin activity in pyramidal tract (PT) neurons, but not intratelencephalic (IT) neurons, is necessary for learning. High-density electrophysiology in the premotor cortex uncovered that neural dynamics anticipating licks are progressively shaped during learning, which explains the change in lick timing. Such reconfiguration in behaviorally relevant dynamics is impeded by CaMKII manipulation in PT neurons. Altogether, the activity of plasticity-related proteins in PT neurons plays a central role in sculpting neocortical dynamics to learn new behavior.
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Affiliation(s)
- Shouvik Majumder
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA
| | - Koichi Hirokawa
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA
| | - Zidan Yang
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA
| | - Ronald Paletzki
- National Institute of Mental Health, Bethesda, MD 20814, USA
| | | | - Lorenzo Fontolan
- Turing Centre for Living Systems, Aix- Marseille University, INSERM, INMED U1249, Marseille, France
- Janelia Research Campus, HHMI, Ashburn VA 20147, USA
| | - Sandro Romani
- Janelia Research Campus, HHMI, Ashburn VA 20147, USA
| | - Anant Jain
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA
| | - Ryohei Yasuda
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA
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Daie K, Fontolan L, Druckmann S, Svoboda K. Feedforward amplification in recurrent networks underlies paradoxical neural coding. bioRxiv 2023:2023.08.04.552026. [PMID: 37577599 PMCID: PMC10418196 DOI: 10.1101/2023.08.04.552026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The activity of single neurons encodes behavioral variables, such as sensory stimuli (Hubel & Wiesel 1959) and behavioral choice (Britten et al. 1992; Guo et al. 2014), but their influence on behavior is often mysterious. We estimated the influence of a unit of neural activity on behavioral choice from recordings in anterior lateral motor cortex (ALM) in mice performing a memory-guided movement task (H. K. Inagaki et al. 2018). Choice selectivity grew as it flowed through a sequence of directions in activity space. Early directions carried little selectivity but were predicted to have a large behavioral influence, while late directions carried large selectivity and little behavioral influence. Consequently, estimated behavioral influence was only weakly correlated with choice selectivity; a large proportion of neurons selective for one choice were predicted to influence choice in the opposite direction. These results were consistent with models in which recurrent circuits produce feedforward amplification (Goldman 2009; Ganguli et al. 2008; Murphy & Miller 2009) so that small amplitude signals along early directions are amplified to produce low-dimensional choice selectivity along the late directions, and behavior. Targeted photostimulation experiments (Daie et al. 2021b) revealed that activity along the early directions triggered sequential activity along the later directions and caused predictable behavioral biases. These results demonstrate the existence of an amplifying feedforward dynamical motif in the motor cortex, explain paradoxical responses to perturbation experiments (Chettih & Harvey 2019; Daie et al. 2021b; Russell et al. 2019), and reveal behavioral relevance of small amplitude neural dynamics.
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Abstract
The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.
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Affiliation(s)
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Kayvon Daie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
| | - Arseny Finkelstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Lorenzo Fontolan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
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Finkelstein A, Fontolan L, Economo MN, Li N, Romani S, Svoboda K. Publisher Correction: Attractor dynamics gate cortical information flow during decision-making. Nat Neurosci 2021; 24:897. [PMID: 34012095 DOI: 10.1038/s41593-021-00869-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Arseny Finkelstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lorenzo Fontolan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Michael N Economo
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nuo Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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Inagaki HK, Fontolan L, Romani S, Svoboda K. Discrete attractor dynamics underlies persistent activity in the frontal cortex. Nature 2019; 566:212-217. [PMID: 30728503 DOI: 10.1038/s41586-019-0919-7] [Citation(s) in RCA: 148] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/07/2019] [Indexed: 12/24/2022]
Abstract
Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.
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Hyafil A, Giraud AL, Fontolan L, Gutkin B. Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. Trends Neurosci 2016; 38:725-740. [PMID: 26549886 DOI: 10.1016/j.tins.2015.09.001] [Citation(s) in RCA: 223] [Impact Index Per Article: 27.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 08/14/2015] [Accepted: 09/01/2015] [Indexed: 10/22/2022]
Abstract
Neural oscillations are ubiquitously observed in the mammalian brain, but it has proven difficult to tie oscillatory patterns to specific cognitive operations. Notably, the coupling between neural oscillations at different timescales has recently received much attention, both from experimentalists and theoreticians. We review the mechanisms underlying various forms of this cross-frequency coupling. We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics. Finally, we associate these mechanisms with several putative functions of cross-frequency coupling, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions.
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Affiliation(s)
- Alexandre Hyafil
- Universitat Pompeu Fabra, Theoretical and Computational Neuroscience, Roc Boronat 138, 08018 Barcelona, Spain; Research Unit, Parc Sanitari Sant Joan de Déu and Universitat de Barcelona, Esplugues de Llobregat, Barcelona, Spain.
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Campus Biotech, 9 chemin des Mines, 1211 Geneva, Switzerland
| | - Lorenzo Fontolan
- Department of Neuroscience, University of Geneva, Campus Biotech, 9 chemin des Mines, 1211 Geneva, Switzerland
| | - Boris Gutkin
- Group for Neural Theory, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité 960, Département d'Etudes Cognitives, Ecole Normale Supérieure, 29 rue d'Ulm, 75005 Paris, France; Centre for Cognition and Decision Making, National Research University Higher School of Economics, Myasnitskaya Street 20, Moscow 101000, Russia
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Hyafil A, Fontolan L, Kabdebon C, Gutkin B, Giraud AL. Speech encoding by coupled cortical theta and gamma oscillations. eLife 2015; 4:e06213. [PMID: 26023831 PMCID: PMC4480273 DOI: 10.7554/elife.06213] [Citation(s) in RCA: 99] [Impact Index Per Article: 11.0] [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: 12/23/2014] [Accepted: 05/28/2015] [Indexed: 12/11/2022] Open
Abstract
Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta–gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding. DOI:http://dx.doi.org/10.7554/eLife.06213.001 Some people speak twice as fast as others, while people with different accents pronounce the same words in different ways. However, despite these differences between speakers, humans can usually follow spoken language with remarkable ease. The different elements of speech have different frequencies: the typical frequency for syllables, for example, is about four syllables per second in speech. Phonemes, which are the smallest elements of speech, appear at a higher frequency. However, these elements are all transmitted at the same time, so the brain needs to be able to process them simultaneously. The auditory cortex, the part of the brain that processes sound, produces various ‘waves’ of electrical activity, and these waves also have a characteristic frequency (which is the number of bursts of neural activity per second). One type of brain wave, called the theta rhythm, has a frequency of three to eight bursts per second, which is similar to the typical frequency of syllables in speech, and the frequency of another brain wave, the gamma rhythm, is similar to the frequency of phonemes. It has been suggested that these two brain waves may have a central role in our ability to follow speech, but to date there has been no direct evidence to support this theory. Hyafil et al. have now used computer models of neural oscillations to explore this theory. Their simulations show that, as predicted, the theta rhythm tracks the syllables in spoken language, while the gamma rhythm encodes the specific features of each phoneme. Moreover, the two rhythms work together to establish the sequence of phonemes that makes up each syllable. These findings will support the development of improved speech recognition technologies. DOI:http://dx.doi.org/10.7554/eLife.06213.002
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Affiliation(s)
- Alexandre Hyafil
- INSERM U960, Group for Neural Theory, Département d'Etudes Cognitives, Ecole Normale Supérieure, Paris, France
| | - Lorenzo Fontolan
- INSERM U960, Group for Neural Theory, Département d'Etudes Cognitives, Ecole Normale Supérieure, Paris, France
| | - Claire Kabdebon
- INSERM U960, Group for Neural Theory, Département d'Etudes Cognitives, Ecole Normale Supérieure, Paris, France
| | - Boris Gutkin
- INSERM U960, Group for Neural Theory, Département d'Etudes Cognitives, Ecole Normale Supérieure, Paris, France
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Geneva, Switzerland
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Fontolan L, Morillon B, Liegeois-Chauvel C, Giraud AL. The contribution of frequency-specific activity to hierarchical information processing in the human auditory cortex. Nat Commun 2014; 5:4694. [PMID: 25178489 PMCID: PMC4164774 DOI: 10.1038/ncomms5694] [Citation(s) in RCA: 153] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Accepted: 07/14/2014] [Indexed: 01/21/2023] Open
Abstract
The fact that feed-forward and top-down propagation of sensory information use distinct frequency bands is an appealing assumption for which evidence remains scarce. Here we obtain human depth recordings from two auditory cortical regions in both hemispheres, while subjects listen to sentences, and show that information travels in each direction using separate frequency channels. Bottom-up and top-down propagation dominates in γ- and δ–β (<40 Hz) bands, respectively. The predominance of low frequencies for top-down information transfer is confirmed by cross-regional frequency coupling, which indicates that the power of γ-activity in A1 is modulated by the phase of δ–β activity sampled from association auditory cortex (AAC). This cross-regional coupling effect is absent in the opposite direction. Finally, we show that information transfer does not proceed continuously but by time windows where bottom-up or top-down processing alternatively dominates. These findings suggest that the brain uses both frequency- and time-division multiplexing to optimize directional information transfer. Sensory processing relies on information transfer in cortical hierarchies. Using depth recordings of neural activity obtained while individuals with epilepsy listen to spoken sentences, the authors show that ascending and descending information is propagated between cortical regions through distinct neural frequencies.
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Affiliation(s)
- L Fontolan
- 1] Department of Neuroscience, University of Geneva, Biotech Campus, 9, Chemin des Mines, Geneva 1211, Switzerland [2]
| | - B Morillon
- 1] Department of Psychiatry, Columbia University Medical Center, New York, New York 10032, USA [2]
| | - C Liegeois-Chauvel
- INSERM U1106-Institut de Neurosciences des Systèmes, Université Aix-Marseille, Marseille 13005, France
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Biotech Campus, 9, Chemin des Mines, Geneva 1211, Switzerland
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Fontolan L, Krupa M, Hyafil A, Gutkin B. Analytical insights on theta-gamma coupled neural oscillators. J Math Neurosci 2013; 3:16. [PMID: 23945442 PMCID: PMC3848946 DOI: 10.1186/2190-8567-3-16] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Accepted: 06/13/2013] [Indexed: 06/02/2023]
Abstract
In this paper, we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30-100 Hz) range, coupled to a delta/theta frequency (1-8 Hz) neural oscillator. Using analytical and semianalytical methods, we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e., windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case, we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation, we can compute numerically the time to the first gamma spike, and then use singular perturbation theory to find successive spike times. On the other hand, in the excitable condition, we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes.
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Affiliation(s)
- Lorenzo Fontolan
- Department of Fundamental Neurosciences, CMU, University of Geneva, 1 rue Michel Servet, 1211, Geneva, Switzerland
| | - Maciej Krupa
- INRIA Paris-Rocquencourt Research Centre, Domaine de Voluceau BP 105, 78153, Le Chesnay, France
| | - Alexandre Hyafil
- Group for Neural Theory, Départment des Etudes Cognitives, Ecole Normale Supérieure, 5 rue d’Ulm, 75005, Paris, France
| | - Boris Gutkin
- Group for Neural Theory, Départment des Etudes Cognitives, Ecole Normale Supérieure, 5 rue d’Ulm, 75005, Paris, France
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