1
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Regele-Blasco E, Palmer LM. The plasticity of pyramidal neurons in the behaving brain. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230231. [PMID: 38853566 DOI: 10.1098/rstb.2023.0231] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/23/2024] [Indexed: 06/11/2024] Open
Abstract
Neurons are plastic. That is, they change their activity according to different behavioural conditions. This endows pyramidal neurons with an incredible computational power for the integration and processing of synaptic inputs. Plasticity can be investigated at different levels of investigation within a single neuron, from spines to dendrites, to synaptic input. Although most of our knowledge stems from the in vitro brain slice preparation, plasticity plays a vital role during behaviour by providing a flexible substrate for the execution of appropriate actions in our ever-changing environment. Owing to advances in recording techniques, the plasticity of neurons and the neural networks in which they are embedded is now beginning to be realized in the in vivo intact brain. This review focuses on the structural and functional synaptic plasticity of pyramidal neurons, with a specific focus on the latest developments from in vivo studies. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.
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Affiliation(s)
- Elena Regele-Blasco
- The Florey Institute of Neuroscience and Mental Health, The Florey Department of Neuroscience and Mental Health, University of Melbourne , Victoria 3052, Australia
| | - Lucy M Palmer
- The Florey Institute of Neuroscience and Mental Health, The Florey Department of Neuroscience and Mental Health, University of Melbourne , Victoria 3052, Australia
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2
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Quass GL, Rogalla MM, Ford AN, Apostolides PF. Mixed Representations of Sound and Action in the Auditory Midbrain. J Neurosci 2024; 44:e1831232024. [PMID: 38918064 PMCID: PMC11270520 DOI: 10.1523/jneurosci.1831-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 06/05/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
Linking sensory input and its consequences is a fundamental brain operation. During behavior, the neural activity of neocortical and limbic systems often reflects dynamic combinations of sensory and task-dependent variables, and these "mixed representations" are suggested to be important for perception, learning, and plasticity. However, the extent to which such integrative computations might occur outside of the forebrain is less clear. Here, we conduct cellular-resolution two-photon Ca2+ imaging in the superficial "shell" layers of the inferior colliculus (IC), as head-fixed mice of either sex perform a reward-based psychometric auditory task. We find that the activity of individual shell IC neurons jointly reflects auditory cues, mice's actions, and behavioral trial outcomes, such that trajectories of neural population activity diverge depending on mice's behavioral choice. Consequently, simple classifier models trained on shell IC neuron activity can predict trial-by-trial outcomes, even when training data are restricted to neural activity occurring prior to mice's instrumental actions. Thus, in behaving mice, auditory midbrain neurons transmit a population code that reflects a joint representation of sound, actions, and task-dependent variables.
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Affiliation(s)
- Gunnar L Quass
- Department of Otolaryngology-Head & Neck Surgery, Kresge Hearing Research Institute, University of Michigan Medical School, Ann Arbor, Michigan 48109
| | - Meike M Rogalla
- Department of Otolaryngology-Head & Neck Surgery, Kresge Hearing Research Institute, University of Michigan Medical School, Ann Arbor, Michigan 48109
| | - Alexander N Ford
- Department of Otolaryngology-Head & Neck Surgery, Kresge Hearing Research Institute, University of Michigan Medical School, Ann Arbor, Michigan 48109
| | - Pierre F Apostolides
- Department of Otolaryngology-Head & Neck Surgery, Kresge Hearing Research Institute, University of Michigan Medical School, Ann Arbor, Michigan 48109
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan 48109
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3
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Caya-Bissonnette L, Béïque JC. Half a century legacy of long-term potentiation. Curr Biol 2024; 34:R640-R662. [PMID: 38981433 DOI: 10.1016/j.cub.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
In 1973, two papers from Bliss and Lømo and from Bliss and Gardner-Medwin reported that high-frequency synaptic stimulation in the dentate gyrus of rabbits resulted in a long-lasting increase in synaptic strength. This form of synaptic plasticity, commonly referred to as long-term potentiation (LTP), was immediately considered as an attractive mechanism accounting for the ability of the brain to store information. In this historical piece looking back over the past 50 years, we discuss how these two landmark contributions directly motivated a colossal research effort and detail some of the resulting milestones that have shaped our evolving understanding of the molecular and cellular underpinnings of LTP. We highlight the main features of LTP, cover key experiments that defined its induction and expression mechanisms, and outline the evidence supporting a potential role of LTP in learning and memory. We also briefly explore some ramifications of LTP on network stability, consider current limitations of LTP as a model of associative memory, and entertain future research orientations.
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Affiliation(s)
- Léa Caya-Bissonnette
- Graduate Program in Neuroscience, University of Ottawa, 451 ch. Smyth Road (3501N), Ottawa, ON K1H 8M5, Canada; Brain and Mind Research Institute's Centre for Neural Dynamics and Artificial Intelligence, 451 ch. Smyth Road (3501N), Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, 451 ch. Smyth Road (3501N), Ottawa, ON K1H 8M5, Canada
| | - Jean-Claude Béïque
- Brain and Mind Research Institute's Centre for Neural Dynamics and Artificial Intelligence, 451 ch. Smyth Road (3501N), Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, 451 ch. Smyth Road (3501N), Ottawa, ON K1H 8M5, Canada.
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4
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Anisetti VR, Kandala A, Scellier B, Schwarz JM. Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks. Neural Comput 2024; 36:596-620. [PMID: 38457749 DOI: 10.1162/neco_a_01648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/20/2023] [Indexed: 03/10/2024]
Abstract
We introduce frequency propagation, a learning algorithm for nonlinear physical networks. In a resistive electrical circuit with variable resistors, an activation current is applied at a set of input nodes at one frequency and an error current is applied at a set of output nodes at another frequency. The voltage response of the circuit to these boundary currents is the superposition of an activation signal and an error signal whose coefficients can be read in different frequencies of the frequency domain. Each conductance is updated proportionally to the product of the two coefficients. The learning rule is local and proved to perform gradient descent on a loss function. We argue that frequency propagation is an instance of a multimechanism learning strategy for physical networks, be it resistive, elastic, or flow networks. Multimechanism learning strategies incorporate at least two physical quantities, potentially governed by independent physical mechanisms, to act as activation and error signals in the training process. Locally available information about these two signals is then used to update the trainable parameters to perform gradient descent. We demonstrate how earlier work implementing learning via chemical signaling in flow networks (Anisetti, Scellier, et al., 2023) also falls under the rubric of multimechanism learning.
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Affiliation(s)
| | - Ananth Kandala
- Department of Physics, University of Florida, Gainesville, FL 32611, U.S.A.
| | | | - J M Schwarz
- Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A
- Indian Creek Farm, Ithaca, NY 14850, U.S.A.
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5
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Beninger J, Rossbroich J, Tóth K, Naud R. Functional subtypes of synaptic dynamics in mouse and human. Cell Rep 2024; 43:113785. [PMID: 38363673 DOI: 10.1016/j.celrep.2024.113785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/08/2023] [Accepted: 01/27/2024] [Indexed: 02/18/2024] Open
Abstract
Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics. In rodent data, we find five clusters that partially converge with transgenic-associated subtypes. Strikingly, the application of the same clustering method in human data infers a highly similar number of clusters, supportive of stable clustering. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.
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Affiliation(s)
- John Beninger
- Center for Neural Dynamics and Artificial Intelligence, University of Ottawa, Ottawa, ON K1H 8M5, Canada; uOttawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Julian Rossbroich
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland; Faculty of Science, University of Basel, Basel, Switzerland
| | - Katalin Tóth
- Center for Neural Dynamics and Artificial Intelligence, University of Ottawa, Ottawa, ON K1H 8M5, Canada; uOttawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Richard Naud
- Center for Neural Dynamics and Artificial Intelligence, University of Ottawa, Ottawa, ON K1H 8M5, Canada; uOttawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Physics, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
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6
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Ford AN, Czarny JE, Rogalla MM, Quass GL, Apostolides PF. Auditory Corticofugal Neurons Transmit Auditory and Non-auditory Information During Behavior. J Neurosci 2024; 44:e1190232023. [PMID: 38123993 PMCID: PMC10869159 DOI: 10.1523/jneurosci.1190-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/08/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Layer 5 pyramidal neurons of sensory cortices project "corticofugal" axons to myriad sub-cortical targets, thereby broadcasting high-level signals important for perception and learning. Recent studies suggest dendritic Ca2+ spikes as key biophysical mechanisms supporting corticofugal neuron function: these long-lasting events drive burst firing, thereby initiating uniquely powerful signals to modulate sub-cortical representations and trigger learning-related plasticity. However, the behavioral relevance of corticofugal dendritic spikes is poorly understood. We shed light on this issue using 2-photon Ca2+ imaging of auditory corticofugal dendrites as mice of either sex engage in a GO/NO-GO sound-discrimination task. Unexpectedly, only a minority of dendritic spikes were triggered by behaviorally relevant sounds under our conditions. Task related dendritic activity instead mostly followed sound cue termination and co-occurred with mice's instrumental licking during the answer period of behavioral trials, irrespective of reward consumption. Temporally selective, optogenetic silencing of corticofugal neurons during the trial answer period impaired auditory discrimination learning. Thus, auditory corticofugal systems' contribution to learning and plasticity may be partially nonsensory in nature.
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Affiliation(s)
- Alexander N Ford
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Jordyn E Czarny
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Meike M Rogalla
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Gunnar L Quass
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Pierre F Apostolides
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan 48109
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7
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Zheng H, Zheng Z, Hu R, Xiao B, Wu Y, Yu F, Liu X, Li G, Deng L. Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics. Nat Commun 2024; 15:277. [PMID: 38177124 PMCID: PMC10766638 DOI: 10.1038/s41467-023-44614-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024] Open
Abstract
It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.
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Affiliation(s)
- Hanle Zheng
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Zhong Zheng
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Rui Hu
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Bo Xiao
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Yujie Wu
- Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Fangwen Yu
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Xue Liu
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lei Deng
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China.
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8
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Friedenberger Z, Harkin E, Tóth K, Naud R. Silences, spikes and bursts: Three-part knot of the neural code. J Physiol 2023; 601:5165-5193. [PMID: 37889516 DOI: 10.1113/jp281510] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.
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Affiliation(s)
- Zachary Friedenberger
- Brain and Mind Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Centre for Neural Dynamics and Artifical Intelligence, Department of Physics, University of Ottawa, Ottawa, Ontario, Ottawa
| | - Emerson Harkin
- Brain and Mind Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Katalin Tóth
- Brain and Mind Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Richard Naud
- Brain and Mind Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Centre for Neural Dynamics and Artifical Intelligence, Department of Physics, University of Ottawa, Ottawa, Ontario, Ottawa
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9
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Makarov R, Pagkalos M, Poirazi P. Dendrites and efficiency: Optimizing performance and resource utilization. Curr Opin Neurobiol 2023; 83:102812. [PMID: 37980803 DOI: 10.1016/j.conb.2023.102812] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/21/2023]
Abstract
The brain is a highly efficient system that has evolved to optimize performance under limited resources. In this review, we highlight recent theoretical and experimental studies that support the view that dendrites make information processing and storage in the brain more efficient. This is achieved through the dynamic modulation of integration versus segregation of inputs and activity within a neuron. We argue that under conditions of limited energy and space, dendrites help biological networks to implement complex functions such as processing natural stimuli on behavioral timescales, performing the inference process on those stimuli in a context-specific manner, and storing the information in overlapping populations of neurons. A global picture starts to emerge, in which dendrites help the brain achieve efficiency through a combination of optimization strategies that balance the tradeoff between performance and resource utilization.
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Affiliation(s)
- Roman Makarov
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece; Department of Biology, University of Crete, Heraklion, 70013, Greece. https://twitter.com/_RomanMakarov
| | - Michalis Pagkalos
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece; Department of Biology, University of Crete, Heraklion, 70013, Greece. https://twitter.com/MPagkalos
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece.
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10
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Francioni V, Tang VD, Brown NJ, Toloza EH, Harnett M. Vectorized instructive signals in cortical dendrites during a brain-computer interface task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565534. [PMID: 37961227 PMCID: PMC10635122 DOI: 10.1101/2023.11.03.565534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence1,2. Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments3-7. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain.
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Affiliation(s)
- Valerio Francioni
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Vincent D Tang
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Norma J. Brown
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Enrique H.S. Toloza
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Mark Harnett
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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11
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Chapman GW, Hasselmo ME. Predictive learning by a burst-dependent learning rule. Neurobiol Learn Mem 2023; 205:107826. [PMID: 37696414 DOI: 10.1016/j.nlm.2023.107826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 08/05/2023] [Accepted: 09/03/2023] [Indexed: 09/13/2023]
Abstract
Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the presence of sensory noise, in order to be useful for informing behavior. In contrast, typical machine learning approaches require many thousands of samples, and generalize poorly to unexperienced examples, or fail completely to predict at long timescales. Here, we propose a novel neural network module which incorporates hierarchy and recurrent feedback terms, constituting a simplified model of neocortical microcircuits. This microcircuit predicts spatiotemporal trajectories at the input layer using a temporal error minimization algorithm. We show that this module is able to predict with higher accuracy into the future compared to traditional models. Investigating this model we find that successive predictive models learn representations which are increasingly removed from the raw sensory space, namely as successive temporal derivatives of the positional information. Next, we introduce a spiking neural network model which implements the rate-model through the use of a recently proposed biological learning rule utilizing dual-compartment neurons. We show that this network performs well on the same tasks as the mean-field models, by developing intrinsic dynamics that follow the dynamics of the external stimulus, while coordinating transmission of higher-order dynamics. Taken as a whole, these findings suggest that hierarchical temporal abstraction of sequences, rather than feed-forward reconstruction, may be responsible for the ability of neural systems to quickly adapt to novel situations.
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Affiliation(s)
- G William Chapman
- Center for Systems Neuroscience, Boston University, Boston, MA, USA.
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12
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Quass GL, Rogalla MM, Ford AN, Apostolides PF. Mixed representations of sound and action in the auditory midbrain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.19.558449. [PMID: 37786676 PMCID: PMC10541616 DOI: 10.1101/2023.09.19.558449] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Linking sensory input and its consequences is a fundamental brain operation. Accordingly, neural activity of neo-cortical and limbic systems often reflects dynamic combinations of sensory and behaviorally relevant variables, and these "mixed representations" are suggested to be important for perception, learning, and plasticity. However, the extent to which such integrative computations might occur in brain regions upstream of the forebrain is less clear. Here, we conduct cellular-resolution 2-photon Ca2+ imaging in the superficial "shell" layers of the inferior colliculus (IC), as head-fixed mice of either sex perform a reward-based psychometric auditory task. We find that the activity of individual shell IC neurons jointly reflects auditory cues and mice's actions, such that trajectories of neural population activity diverge depending on mice's behavioral choice. Consequently, simple classifier models trained on shell IC neuron activity can predict trial-by-trial outcomes, even when training data are restricted to neural activity occurring prior to mice's instrumental actions. Thus in behaving animals, auditory midbrain neurons transmit a population code that reflects a joint representation of sound and action.
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Affiliation(s)
- GL Quass
- Kresge Hearing Research Institute, Department of Otolaryngology – Head & Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - MM Rogalla
- Kresge Hearing Research Institute, Department of Otolaryngology – Head & Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - AN Ford
- Kresge Hearing Research Institute, Department of Otolaryngology – Head & Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - PF Apostolides
- Kresge Hearing Research Institute, Department of Otolaryngology – Head & Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
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13
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Zhang Y, He G, Ma L, Liu X, Hjorth JJJ, Kozlov A, He Y, Zhang S, Kotaleski JH, Tian Y, Grillner S, Du K, Huang T. A GPU-based computational framework that bridges neuron simulation and artificial intelligence. Nat Commun 2023; 14:5798. [PMID: 37723170 PMCID: PMC10507119 DOI: 10.1038/s41467-023-41553-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/08/2023] [Indexed: 09/20/2023] Open
Abstract
Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.
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Affiliation(s)
- Yichen Zhang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Gan He
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Lei Ma
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, 100084, China
| | - Xiaofei Liu
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- School of Information Science and Engineering, Yunnan University, Kunming, 650500, China
| | - J J Johannes Hjorth
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
| | - Alexander Kozlov
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Yutao He
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Shenjian Zhang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, SE-10044, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Yonghong Tian
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- School of Electrical and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China
| | - Sten Grillner
- Department of Neuroscience, Karolinska Institute, Stockholm, SE-17165, Sweden
| | - Kai Du
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| | - Tiejun Huang
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, 100871, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, 100084, China
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China
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14
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Lorenzon P, Antos K, Tripathi A, Vedin V, Berghard A, Medini P. In vivo spontaneous activity and coital-evoked inhibition of mouse accessory olfactory bulb output neurons. iScience 2023; 26:107545. [PMID: 37664596 PMCID: PMC10470370 DOI: 10.1016/j.isci.2023.107545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/11/2023] [Accepted: 08/01/2023] [Indexed: 09/05/2023] Open
Abstract
Little is known about estrous effects on brain microcircuits. We examined the accessory olfactory bulb (AOB) in vivo, in anesthetized naturally cycling females, as model microcircuit receiving coital somatosensory information. Whole-cell recordings demonstrate that output neurons are relatively hyperpolarized in estrus and unexpectedly fire high frequency bursts of action potentials. To mimic coitus, a calibrated artificial vagino-cervical stimulation (aVCS) protocol was devised. aVCS evoked stimulus-locked local field responses in the interneuron layer independent of estrous stage. The response is sensitive to α1-adrenergic receptor blockade, as expected since aVCS increases norepinephrine release in AOB. Intriguingly, only in estrus does aVCS inhibit AOB spike output. Estrus-specific output reduction coincides with prolonged aVCS activation of inhibitory interneurons. Accordingly, in estrus the AOB microcircuit sets the stage for coital stimulation to inhibit the output neurons, possibly via high frequency bursting-dependent enhancement of reciprocal synapse efficacy between inter- and output neurons.
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Affiliation(s)
- Paolo Lorenzon
- Department of Integrative Medical Biology, Umeå University, SE90187 Umeå, Sweden
| | - Kamil Antos
- Department of Integrative Medical Biology, Umeå University, SE90187 Umeå, Sweden
| | - Anushree Tripathi
- Department of Integrative Medical Biology, Umeå University, SE90187 Umeå, Sweden
| | - Viktoria Vedin
- Department of Molecular Biology, Umeå University, SE90187 Umeå, Sweden
| | - Anna Berghard
- Department of Molecular Biology, Umeå University, SE90187 Umeå, Sweden
| | - Paolo Medini
- Department of Integrative Medical Biology, Umeå University, SE90187 Umeå, Sweden
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15
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Perrenoud Q, Cardin JA. Beyond rhythm - a framework for understanding the frequency spectrum of neural activity. Front Syst Neurosci 2023; 17:1217170. [PMID: 37719024 PMCID: PMC10500127 DOI: 10.3389/fnsys.2023.1217170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Cognitive and behavioral processes are often accompanied by changes within well-defined frequency bands of the local field potential (LFP i.e., the voltage induced by neuronal activity). These changes are detectable in the frequency domain using the Fourier transform and are often interpreted as neuronal oscillations. However, aside some well-known exceptions, the processes underlying such changes are difficult to track in time, making their oscillatory nature hard to verify. In addition, many non-periodic neural processes can also have spectra that emphasize specific frequencies. Thus, the notion that spectral changes reflect oscillations is likely too restrictive. In this study, we use a simple yet versatile framework to understand the frequency spectra of neural recordings. Using simulations, we derive the Fourier spectra of periodic, quasi-periodic and non-periodic neural processes having diverse waveforms, illustrating how these attributes shape their spectral signatures. We then show how neural processes sum their energy in the local field potential in simulated and real-world recording scenarios. We find that the spectral power of neural processes is essentially determined by two aspects: (1) the distribution of neural events in time and (2) the waveform of the voltage induced by single neural events. Taken together, this work guides the interpretation of the Fourier spectrum of neural recordings and indicates that power increases in specific frequency bands do not necessarily reflect periodic neural activity.
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Affiliation(s)
- Quentin Perrenoud
- Department of Neuroscience, Yale School of Medicine, Kavli Institute for Neuroscience, Wu Tsai Institute, New Haven, CT, United States
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16
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Perrenoud Q, Cardin JA. Beyond rhythm - A framework for understanding the frequency spectrum of neural activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.12.540559. [PMID: 37215044 PMCID: PMC10197620 DOI: 10.1101/2023.05.12.540559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Cognitive and behavioral processes are often accompanied by changes within well-defined frequency bands of the local field potential (LFP i.e., the voltage induced by neuronal activity). These changes are detectable in the frequency domain using the Fourier transform and are often interpreted as neuronal oscillations. However, aside some well-known exceptions, the processes underlying such changes are difficult to track in time, making their oscillatory nature hard to verify. In addition, many non-periodic neural processes can also have spectra that emphasize specific frequencies. Thus, the notion that spectral changes reflect oscillations is likely too restrictive. In this study, we propose a simple yet versatile framework to understand the frequency spectra of neural recordings. Using simulations, we derive the Fourier spectra of periodic, quasi-periodic and non-periodic neural processes having diverse waveforms, illustrating how these attributes shape their spectral signatures. We then show how neural processes sum their energy in the local field potential in simulated and real-world recording scenarios. We find that the spectral power of neural processes is essentially determined by two aspects: 1) the distribution of neural events in time and 2) the waveform of the voltage induced by single neural events. Taken together, this work guides the interpretation of the Fourier spectrum of neural recordings and indicates that power increases in specific frequency bands do not necessarily reflect periodic neural activity.
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Affiliation(s)
- Quentin Perrenoud
- Department of Neuroscience, Kavli Institute for Neuroscience, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT, USA
| | - Jessica A. Cardin
- Department of Neuroscience, Kavli Institute for Neuroscience, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT, USA
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17
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Keijser J, Sprekeler H. Cortical interneurons: fit for function and fit to function? Evidence from development and evolution. Front Neural Circuits 2023; 17:1172464. [PMID: 37215503 PMCID: PMC10192557 DOI: 10.3389/fncir.2023.1172464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 03/30/2023] [Indexed: 05/24/2023] Open
Abstract
Cortical inhibitory interneurons form a broad spectrum of subtypes. This diversity suggests a division of labor, in which each cell type supports a distinct function. In the present era of optimisation-based algorithms, it is tempting to speculate that these functions were the evolutionary or developmental driving force for the spectrum of interneurons we see in the mature mammalian brain. In this study, we evaluated this hypothesis using the two most common interneuron types, parvalbumin (PV) and somatostatin (SST) expressing cells, as examples. PV and SST interneurons control the activity in the cell bodies and the apical dendrites of excitatory pyramidal cells, respectively, due to a combination of anatomical and synaptic properties. But was this compartment-specific inhibition indeed the function for which PV and SST cells originally evolved? Does the compartmental structure of pyramidal cells shape the diversification of PV and SST interneurons over development? To address these questions, we reviewed and reanalyzed publicly available data on the development and evolution of PV and SST interneurons on one hand, and pyramidal cell morphology on the other. These data speak against the idea that the compartment structure of pyramidal cells drove the diversification into PV and SST interneurons. In particular, pyramidal cells mature late, while interneurons are likely committed to a particular fate (PV vs. SST) during early development. Moreover, comparative anatomy and single cell RNA-sequencing data indicate that PV and SST cells, but not the compartment structure of pyramidal cells, existed in the last common ancestor of mammals and reptiles. Specifically, turtle and songbird SST cells also express the Elfn1 and Cbln4 genes that are thought to play a role in compartment-specific inhibition in mammals. PV and SST cells therefore evolved and developed the properties that allow them to provide compartment-specific inhibition before there was selective pressure for this function. This suggest that interneuron diversity originally resulted from a different evolutionary driving force and was only later co-opted for the compartment-specific inhibition it seems to serve in mammals today. Future experiments could further test this idea using our computational reconstruction of ancestral Elfn1 protein sequences.
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Affiliation(s)
- Joram Keijser
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Einstein Center for Neurosciences, Charité University Medicine Berlin, Berlin, Germany
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt University of Berlin, Berlin, Germany
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18
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Kim YJ, Ujfalussy BB, Lengyel M. Parallel functional architectures within a single dendritic tree. Cell Rep 2023; 42:112386. [PMID: 37060564 PMCID: PMC7614531 DOI: 10.1016/j.celrep.2023.112386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/31/2022] [Accepted: 03/28/2023] [Indexed: 04/16/2023] Open
Abstract
The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the underlying functional architecture as unitary, such that each synaptic input makes a single contribution to the neuronal response. Here, we show that the input-output transformation of CA1 pyramidal cells is instead best captured by two distinct functional architectures operating in parallel. We used statistically principled methods to fit flexible, yet interpretable, models of the transformation of input spikes into the somatic "output" voltage and to automatically select among alternative functional architectures. With dendritic Na+ channels blocked, responses are accurately captured by a single static and global nonlinearity. In contrast, dendritic Na+-dependent integration requires a functional architecture with multiple dynamic nonlinearities and clustered connectivity. These two architectures incorporate distinct morphological and biophysical properties of the neuron and its synaptic organization.
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Affiliation(s)
- Young Joon Kim
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Harvard Medical School, Boston, MA, USA.
| | - Balázs B Ujfalussy
- Laboratory of Biological Computation, Institute of Experimental Medicine, Budapest, Hungary
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
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19
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Wilmes KA, Clopath C. Dendrites help mitigate the plasticity-stability dilemma. Sci Rep 2023; 13:6543. [PMID: 37085642 PMCID: PMC10121616 DOI: 10.1038/s41598-023-32410-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/27/2023] [Indexed: 04/23/2023] Open
Abstract
With Hebbian learning 'who fires together wires together', well-known problems arise. Hebbian plasticity can cause unstable network dynamics and overwrite stored memories. Because the known homeostatic plasticity mechanisms tend to be too slow to combat unstable dynamics, it has been proposed that plasticity must be highly gated and synaptic strengths limited. While solving the issue of stability, gating and limiting plasticity does not solve the stability-plasticity dilemma. We propose that dendrites enable both stable network dynamics and considerable synaptic changes, as they allow the gating of plasticity in a compartment-specific manner. We investigate how gating plasticity influences network stability in plastic balanced spiking networks of neurons with dendrites. We compare how different ways to gate plasticity, namely via modulating excitability, learning rate, and inhibition increase stability. We investigate how dendritic versus perisomatic gating allows for different amounts of weight changes in stable networks. We suggest that the compartmentalisation of pyramidal cells enables dendritic synaptic changes while maintaining stability. We show that the coupling between dendrite and soma is critical for the plasticity-stability trade-off. Finally, we show that spatially restricted plasticity additionally improves stability.
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Affiliation(s)
- Katharina A Wilmes
- Imperial College London, London, United Kingdom.
- University of Bern, Bern, Switzerland.
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20
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Shervani-Tabar N, Rosenbaum R. Meta-learning biologically plausible plasticity rules with random feedback pathways. Nat Commun 2023; 14:1805. [PMID: 37002222 PMCID: PMC10066328 DOI: 10.1038/s41467-023-37562-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/21/2023] [Indexed: 04/04/2023] Open
Abstract
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-learning to discover effective, interpretable learning rules satisfying biological constraints.
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Affiliation(s)
- Navid Shervani-Tabar
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA.
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
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21
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Lin R, Dai B, Zhao Y, Chen G, Lu H. Constrain Bias Addition to Train Low-Latency Spiking Neural Networks. Brain Sci 2023; 13:brainsci13020319. [PMID: 36831862 PMCID: PMC9954654 DOI: 10.3390/brainsci13020319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/19/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023] Open
Abstract
In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement.
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Affiliation(s)
- Ranxi Lin
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
- University of Chinese Academy of Sciences, Beijing 100089, China
| | - Benzhe Dai
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
- University of Chinese Academy of Sciences, Beijing 100089, China
| | - Yingkai Zhao
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
- University of Chinese Academy of Sciences, Beijing 100089, China
| | - Gang Chen
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
- Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory, Beijing 100083, China
- Correspondence:
| | - Huaxiang Lu
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
- University of Chinese Academy of Sciences, Beijing 100089, China
- Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory, Beijing 100083, China
- Collage of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
- Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences, Beijing 200031, China
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22
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Thivierge JP, Giraud É, Lynn M. Toward a Brain-Inspired Theory of Artificial Learning. Cognit Comput 2023. [DOI: 10.1007/s12559-023-10121-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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23
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Lin C, Wu X, Zhang Y. Bursting hierarchy in an adaptive exponential integrate-and-fire network synchronization. BIOLOGICAL CYBERNETICS 2022; 116:545-556. [PMID: 36044046 DOI: 10.1007/s00422-022-00942-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Neuronal network synchronization has received wide interest. In the present manuscript, we study the influence of initial membrane potentials together with network topology on bursting synchronization, in particular the sequential order of stabilized bursting among neurons. We find a hierarchical phenomenon on their bursting order. With a focus on situations where network coupling advances spiking times of neurons, we grade neurons into different layers. Together with the neuronal network structure, we construct directed graphs to indicate bursting propagation between different layers. More explicitly, neurons in upper layers burst earlier than those in lower layers. More interestingly, we find that among the same layer, bursting order of neurons is mainly associated with the number of neurons they connected to the upper layer; more stimuli lead to earlier bursting. Receiving effectively the same stimuli from the upper layer, we observe neurons with fewer connections would burst earlier.
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Affiliation(s)
- Congping Lin
- School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
- Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
- Hubei Key Laboratory of Engineering Modeling and Scientific Computing, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Xiaoyue Wu
- School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Yiwei Zhang
- School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
- Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
- Hubei Key Laboratory of Engineering Modeling and Scientific Computing, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
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24
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Hahn LA, Balakhonov D, Lundqvist M, Nieder A, Rose J. Oscillations without cortex: Working memory modulates brainwaves in the endbrain of crows. Prog Neurobiol 2022; 219:102372. [DOI: 10.1016/j.pneurobio.2022.102372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/21/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
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25
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Ghadimi A, Steiner LA, Popovic MR, Milosevic L, Lankarany M. Inferring stimulation induced short-term synaptic plasticity dynamics using novel dual optimization algorithm. PLoS One 2022; 17:e0273699. [PMID: 36129852 PMCID: PMC9491593 DOI: 10.1371/journal.pone.0273699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 08/12/2022] [Indexed: 12/05/2022] Open
Abstract
Experimental evidence in both human and animal studies demonstrated that deep brain stimulation (DBS) can induce short-term synaptic plasticity (STP) in the stimulated nucleus. Given that DBS-induced STP may be connected to the therapeutic effects of DBS, we sought to develop a computational predictive model that infers the dynamics of STP in response to DBS at different frequencies. Existing methods for estimating STP–either model-based or model-free approaches–require access to pre-synaptic spiking activity. However, in the context of DBS, extracellular stimulation (e.g. DBS) can be used to elicit presynaptic activations directly. We present a model-based approach that integrates multiple individual frequencies of DBS-like electrical stimulation as pre-synaptic spikes and infers parameters of the Tsodyks-Markram (TM) model from post-synaptic currents of the stimulated nucleus. By distinguishing between the steady-state and transient responses of the TM model, we develop a novel dual optimization algorithm that infers the model parameters in two steps. First, the TM model parameters are calculated by integrating multiple frequencies of stimulation to estimate the steady state response of post-synaptic current through a closed-form analytical solution. The results of this step are utilized as the initial values for the second step in which a non-derivative optimization algorithm is used to track the transient response of the post-synaptic potential across different individual frequencies of stimulation. Moreover, in order to confirm the applicability of the method, we applied our algorithm–as a proof of concept–to empirical data recorded from acute rodent brain slices of the subthalamic nucleus (STN) during DBS-like stimulation to infer dynamics of STP for inhibitory synaptic inputs.
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Affiliation(s)
- Alireza Ghadimi
- Krembil Research Institute – University Health Network (UHN), Toronto, Ontario, Canada
- Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, Ontario, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network (UHN), Toronto, Ontario, Canada
| | - Leon Amadeus Steiner
- Krembil Research Institute – University Health Network (UHN), Toronto, Ontario, Canada
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Milos R. Popovic
- Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, Ontario, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network (UHN), Toronto, Ontario, Canada
| | - Luka Milosevic
- Krembil Research Institute – University Health Network (UHN), Toronto, Ontario, Canada
- Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, Ontario, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network (UHN), Toronto, Ontario, Canada
| | - Milad Lankarany
- Krembil Research Institute – University Health Network (UHN), Toronto, Ontario, Canada
- Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, Toronto, Ontario, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network (UHN), Toronto, Ontario, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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26
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Mercier MS, Magloire V, Cornford JH, Kullmann DM. Long-term potentiation in neurogliaform interneurons modulates excitation-inhibition balance in the temporoammonic pathway. J Physiol 2022; 600:4001-4017. [PMID: 35876215 PMCID: PMC9540908 DOI: 10.1113/jp282753] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/19/2022] [Indexed: 11/08/2022] Open
Abstract
Apical dendrites of pyramidal neurons integrate information from higher-order cortex and thalamus, and gate signalling and plasticity at proximal synapses. In the hippocampus, neurogliaform cells and other interneurons located within stratum lacunosum-moleculare (SLM) mediate powerful inhibition of CA1 pyramidal neuron distal dendrites. Is the recruitment of such inhibition itself subject to use-dependent plasticity, and if so, what induction rules apply? Here we show that interneurons in mouse SLM exhibit Hebbian NMDA receptor-dependent long-term potentiation (LTP). Such plasticity can be induced by selective optogenetic stimulation of afferents in the temporoammonic pathway from the entorhinal cortex (EC), but not by equivalent stimulation of afferents from the thalamic nucleus reuniens. We further show that theta-burst patterns of afferent firing induces LTP in neurogliaform interneurons identified using neuron-derived neurotrophic factor (Ndnf)-Cre mice. Theta-burst activity of EC afferents led to an increase in disynaptic feed-forward inhibition, but not monosynaptic excitation, of CA1 pyramidal neurons. Activity-dependent synaptic plasticity in SLM interneurons thus alters the excitation-inhibition balance at EC inputs to the apical dendrites of pyramidal neurons, implying a dynamic role for these interneurons in gating CA1 dendritic computations. KEY POINTS: Electrogenic phenomena in distal dendrites of principal neurons in the hippocampus have a major role in gating synaptic plasticity at afferent synapses on proximal dendrites. Apical dendrites also receive powerful feed-forward inhibition, mediated in large part by neurogliaform neurons. Here we show that theta-burst activity in afferents from the entorhinal cortex (EC) induces 'Hebbian' long-term potentiation (LTP) at excitatory synapses recruiting these GABAergic cells. LTP in interneurons innervating apical dendrites increases disynaptic inhibition of principal neurons, thus shifting the excitation-inhibition balance in the temporoammonic (TA) pathway in favour of inhibition, with implications for computations and learning rules in proximal dendrites.
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Affiliation(s)
- Marion S. Mercier
- UCL Queen Square Institute of NeurologyDepartment of Clinical and Experimental EpilepsyUniversity College LondonLondonUK
| | - Vincent Magloire
- UCL Queen Square Institute of NeurologyDepartment of Clinical and Experimental EpilepsyUniversity College LondonLondonUK
| | - Jonathan H. Cornford
- UCL Queen Square Institute of NeurologyDepartment of Clinical and Experimental EpilepsyUniversity College LondonLondonUK
| | - Dimitri M. Kullmann
- UCL Queen Square Institute of NeurologyDepartment of Clinical and Experimental EpilepsyUniversity College LondonLondonUK
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27
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Keijser J, Sprekeler H. Optimizing interneuron circuits for compartment-specific feedback inhibition. PLoS Comput Biol 2022; 18:e1009933. [PMID: 35482670 PMCID: PMC9049365 DOI: 10.1371/journal.pcbi.1009933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/18/2022] [Indexed: 12/02/2022] Open
Abstract
Cortical circuits process information by rich recurrent interactions between excitatory neurons and inhibitory interneurons. One of the prime functions of interneurons is to stabilize the circuit by feedback inhibition, but the level of specificity on which inhibitory feedback operates is not fully resolved. We hypothesized that inhibitory circuits could enable separate feedback control loops for different synaptic input streams, by means of specific feedback inhibition to different neuronal compartments. To investigate this hypothesis, we adopted an optimization approach. Leveraging recent advances in training spiking network models, we optimized the connectivity and short-term plasticity of interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons. Over the course of the optimization, the interneurons diversified into two classes that resembled parvalbumin (PV) and somatostatin (SST) expressing interneurons. Using simulations and mathematical analyses, we show that the resulting circuit can be understood as a neural decoder that inverts the nonlinear biophysical computations performed within the pyramidal cells. Our model provides a proof of concept for studying structure-function relations in cortical circuits by a combination of gradient-based optimization and biologically plausible phenomenological models. The brain contains billions of nerve cells—neurons—that can be classified into different types depending on their shape, connectivity and activity. A particularly diverse group of neurons is that of inhibitory neurons, named after their suppressive effect on neural activity. Presumably, their diverse properties allow inhibitory neurons to fulfil different functions, but what these functions are is currently unclear. In this paper, we investigated if a particular function can explain the existence and properties of the two most common inhibitory cell classes: The need to regulate activity in different physical parts (compartments) of the neurons they target. We investigated this function in a computer model, using optimization to find the neuron properties best-suited for compartment-specific inhibition. Our key result is that after the optimization, model neurons largely fell into two classes that resembled the two types of biological neurons. In particular, the optimized neurons were connected to only one compartment of other neurons. This suggests that the diversity of inhibitory neurons is well suited for compartment-specific inhibition. In the future, our approach of optimizing neural properties might be used to investigate other functions (or dysfunctions) of neuron diversity.
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Affiliation(s)
- Joram Keijser
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- * E-mail:
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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28
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Intrinsic bursts facilitate learning of Lévy flight movements in recurrent neural network models. Sci Rep 2022; 12:4951. [PMID: 35322813 PMCID: PMC8943163 DOI: 10.1038/s41598-022-08953-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/09/2022] [Indexed: 11/24/2022] Open
Abstract
Isolated spikes and bursts of spikes are thought to provide the two major modes of information coding by neurons. Bursts are known to be crucial for fundamental processes between neuron pairs, such as neuronal communications and synaptic plasticity. Neuronal bursting also has implications in neurodegenerative diseases and mental disorders. Despite these findings on the roles of bursts, whether and how bursts have an advantage over isolated spikes in the network-level computation remains elusive. Here, we demonstrate in a computational model that not isolated spikes, but intrinsic bursts can greatly facilitate learning of Lévy flight random walk trajectories by synchronizing burst onsets across a neural population. Lévy flight is a hallmark of optimal search strategies and appears in cognitive behaviors such as saccadic eye movements and memory retrieval. Our results suggest that bursting is crucial for sequence learning by recurrent neural networks when sequences comprise long-tailed distributed discrete jumps.
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29
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Yu Q, Song S, Ma C, Pan L, Tan KC. Synaptic Learning With Augmented Spikes. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:1134-1146. [PMID: 33471768 DOI: 10.1109/tnnls.2020.3040969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.
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30
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Geng HY, Arbuthnott G, Yung WH, Ke Y. Long-Range Monosynaptic Inputs Targeting Apical and Basal Dendrites of Primary Motor Cortex Deep Output Neurons. Cereb Cortex 2021; 32:3975-3989. [PMID: 34905771 DOI: 10.1093/cercor/bhab460] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/10/2021] [Accepted: 11/16/2021] [Indexed: 12/31/2022] Open
Abstract
The primary motor cortex (M1) integrates various long-range signals from other brain regions for the learning and execution of goal-directed movements. How the different inputs target the distinct apical and basal dendrites of M1 pyramidal neurons is crucial in understanding the functions of M1, but the detailed connectivity pattern is still largely unknown. Here, by combining cre-dependent rabies virus tracing, layer-specific chemical retrograde tracing, optogenetic stimulation, and electrophysiological recording, we mapped all long-range monosynaptic inputs to M1 deep output neurons in layer 5 (L5) in mice. We revealed that most upstream areas innervate both dendritic compartments concurrently. These include the sensory cortices, higher motor cortices, sensory and motor thalamus, association cortices, as well as many subcortical nuclei. Furthermore, the dichotomous inputs arise mostly from spatially segregated neuronal subpopulations within an upstream nucleus, and even in the case of an individual cortical layer. Therefore, these input areas could serve as both feedforward and feedback sources albeit via different subpopulations. Taken together, our findings revealed a previously unknown and highly intricate synaptic input pattern of M1L5 neurons, which implicates that the dendritic computations carried out by these neurons during motor execution or learning are far more complicated than we currently understand.
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Affiliation(s)
- Hong-Yan Geng
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - Gordon Arbuthnott
- Brain Mechanisms for Behaviour Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0485, Japan
| | - Wing-Ho Yung
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong.,Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Hong Kong
| | - Ya Ke
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong.,Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Hong Kong
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31
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Vercruysse F, Naud R, Sprekeler H. Self-organization of a doubly asynchronous irregular network state for spikes and bursts. PLoS Comput Biol 2021; 17:e1009478. [PMID: 34748532 PMCID: PMC8575278 DOI: 10.1371/journal.pcbi.1009478] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 09/24/2021] [Indexed: 11/21/2022] Open
Abstract
Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.
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Affiliation(s)
- Filip Vercruysse
- Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Richard Naud
- Department of Physics, University of Ottawa, Ottawa, Canada
- uOttawa Brain Mind Institute, Center for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada
| | - Henning Sprekeler
- Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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32
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Leleo EG, Segev I. Burst control: Synaptic conditions for burst generation in cortical layer 5 pyramidal neurons. PLoS Comput Biol 2021; 17:e1009558. [PMID: 34727124 PMCID: PMC8589150 DOI: 10.1371/journal.pcbi.1009558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 11/12/2021] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
The output of neocortical layer 5 pyramidal cells (L5PCs) is expressed by a train of single spikes with intermittent bursts of multiple spikes at high frequencies. The bursts are the result of nonlinear dendritic properties, including Na+, Ca2+, and NMDA spikes, that interact with the ~10,000 synapses impinging on the neuron's dendrites. Output spike bursts are thought to implement key dendritic computations, such as coincidence detection of bottom-up inputs (arriving mostly at the basal tree) and top-down inputs (arriving mostly at the apical tree). In this study we used a detailed nonlinear model of L5PC receiving excitatory and inhibitory synaptic inputs to explore the conditions for generating bursts and for modulating their properties. We established the excitatory input conditions on the basal versus the apical tree that favor burst and show that there are two distinct types of bursts. Bursts consisting of 3 or more spikes firing at < 200 Hz, which are generated by stronger excitatory input to the basal versus the apical tree, and bursts of ~2-spikes at ~250 Hz, generated by prominent apical tuft excitation. Localized and well-timed dendritic inhibition on the apical tree differentially modulates Na+, Ca2+, and NMDA spikes and, consequently, finely controls the burst output. Finally, we explored the implications of different burst classes and respective dendritic inhibition for regulating synaptic plasticity.
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Affiliation(s)
- Eilam Goldenberg Leleo
- The Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Idan Segev
- The Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem, Israel
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33
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Gallinaro JV, Clopath C. Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity. PLoS Comput Biol 2021; 17:e1009593. [PMID: 34762644 PMCID: PMC8610285 DOI: 10.1371/journal.pcbi.1009593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/23/2021] [Accepted: 10/26/2021] [Indexed: 11/19/2022] Open
Abstract
Cell assemblies are thought to be the substrate of memory in the brain. Theoretical studies have previously shown that assemblies can be formed in networks with multiple types of plasticity. But how exactly they are formed and how they encode information is yet to be fully understood. One possibility is that memories are stored in silent assemblies. Here we used a computational model to study the formation of silent assemblies in a network of spiking neurons with excitatory and inhibitory plasticity. We found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals. We also found that this spiking irregularity could be read out with support of short-term plasticity, and that it could contribute to the longevity of memories.
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Affiliation(s)
- Júlia V. Gallinaro
- Bioengineering Department, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Bioengineering Department, Imperial College London, London, United Kingdom
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34
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A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron 2021; 109:4001-4017.e10. [PMID: 34715026 PMCID: PMC8691952 DOI: 10.1016/j.neuron.2021.09.044] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 08/10/2021] [Accepted: 09/23/2021] [Indexed: 11/23/2022]
Abstract
Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.
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35
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Berg J, Sorensen SA, Ting JT, Miller JA, Chartrand T, Buchin A, Bakken TE, Budzillo A, Dee N, Ding SL, Gouwens NW, Hodge RD, Kalmbach B, Lee C, Lee BR, Alfiler L, Baker K, Barkan E, Beller A, Berry K, Bertagnolli D, Bickley K, Bomben J, Braun T, Brouner K, Casper T, Chong P, Crichton K, Dalley R, de Frates R, Desta T, Lee SD, D'Orazi F, Dotson N, Egdorf T, Enstrom R, Farrell C, Feng D, Fong O, Furdan S, Galakhova AA, Gamlin C, Gary A, Glandon A, Goldy J, Gorham M, Goriounova NA, Gratiy S, Graybuck L, Gu H, Hadley K, Hansen N, Heistek TS, Henry AM, Heyer DB, Hill D, Hill C, Hupp M, Jarsky T, Kebede S, Keene L, Kim L, Kim MH, Kroll M, Latimer C, Levi BP, Link KE, Mallory M, Mann R, Marshall D, Maxwell M, McGraw M, McMillen D, Melief E, Mertens EJ, Mezei L, Mihut N, Mok S, Molnar G, Mukora A, Ng L, Ngo K, Nicovich PR, Nyhus J, Olah G, Oldre A, Omstead V, Ozsvar A, Park D, Peng H, Pham T, Pom CA, Potekhina L, Rajanbabu R, Ransford S, Reid D, Rimorin C, Ruiz A, Sandman D, Sulc J, Sunkin SM, Szafer A, Szemenyei V, Thomsen ER, Tieu M, Torkelson A, Trinh J, Tung H, Wakeman W, Waleboer F, Ward K, Wilbers R, Williams G, Yao Z, Yoon JG, Anastassiou C, Arkhipov A, Barzo P, Bernard A, Cobbs C, de Witt Hamer PC, Ellenbogen RG, Esposito L, Ferreira M, Gwinn RP, Hawrylycz MJ, Hof PR, Idema S, Jones AR, Keene CD, Ko AL, Murphy GJ, Ng L, Ojemann JG, Patel AP, Phillips JW, Silbergeld DL, Smith K, Tasic B, Yuste R, Segev I, de Kock CPJ, Mansvelder HD, Tamas G, Zeng H, Koch C, Lein ES. Human neocortical expansion involves glutamatergic neuron diversification. Nature 2021; 598:151-158. [PMID: 34616067 PMCID: PMC8494638 DOI: 10.1038/s41586-021-03813-8] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/07/2021] [Indexed: 11/09/2022]
Abstract
The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer's disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.
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Affiliation(s)
- Jim Berg
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Allison Beller
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Kyla Berry
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kris Bickley
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Peter Chong
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Tsega Desta
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Szabina Furdan
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Anna A Galakhova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Clare Gamlin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Alex M Henry
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chris Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lisa Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Caitlin Latimer
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Desiree Marshall
- Department of Pathology, University of Washington, Seattle, WA, USA
| | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Erica Melief
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Eline J Mertens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Leona Mezei
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Norbert Mihut
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | | | - Gabor Molnar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Gaspar Olah
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Attila Ozsvar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - David Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Viktor Szemenyei
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Femke Waleboer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA, USA
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Pal Barzo
- Department of Neurosurgery, University of Szeged, Szeged, Hungary
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Philip C de Witt Hamer
- Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sander Idema
- Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - C Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Anoop P Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | - Rafael Yuste
- NeuroTechnology Center, Columbia University, New York, NY, USA
| | - Idan Segev
- Edmond and Lily Safra Center for Brain Sciences and Department of Neurobiology, The Hebrew University Jerusalem, Jerusalem, Israel
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Gabor Tamas
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA.
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA.
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36
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Schulz JM, Kay JW, Bischofberger J, Larkum ME. GABA B Receptor-Mediated Regulation of Dendro-Somatic Synergy in Layer 5 Pyramidal Neurons. Front Cell Neurosci 2021; 15:718413. [PMID: 34512268 PMCID: PMC8425515 DOI: 10.3389/fncel.2021.718413] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/20/2021] [Indexed: 11/24/2022] Open
Abstract
Synergistic interactions between independent synaptic input streams may fundamentally change the action potential (AP) output. Using partial information decomposition, we demonstrate here a substantial contribution of synergy between somatic and apical dendritic inputs to the information in the AP output of L5b pyramidal neurons. Activation of dendritic GABAB receptors (GABABRs), known to decrease APs in vivo, potently decreased synergy and increased somatic control of AP output. Synergy was the result of the voltage-dependence of the transfer resistance between dendrite and soma, which showed a two-fold increase per 28.7 mV dendritic depolarization. GIRK channels activated by dendritic GABABRs decreased voltage-dependent transfer resistances and AP output. In contrast, inhibition of dendritic L-type Ca2+ channels prevented high-frequency bursts of APs, but did not affect dendro-somatic synergy. Finally, we show that NDNF-positive neurogliaform cells effectively control somatic AP via synaptic activation of dendritic GIRK channels. These results uncover a novel inhibitory mechanism that powerfully gates cellular information flow in the cortex.
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Affiliation(s)
- Jan M Schulz
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Jim W Kay
- Department of Statistics, University of Glasgow, Glasgow, United Kingdom
| | | | - Matthew E Larkum
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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37
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Mease RA, Gonzalez AJ. Corticothalamic Pathways From Layer 5: Emerging Roles in Computation and Pathology. Front Neural Circuits 2021; 15:730211. [PMID: 34566583 PMCID: PMC8458899 DOI: 10.3389/fncir.2021.730211] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/10/2021] [Indexed: 11/29/2022] Open
Abstract
Large portions of the thalamus receive strong driving input from cortical layer 5 (L5) neurons but the role of this important pathway in cortical and thalamic computations is not well understood. L5-recipient "higher-order" thalamic regions participate in cortico-thalamo-cortical (CTC) circuits that are increasingly recognized to be (1) anatomically and functionally distinct from better-studied "first-order" CTC networks, and (2) integral to cortical activity related to learning and perception. Additionally, studies are beginning to elucidate the clinical relevance of these networks, as dysfunction across these pathways have been implicated in several pathological states. In this review, we highlight recent advances in understanding L5 CTC networks across sensory modalities and brain regions, particularly studies leveraging cell-type-specific tools that allow precise experimental access to L5 CTC circuits. We aim to provide a focused and accessible summary of the anatomical, physiological, and computational properties of L5-originating CTC networks, and outline their underappreciated contribution in pathology. We particularly seek to connect single-neuron and synaptic properties to network (dys)function and emerging theories of cortical computation, and highlight information processing in L5 CTC networks as a promising focus for computational studies.
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Affiliation(s)
- Rebecca A. Mease
- Institute of Physiology and Pathophysiology, Medical Biophysics, Heidelberg University, Heidelberg, Germany
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38
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Pulikkottil VV, Somashekar BP, Bhalla US. Computation, wiring, and plasticity in synaptic clusters. Curr Opin Neurobiol 2021; 70:101-112. [PMID: 34509808 DOI: 10.1016/j.conb.2021.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 07/28/2021] [Accepted: 08/09/2021] [Indexed: 01/19/2023]
Abstract
Synaptic clusters on dendrites are extraordinarily compact computational building blocks. They contribute to key local computations through biophysical and biochemical signaling that utilizes convergence in space and time as an organizing principle. However, these computations can only arise in very special contexts. Dendritic cluster computations, their highly organized input connectivity, and the mechanisms for their formation are closely linked, yet these have not been analyzed as parts of a single process. Here, we examine these linkages. The sheer density of axonal and dendritic arborizations means that there are far more potential connections (close enough for a spine to reach an axon) than actual ones. We see how dendritic clusters draw upon electrical, chemical, and mechano-chemical signaling to implement the rules for formation of connections and subsequent computations. Crucially, the same mechanisms that underlie their functions also underlie their formation.
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Affiliation(s)
| | - Bhanu Priya Somashekar
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Upinder S Bhalla
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India.
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39
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Williams E, Payeur A, Gidon A, Naud R. Neural burst codes disguised as rate codes. Sci Rep 2021; 11:15910. [PMID: 34354118 PMCID: PMC8342467 DOI: 10.1038/s41598-021-95037-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/13/2021] [Indexed: 02/07/2023] Open
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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40
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de Kock CPJ, Pie J, Pieneman AW, Mease RA, Bast A, Guest JM, Oberlaender M, Mansvelder HD, Sakmann B. High-frequency burst spiking in layer 5 thick-tufted pyramids of rat primary somatosensory cortex encodes exploratory touch. Commun Biol 2021; 4:709. [PMID: 34112934 PMCID: PMC8192911 DOI: 10.1038/s42003-021-02241-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 05/18/2021] [Indexed: 01/14/2023] Open
Abstract
Diversity of cell-types that collectively shape the cortical microcircuit ensures the necessary computational richness to orchestrate a wide variety of behaviors. The information content embedded in spiking activity of identified cell-types remain unclear to a large extent. Here, we recorded spike responses upon whisker touch of anatomically identified excitatory cell-types in primary somatosensory cortex in naive, untrained rats. We find major differences across layers and cell-types. The temporal structure of spontaneous spiking contains high-frequency bursts (≥100 Hz) in all morphological cell-types but a significant increase upon whisker touch is restricted to layer L5 thick-tufted pyramids (L5tts) and thus provides a distinct neurophysiological signature. We find that whisker touch can also be decoded from L5tt bursting, but not from other cell-types. We observed high-frequency bursts in L5tts projecting to different subcortical regions, including thalamus, midbrain and brainstem. We conclude that bursts in L5tts allow accurate coding and decoding of exploratory whisker touch. In order to investigate the information encoded by spiking activity in different neuronal cell types in the primary somatosensory cortex, de Kock et al performed electrophysiological recordings in untrained rats. They demonstrated that an increase in high-frequency burst spiking in thick tufted pyramids in layer 5 of the cortex allow accurate encoding of exploratory whisker touch.
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Affiliation(s)
- Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU, Amsterdam, the Netherlands.
| | - Jean Pie
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU, Amsterdam, the Netherlands.,University of Amsterdam, Swammerdam Institute for Life Sciences, Amsterdam, Netherlands
| | - Anton W Pieneman
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU, Amsterdam, the Netherlands
| | - Rebecca A Mease
- Institute of Physiology and Pathophysiology, Heidelberg University, Heidelberg, Germany
| | - Arco Bast
- Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany
| | - Jason M Guest
- Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany
| | - Marcel Oberlaender
- Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU, Amsterdam, the Netherlands
| | - Bert Sakmann
- Max Planck Institute for Neurobiology, Martinsried, Germany
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41
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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: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [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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42
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Song S, Ma C, Sun W, Xu J, Dang J, Yu Q. Efficient learning with augmented spikes: A case study with image classification. Neural Netw 2021; 142:205-212. [PMID: 34023641 DOI: 10.1016/j.neunet.2021.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 02/15/2021] [Accepted: 05/06/2021] [Indexed: 10/21/2022]
Abstract
Efficient learning of spikes plays a valuable role in training spiking neural networks (SNNs) to have desired responses to input stimuli. However, current learning rules are limited to a binary form of spikes. The seemingly ubiquitous phenomenon of burst in nervous systems suggests a new way to carry more information with spike bursts in addition to times. Based on this, we introduce an advanced form, the augmented spikes, where spike coefficients are used to carry additional information. How could neurons learn and benefit from augmented spikes remains unclear. In this paper, we propose two new efficient learning rules to process spatiotemporal patterns composed of augmented spikes. Moreover, we examine the learning abilities of our methods with a synthetic recognition task of augmented spike patterns and two practical ones for image classification. Experimental results demonstrate that our rules are capable of extracting information carried by both the timing and coefficient of spikes. Our proposed approaches achieve remarkable performance and good robustness under various noise conditions, as compared to benchmarks. The improved performance indicates the merits of augmented spikes and our learning rules, which could be beneficial and generalized to a broad range of spike-based platforms.
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Affiliation(s)
- Shiming Song
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Chenxiang Ma
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Wei Sun
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Jianwu Dang
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Qiang Yu
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
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43
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Short-Term Synaptic Plasticity Makes Neurons Sensitive to the Distribution of Presynaptic Population Firing Rates. eNeuro 2021; 8:ENEURO.0297-20.2021. [PMID: 33579731 PMCID: PMC8035045 DOI: 10.1523/eneuro.0297-20.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 01/14/2021] [Accepted: 01/19/2021] [Indexed: 12/25/2022] Open
Abstract
The ability to discriminate spikes that encode a particular stimulus from spikes produced by background activity is essential for reliable information processing in the brain. We describe how synaptic short-term plasticity (STP) modulates the output of presynaptic populations as a function of the distribution of the spiking activity and find a strong relationship between STP features and sparseness of the population code, which could solve this problem. Furthermore, we show that feedforward excitation followed by inhibition (FF-EI), combined with target-dependent STP, promote substantial increase in the signal gain even for considerable deviations from the optimal conditions, granting robustness to this mechanism. A simulated neuron driven by a spiking FF-EI network is reliably modulated as predicted by a rate analysis and inherits the ability to differentiate sparse signals from dense background activity changes of the same magnitude, even at very low signal-to-noise conditions. We propose that the STP-based distribution discrimination is likely a latent function in several regions such as the cerebellum and the hippocampus.
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44
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Rossbroich J, Trotter D, Beninger J, Tóth K, Naud R. Linear-nonlinear cascades capture synaptic dynamics. PLoS Comput Biol 2021; 17:e1008013. [PMID: 33720935 PMCID: PMC7993773 DOI: 10.1371/journal.pcbi.1008013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 03/25/2021] [Accepted: 02/25/2021] [Indexed: 11/18/2022] Open
Abstract
Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.
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Affiliation(s)
- Julian Rossbroich
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Daniel Trotter
- Department of Physics, University of Ottawa, Ottawa, ON, Canada
| | - John Beninger
- uOttawa Brain Mind Institute, Center for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Katalin Tóth
- uOttawa Brain Mind Institute, Center for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Richard Naud
- Department of Physics, University of Ottawa, Ottawa, ON, Canada
- uOttawa Brain Mind Institute, Center for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
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45
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Doron G, Shin JN, Takahashi N, Drüke M, Bocklisch C, Skenderi S, de Mont L, Toumazou M, Ledderose J, Brecht M, Naud R, Larkum ME. Perirhinal input to neocortical layer 1 controls learning. Science 2021; 370:370/6523/eaaz3136. [PMID: 33335033 DOI: 10.1126/science.aaz3136] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 08/27/2020] [Accepted: 10/23/2020] [Indexed: 12/28/2022]
Abstract
Hippocampal output influences memory formation in the neocortex, but this process is poorly understood because the precise anatomical location and the underlying cellular mechanisms remain elusive. Here, we show that perirhinal input, predominantly to sensory cortical layer 1 (L1), controls hippocampal-dependent associative learning in rodents. This process was marked by the emergence of distinct firing responses in defined subpopulations of layer 5 (L5) pyramidal neurons whose tuft dendrites receive perirhinal inputs in L1. Learning correlated with burst firing and the enhancement of dendritic excitability, and it was suppressed by disruption of dendritic activity. Furthermore, bursts, but not regular spike trains, were sufficient to retrieve learned behavior. We conclude that hippocampal information arriving at L5 tuft dendrites in neocortical L1 mediates memory formation in the neocortex.
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Affiliation(s)
- Guy Doron
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany.
| | - Jiyun N Shin
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Naoya Takahashi
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Moritz Drüke
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Christina Bocklisch
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Salina Skenderi
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Lisa de Mont
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Maria Toumazou
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Julia Ledderose
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
| | - Michael Brecht
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, D-10115 Berlin, Germany.,NeuroCure Cluster, Charité - Universitätsmedizin Berlin, D-10117 Berlin, Germany
| | - Richard Naud
- University of Ottawa Brain and Mind Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.,Department of Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Matthew E Larkum
- Institute for Biology, Humboldt-Universität zu Berlin, D-10117 Berlin, Germany. .,NeuroCure Cluster, Charité - Universitätsmedizin Berlin, D-10117 Berlin, Germany
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46
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Ng KKW, Snow IN, Birznieks I, Vickery RM. Burst gap code predictions for tactile frequency are valid across the range of perceived frequencies attributed to two distinct tactile channels. J Neurophysiol 2021; 125:687-692. [PMID: 33439792 DOI: 10.1152/jn.00662.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Perceived frequency of vibrotactile stimuli can be divided into two distinctive cutaneous sensations-flutter (<60 Hz) and vibratory hum (>60 Hz), mediated by two different tactile afferent types [fast adapting type I (FA1) and fast adapting type II (FA2), respectively]. We recently demonstrated a novel form of neural coding in the human tactile system, where frequency perception of stimulus pulses grouped into periodic bursts in the flutter range depended on the duration of the silent gap between bursts, rather than the periodicity or mean impulse rate. Here, we investigated whether this interburst interval could also explain the perceived frequency of electrocutaneous pulse patterns delivered at frequencies above the flutter range. At stimulus rates of 50 to 190 pulses/s, the burst gap model correctly predicted the perceived frequency. This shows that the burst gap code represents a general coding strategy that spans the range of frequencies traditionally attributed to two different tactile channels.NEW & NOTEWORTHY We present evidence for a generalized frequency processing strategy on tactile afferent inputs that is shared across a broad range of frequencies extending beyond the flutter range, supporting the notion that spike timing has an important role in shaping tactile perception.
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Affiliation(s)
- Kevin K W Ng
- School of Medical Sciences, UNSW Sydney, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
| | - Ian N Snow
- School of Medical Sciences, UNSW Sydney, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
| | - Ingvars Birznieks
- School of Medical Sciences, UNSW Sydney, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
| | - Richard M Vickery
- School of Medical Sciences, UNSW Sydney, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
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47
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Stable maintenance of multiple representational formats in human visual short-term memory. Proc Natl Acad Sci U S A 2020; 117:32329-32339. [PMID: 33288707 DOI: 10.1073/pnas.2006752117] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Visual short-term memory (VSTM) enables humans to form a stable and coherent representation of the external world. However, the nature and temporal dynamics of the neural representations in VSTM that support this stability are barely understood. Here we combined human intracranial electroencephalography (iEEG) recordings with analyses using deep neural networks and semantic models to probe the representational format and temporal dynamics of information in VSTM. We found clear evidence that VSTM maintenance occurred in two distinct representational formats which originated from different encoding periods. The first format derived from an early encoding period (250 to 770 ms) corresponded to higher-order visual representations. The second format originated from a late encoding period (1,000 to 1,980 ms) and contained abstract semantic representations. These representational formats were overall stable during maintenance, with no consistent transformation across time. Nevertheless, maintenance of both representational formats showed substantial arrhythmic fluctuations, i.e., waxing and waning in irregular intervals. The increases of the maintained representational formats were specific to the phases of hippocampal low-frequency activity. Our results demonstrate that human VSTM simultaneously maintains representations at different levels of processing, from higher-order visual information to abstract semantic representations, which are stably maintained via coupling to hippocampal low-frequency activity.
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48
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Torres JJ, Baroni F, Latorre R, Varona P. Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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49
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Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields. J Neurosci 2020; 40:9634-9649. [PMID: 33168622 PMCID: PMC7726533 DOI: 10.1523/jneurosci.0276-20.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 07/06/2020] [Accepted: 07/12/2020] [Indexed: 12/18/2022] Open
Abstract
Cortical areas comprise multiple types of inhibitory interneurons, with stereotypical connectivity motifs that may follow specific plasticity rules. Yet, their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyze the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Synapses from each inhibitory population change according to distinct plasticity rules. We tested different combinations of three rules: Hebbian, anti-Hebbian, and homeostatic scaling. Depending on the inhibitory plasticity rule, synapses become unspecific (flat), anticorrelated to, or correlated with excitatory synapses. Crucially, the neuron's receptive field (i.e., its response to presynaptic stimuli) depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or nonpreferred inputs, in line with recent experimental findings that show dramatic context-dependent changes of neurons' receptive fields. We thus confirm that a neuron's receptive field does not follow directly from the weight profiles of its presynaptic afferents. Our results show how plasticity rules in various cell types can interact to shape cortical circuit motifs and their dynamics.SIGNIFICANCE STATEMENT Neurons in sensory areas of the cortex are known to respond to specific features of a given input (e.g., specific sound frequencies), but recent experimental studies show that such responses (i.e., their receptive fields) depend on context. Inspired by the cortical connectivity, we built models of excitatory and inhibitory inputs onto a single neuron, to study how receptive fields may change on short and long time scales. We show how various synaptic plasticity rules allow for the emergence of diverse connectivity profiles and, moreover, how their dynamic interaction creates a mechanism by which postsynaptic responses can quickly change. Our work emphasizes multiple roles of inhibition in cortical processing and provides a first mechanistic model for flexible receptive fields.
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50
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Hattori K, Hayakawa T, Nakanishi A, Ishida M, Yamamoto H, Hirano-Iwata A, Tanii T. Contribution of AMPA and NMDA receptors in the spontaneous firing patterns of single neurons in autaptic culture. Biosystems 2020; 198:104278. [PMID: 33075473 DOI: 10.1016/j.biosystems.2020.104278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 10/11/2020] [Accepted: 10/14/2020] [Indexed: 10/23/2022]
Abstract
Single neurons in an autaptic culture exhibit various types of firing pattern with different firing durations and rhythms. However, a neuron with autapses has often been modeled as an oscillator providing a monotonic firing pattern with a constant periodicity because of the lack of a mathematical model. In the work described in this study, we use computational simulation and whole-cell patch-clamp recording to elucidate and model the mechanism by which such neurons generate various firing pattens. In the computational simulation, three types of spontaneous firing pattern, i.e., short, long-lasting, and periodic burst firing patterns are realized by changing the combination ratio of N-methyl-d-aspartate (NMDA) to α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) conductance. These three types of firing patterns are also observed in the experiments where neurons are cultured in isolation on micropatterned substrates. Using the AMPA and NMDA current models, we discuss that, in principle, autapses can regulate rhythmicity and information selection in neuronal networks.
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Affiliation(s)
- Kouhei Hattori
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan.
| | - Takeshi Hayakawa
- Tohoku University, Research Institute of Electrical Communication, 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan
| | - Akira Nakanishi
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
| | - Mihoko Ishida
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
| | - Hideaki Yamamoto
- Tohoku University, Research Institute of Electrical Communication, 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan
| | - Ayumi Hirano-Iwata
- Tohoku University, Research Institute of Electrical Communication, 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan; Tohoku University, WPI-Advanced Institute for Materials Research (WPI-AIMR), 2-1-1 Katahira, Aoba, Sendai 980-8577, Japan
| | - Takashi Tanii
- Waseda University, Faculty of Science and Engineering, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan
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