1
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McNamee DC. The generative neural microdynamics of cognitive processing. Curr Opin Neurobiol 2024; 85:102855. [PMID: 38428170 DOI: 10.1016/j.conb.2024.102855] [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: 07/07/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 03/03/2024]
Abstract
The entorhinal cortex and hippocampus form a recurrent network that informs many cognitive processes, including memory, planning, navigation, and imagination. Neural recordings from these regions reveal spatially organized population codes corresponding to external environments and abstract spaces. Aligning the former cognitive functionalities with the latter neural phenomena is a central challenge in understanding the entorhinal-hippocampal circuit (EHC). Disparate experiments demonstrate a surprising level of complexity and apparent disorder in the intricate spatiotemporal dynamics of sequential non-local hippocampal reactivations, which occur particularly, though not exclusively, during immobile pauses and rest. We review these phenomena with a particular focus on their apparent lack of physical simulative realism. These observations are then integrated within a theoretical framework and proposed neural circuit mechanisms that normatively characterize this neural complexity by conceiving different regimes of hippocampal microdynamics as neuromarkers of diverse cognitive computations.
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2
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Sloin HE, Spivak L, Levi A, Gattegno R, Someck S, Stark E. Local activation of CA1 pyramidal cells induces theta-phase precession. Science 2024; 383:551-558. [PMID: 38301006 DOI: 10.1126/science.adk2456] [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: 08/10/2023] [Accepted: 12/21/2023] [Indexed: 02/03/2024]
Abstract
Hippocampal theta-phase precession is involved in spatiotemporal coding and in generating multineural spike sequences, but how precession originates remains unresolved. To determine whether precession can be generated directly in hippocampal area CA1 and disambiguate multiple competing mechanisms, we used closed-loop optogenetics to impose artificial place fields in pyramidal cells of mice running on a linear track. More than one-third of the CA1 artificial fields exhibited synthetic precession that persisted for a full theta cycle. By contrast, artificial fields in the parietal cortex did not exhibit synthetic precession. These findings are incompatible with precession models based on inheritance, dual-input, spreading activation, inhibition-excitation summation, or somato-dendritic competition. Thus, a precession generator resides locally within CA1.
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Affiliation(s)
- Hadas E Sloin
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Lidor Spivak
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Amir Levi
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Roni Gattegno
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Shirly Someck
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Stark
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol Department of Neurobiology, Haifa University, Haifa 3103301, Israel
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3
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Wu Y, Chen ZS. Computational models for state-dependent traveling waves in hippocampal formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.19.541436. [PMID: 37292865 PMCID: PMC10245836 DOI: 10.1101/2023.05.19.541436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Hippocampal theta (4-10 Hz) oscillations have been identified as traveling waves in both rodents and humans. In freely foraging rodents, the theta traveling wave is a planar wave propagating from the dorsal to ventral hippocampus along the septotemporal axis. Motivated from experimental findings, we develop a spiking neural network of excitatory and inhibitory neurons to generate state-dependent hippocampal traveling waves to improve current mechanistic understanding of propagating waves. Model simulations demonstrate the necessary conditions for generating wave propagation and characterize the traveling wave properties with respect to model parameters, running speed and brain state of the animal. Networks with long-range inhibitory connections are more suitable than networks with long-range excitatory connections. We further generalize the spiking neural network to model traveling waves in the medial entorhinal cortex (MEC) and predict that traveling theta waves in the hippocampus and entorhinal cortex are in sink.
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4
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Reifenstein ET, Bin Khalid I, Kempter R. Synaptic learning rules for sequence learning. eLife 2021; 10:e67171. [PMID: 33860763 PMCID: PMC8175084 DOI: 10.7554/elife.67171] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 03/31/2021] [Indexed: 12/29/2022] Open
Abstract
Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.
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Affiliation(s)
- Eric Torsten Reifenstein
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
| | - Ikhwan Bin Khalid
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu BerlinBerlinGermany
| | - Richard Kempter
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
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5
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Seenivasan P, Narayanan R. Efficient phase coding in hippocampal place cells. PHYSICAL REVIEW RESEARCH 2020; 2:033393. [PMID: 32984841 PMCID: PMC7116119 DOI: 10.1103/physrevresearch.2.033393] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Neural codes have been postulated to build efficient representations of the external world. The hippocampus, an encoding system, employs neuronal firing rates and spike phases to encode external space. Although the biophysical origin of such codes is at a single neuronal level, the role of neural components in efficient coding is not understood. The complexity of this problem lies in the dimensionality of the parametric space encompassing neural components, and is amplified by the enormous biological heterogeneity observed in each parameter. A central question that spans encoding systems therefore is how neurons arrive at efficient codes in the face of widespread biological heterogeneities. To answer this, we developed a conductance-based spiking model for phase precession, a phase code of external space exhibited by hippocampal place cells. Our model accounted for several experimental observations on place cell firing and electrophysiology: the emergence of phase precession from exact spike timings of conductance-based models with neuron-specific ion channels and receptors; biological heterogeneities in neural components and excitability; the emergence of subthreshold voltage ramp, increased firing rate, enhanced theta power within the place field; a signature reduction in extracellular theta frequency compared to its intracellular counterpart; and experience-dependent asymmetry in firing-rate profile. We formulated phase-coding efficiency, using Shannon's information theory, as an information maximization problem with spike phase as the response and external space within a single place field as the stimulus. We employed an unbiased stochastic search spanning an 11-dimensional neural space, involving thousands of iterations that accounted for the biophysical richness and neuron-to-neuron heterogeneities. We found a small subset of models that exhibited efficient spatial information transfer through the phase code, and investigated the distinguishing features of this subpopulation at the parametric and functional scales. At the parametric scale, which spans the molecular components that defined the neuron, several nonunique parametric combinations with weak pairwise correlations yielded models with similar high phase-coding efficiency. Importantly, placing additional constraints on these models in terms of matching other aspects of hippocampal neural responses did not hamper parametric degeneracy. We provide quantitative evidence demonstrating this parametric degeneracy to be a consequence of a many-to-one relationship between the different parameters and phase-coding efficiency. At the functional scale, involving the cellular-scale neural properties, our analyses revealed an important higher-order constraint that was exclusive to models exhibiting efficient phase coding. Specifically, we found a counterbalancing negative correlation between neuronal gain and the strength of external synaptic inputs as a critical functional constraint for the emergence of efficient phase coding. These observations implicate intrinsic neural properties as important contributors in effectuating such counterbalance, which can be achieved by recruiting nonunique parametric combinations. Finally, we show that a change in afferent statistics, manifesting as input asymmetry onto these neuronal models, induced an adaptive shift in the phase code that preserved its efficiency. Together, our analyses unveil parametric degeneracy as a mechanism to harness widespread neuron-to-neuron heterogeneity towards accomplishing stable and efficient encoding, provided specific higher-order functional constraints on the relationship of neural gain to external inputs are satisfied.
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6
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Tingley D, Buzsáki G. Routing of Hippocampal Ripples to Subcortical Structures via the Lateral Septum. Neuron 2020; 105:138-149.e5. [PMID: 31784288 PMCID: PMC6952543 DOI: 10.1016/j.neuron.2019.10.012] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 08/06/2019] [Accepted: 10/03/2019] [Indexed: 01/10/2023]
Abstract
The mnemonic functions of hippocampal sharp wave ripples (SPW-Rs) have been studied extensively. Because hippocampal outputs affect not only cortical but also subcortical targets, we examined the impact of SPW-Rs on the firing patterns of lateral septal (LS) neurons in behaving rats. A large fraction of SPW-Rs were temporally locked to high-frequency oscillations (HFOs) (120-180 Hz) in LS, with strongest coupling during non-rapid eye movement (NREM) sleep, followed by waking immobility. However, coherence and spike-local field potential (LFP) coupling between the two structures were low, suggesting that HFOs are generated locally within the LS GABAergic population. This hypothesis was supported by optogenetic induction of HFOs in LS. Spiking of LS neurons was largely independent of the sequential order of spiking in SPW-Rs but instead correlated with the magnitude of excitatory synchrony of the hippocampal output. Thus, LS is strongly activated by SPW-Rs and may convey hippocampal population events to its hypothalamic and brainstem targets.
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Affiliation(s)
- David Tingley
- Neuroscience Institute, New York University, New York, NY 10016, USA
| | - György Buzsáki
- Neuroscience Institute, New York University, New York, NY 10016, USA; Department of Neurology, Langone Medical Center, New York University, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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7
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Park SW, Jang HJ, Kim M, Kwag J. Spatiotemporally random and diverse grid cell spike patterns contribute to the transformation of grid cell to place cell in a neural network model. PLoS One 2019; 14:e0225100. [PMID: 31725775 PMCID: PMC6855461 DOI: 10.1371/journal.pone.0225100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 10/29/2019] [Indexed: 12/13/2022] Open
Abstract
The medial entorhinal cortex and the hippocampus are brain regions specialized in spatial information processing. While an animal navigates around an environment, grid cells in the medial entorhinal cortex spike at multiple discrete locations, forming hexagonal grid patterns, and each grid cell is spatiotemporally dynamic with a different grid size, spacing, and orientation. In contrast, place cells in the hippocampus spike when an animal is at one or more specific locations, called a “place field”. While an animal traverses through a place field, the place cell’s spike phases relative to the hippocampal theta-frequency oscillation advance in phase, known as the “spike phase precession” phenomenon and each spike encodes the specific location within the place field. Interestingly, the medial entorhinal cortical grid cells and the hippocampal place cells are only one excitatory synapse apart. However, how the spatiotemporally dynamic multi-peaked grid cell activities are transformed into hippocampal place cell activities with spike phase precession phenomenon is yet unknown. To address this question, we construct an anatomically and physiologically realistic neural network model comprised of 10,000 grid cell models, each with a spatiotemporally dynamic grid patterns and a place cell model connected by excitatory synapses. Using this neural network model, we show that grid cells’ spike activities with spatiotemporally random and diverse grid orientation, spacing, and phases as inputs to place cell are able to generate a place field with spike phase precession. These results indicate that spatiotemporally random and diverse grid cell spike activities are essential for the formation of place cell activity observed in vivo.
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Affiliation(s)
- Sahn Woo Park
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Hyun Jae Jang
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Mincheol Kim
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Jeehyun Kwag
- Neural Computational Laboratory, Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- * E-mail:
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8
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Mysin IE, Kitchigina VF, Kazanovich YB. Phase relations of theta oscillations in a computer model of the hippocampal CA1 field: Key role of Schaffer collaterals. Neural Netw 2019; 116:119-138. [DOI: 10.1016/j.neunet.2019.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 03/29/2019] [Accepted: 04/02/2019] [Indexed: 02/04/2023]
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9
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Sanders H, Ji D, Sasaki T, Leutgeb JK, Wilson MA, Lisman JE. Temporal coding and rate remapping: Representation of nonspatial information in the hippocampus. Hippocampus 2018; 29:111-127. [PMID: 30129985 DOI: 10.1002/hipo.23020] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 07/19/2018] [Accepted: 08/14/2018] [Indexed: 12/11/2022]
Abstract
Hippocampal place cells represent nonspatial information through a process called rate remapping, which involves a change in the firing rate of a place cell without changes in its spatial specificity. However, many hippocampal phenomena occur on very short time scales over which long-term average firing rates are not an appropriate description of activity. To understand how rate remapping relates to fine-scale temporal firing phenomena, we asked how rate remapping affected burst firing and trial-to-trial spike count variability. In addition, we looked at how rate remapping relates to the theta-frequency oscillations of the hippocampus, which are thought to temporally organize firing on time scales faster than 100 ms. We found that theta phase coding was preserved through changes in firing rate due to rate remapping. Interestingly, rate remapping in CA1 in response to task demands preferentially occurred during the first half of the theta cycle. The other half of the theta cycle contained preferential expression of phase precession, a phenomenon associated with place cell sequences, in agreement with previous results. This difference of place cell coding during different halves of the theta cycle supports recent theoretical suggestions that different processes occur during the two halves of the theta cycle. The differentiation between the halves of the theta cycle was not clear in recordings from CA3 during rate remapping induced by task-irrelevant sensory changes. These findings provide new insight into the way that temporal coding is utilized in the hippocampus and how rate remapping is expressed through that temporal code.
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Affiliation(s)
- Honi Sanders
- Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts.,Neuroscience Program, Brandeis University, Waltham, Massachusetts
| | - Daoyun Ji
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Takuya Sasaki
- Division of Biological Sciences, Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, California
| | - Jill K Leutgeb
- Division of Biological Sciences, Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, California
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - John E Lisman
- Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts.,Department of Biology, Brandeis University, Waltham, Massachusetts
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10
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Zutshi I, Brandon MP, Fu ML, Donegan ML, Leutgeb JK, Leutgeb S. Hippocampal Neural Circuits Respond to Optogenetic Pacing of Theta Frequencies by Generating Accelerated Oscillation Frequencies. Curr Biol 2018; 28:1179-1188.e3. [PMID: 29628373 DOI: 10.1016/j.cub.2018.02.061] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 01/23/2018] [Accepted: 02/22/2018] [Indexed: 01/25/2023]
Abstract
Biological oscillations can be controlled by a small population of rhythmic pacemaker cells, or in the brain, they also can emerge from complex cellular and circuit-level interactions. Whether and how these mechanisms are combined to give rise to oscillatory patterns that govern cognitive function are not well understood. For example, the activity of hippocampal networks is temporally coordinated by a 7- to 9-Hz local field potential (LFP) theta rhythm, yet many individual cells decouple from the LFP frequency to oscillate at frequencies ∼1 Hz higher. To better understand the network interactions that produce these complex oscillatory patterns, we asked whether the relative frequency difference between LFP and individual cells is retained when the LFP frequency is perturbed experimentally. We found that rhythmic optogenetic stimulation of medial septal GABAergic neurons controlled the hippocampal LFP frequency outside of the endogenous theta range, even during behavioral states when endogenous mechanisms would otherwise have generated 7- to 9-Hz theta oscillations. While the LFP frequency matched the optogenetically induced stimulation frequency, the oscillation frequency of individual hippocampal cells remained broadly distributed, and in a subset of cells including interneurons, it was accelerated beyond the new base LFP frequency. The inputs from septal GABAergic neurons to the hippocampus, therefore, do not appear to directly control the cellular oscillation frequency but rather engage cellular and circuit mechanisms that accelerate the rhythmicity of individual cells. Thus, theta oscillations are an example of cortical oscillations that combine inputs from a subcortical pacemaker with local computations to generate complex oscillatory patterns that support cognitive functions.
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Affiliation(s)
- Ipshita Zutshi
- Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mark P Brandon
- Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA; Douglas Hospital Research Centre, Department of Psychiatry, McGill University, 6875 LaSalle Blvd., Montreal, QC H4H 1R3, Canada.
| | - Maylin L Fu
- Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Macayla L Donegan
- Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Jill K Leutgeb
- Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Stefan Leutgeb
- Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA; Kavli Institute for Brain and Mind, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA.
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11
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Gönner L, Vitay J, Hamker FH. Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model. Front Comput Neurosci 2017; 11:84. [PMID: 29075187 PMCID: PMC5643423 DOI: 10.3389/fncom.2017.00084] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 09/01/2017] [Indexed: 01/19/2023] Open
Abstract
Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.
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Affiliation(s)
- Lorenz Gönner
- Artificial Intelligence, Department of Computer Science, Technische Universität Chemnitz, Chemnitz, Germany
| | - Julien Vitay
- Artificial Intelligence, Department of Computer Science, Technische Universität Chemnitz, Chemnitz, Germany
| | - Fred H Hamker
- Artificial Intelligence, Department of Computer Science, Technische Universität Chemnitz, Chemnitz, Germany.,Bernstein Center Computational Neuroscience, Humboldt-Universität Berlin, Berlin, Germany
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12
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Jaramillo J, Kempter R. Phase precession: a neural code underlying episodic memory? Curr Opin Neurobiol 2017; 43:130-138. [PMID: 28390862 DOI: 10.1016/j.conb.2017.02.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 01/25/2017] [Accepted: 02/08/2017] [Indexed: 11/29/2022]
Abstract
In the hippocampal formation, the sequential activation of place-specific cells represents a conceptual model for the spatio-temporal events that assemble episodic memories. The imprinting of behavioral sequences in hippocampal networks might be achieved via spike-timing-dependent plasticity and phase precession of the spiking activity of neurons. It is unclear, however, whether phase precession plays an active role by enabling sequence learning via synaptic plasticity or whether phase precession passively reflects retrieval dynamics. Here we examine these possibilities in the context of potential mechanisms generating phase precession. Knowledge of these mechanisms would allow to selectively alter phase precession and test its role in episodic memory. We finally review the few successful approaches to degrade phase precession and the resulting impact on behavior.
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Affiliation(s)
- Jorge Jaramillo
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany.
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13
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Grienberger C, Milstein AD, Bittner KC, Romani S, Magee JC. Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells. Nat Neurosci 2017; 20:417-426. [PMID: 28114296 DOI: 10.1038/nn.4486] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 12/24/2016] [Indexed: 12/13/2022]
Abstract
Place cells in the CA1 region of the hippocampus express location-specific firing despite receiving a steady barrage of heterogeneously tuned excitatory inputs that should compromise output dynamic range and timing. We examined the role of synaptic inhibition in countering the deleterious effects of off-target excitation. Intracellular recordings in behaving mice demonstrate that bimodal excitation drives place cells, while unimodal excitation drives weaker or no spatial tuning in interneurons. Optogenetic hyperpolarization of interneurons had spatially uniform effects on place cell membrane potential dynamics, substantially reducing spatial selectivity. These data and a computational model suggest that spatially uniform inhibitory conductance enhances rate coding in place cells by suppressing out-of-field excitation and by limiting dendritic amplification. Similarly, we observed that inhibitory suppression of phasic noise generated by out-of-field excitation enhances temporal coding by expanding the range of theta phase precession. Thus, spatially uniform inhibition allows proficient and flexible coding in hippocampal CA1 by suppressing heterogeneously tuned excitation.
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Affiliation(s)
| | - Aaron D Milstein
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA
| | - Katie C Bittner
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA
| | - Sandro Romani
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA
| | - Jeffrey C Magee
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA
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14
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Cheron G, Márquez-Ruiz J, Dan B. Oscillations, Timing, Plasticity, and Learning in the Cerebellum. THE CEREBELLUM 2016; 15:122-38. [PMID: 25808751 DOI: 10.1007/s12311-015-0665-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.
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Affiliation(s)
- G Cheron
- Laboratory of Electrophysiology, Université de Mons, 7000, Mons, Belgium. .,Laboratory of Neurophysiology and Movement Biomechanics, ULB Neuroscience Institute, Université Libre de Bruxelles, CP640, 1070, Brussels, Belgium.
| | - J Márquez-Ruiz
- División de Neurociencias, Universidad Pablo de Olavide, 41013, Seville, Spain
| | - B Dan
- Laboratory of Neurophysiology and Movement Biomechanics, ULB Neuroscience Institute, Université Libre de Bruxelles, CP640, 1070, Brussels, Belgium.,Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, 1020, Brussels, Belgium
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15
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Chadwick A, van Rossum MC, Nolan MF. Flexible theta sequence compression mediated via phase precessing interneurons. eLife 2016; 5. [PMID: 27929374 PMCID: PMC5245972 DOI: 10.7554/elife.20349] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 12/07/2016] [Indexed: 01/15/2023] Open
Abstract
Encoding of behavioral episodes as spike sequences during hippocampal theta oscillations provides a neural substrate for computations on events extended across time and space. However, the mechanisms underlying the numerous and diverse experimentally observed properties of theta sequences remain poorly understood. Here we account for theta sequences using a novel model constrained by the septo-hippocampal circuitry. We show that when spontaneously active interneurons integrate spatial signals and theta frequency pacemaker inputs, they generate phase precessing action potentials that can coordinate theta sequences in place cell populations. We reveal novel constraints on sequence generation, predict cellular properties and neural dynamics that characterize sequence compression, identify circuit organization principles for high capacity sequential representation, and show that theta sequences can be used as substrates for association of conditioned stimuli with recent and upcoming events. Our results suggest mechanisms for flexible sequence compression that are suited to associative learning across an animal’s lifespan. DOI:http://dx.doi.org/10.7554/eLife.20349.001 Nerve cells in the brain exchange information via electrical impulses. In a given brain area, the electrical impulses at any particular moment can be thought of as forming a code that represents an aspect of the outside world. For example, groups of nerve cells in the hippocampus generate a type of code called a theta sequence, which represents a series of recent and upcoming events. The specific timing of electrical impulses within a theta sequence is crucial in creating certain types of memory. There are two major classes of nerve cell in the brain: excitatory cells activate impulses in neighbouring cells, while inhibitory cells act to temporarily block impulses from other nerve cells. Many groups, or “circuits”, of nerve cells contain combinations of both cell types to control how and when they communicate. Previous studies show that both types of cell are active within theta sequences, but it is not known precisely how they contribute to creating the sequences. Chadwick et al. developed a new mathematical model that simulates how theta sequences can emerge from circuits of both excitatory and inhibitory nerve cells. The connections between these simulated cells are based on experimental data from real nerve cells in the hippocampus. The model predicts that inhibitory cells play an important role in generating theta sequences by interacting with groups of excitatory cells to coordinate the timing of electrical impulses. Furthermore, the model shows how memory capacity depends on these connections. The next step following on from this work is to carry out experiments to test the model’s predictions. This will include monitoring the same group of nerve cells in multiple different situations to find out how their theta sequences change, and recording electrical events in individual nerve cells during theta sequences. If the theory’s predictions are confirmed this would lead to a deeper understanding of how our brains remember sequences of events. DOI:http://dx.doi.org/10.7554/eLife.20349.002
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Affiliation(s)
- Angus Chadwick
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Scotland, United Kingdom.,Neuroinformatics Doctoral Training Centre, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark Cw van Rossum
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Scotland, United Kingdom
| | - Matthew F Nolan
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom
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Cell Type-Specific Differences in Spike Timing and Spike Shape in the Rat Parasubiculum and Superficial Medial Entorhinal Cortex. Cell Rep 2016; 16:1005-1015. [PMID: 27425616 PMCID: PMC4967475 DOI: 10.1016/j.celrep.2016.06.057] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Revised: 05/04/2016] [Accepted: 06/12/2016] [Indexed: 01/05/2023] Open
Abstract
The medial entorhinal cortex (MEC) and the adjacent parasubiculum are known for their elaborate spatial discharges (grid cells, border cells, etc.) and the precessing of spikes relative to the local field potential. We know little, however, about how spatio-temporal firing patterns map onto cell types. We find that cell type is a major determinant of spatio-temporal discharge properties. Parasubicular neurons and MEC layer 2 (L2) pyramids have shorter spikes, discharge spikes in bursts, and are theta-modulated (rhythmic, locking, skipping), but spikes phase-precess only weakly. MEC L2 stellates and layer 3 (L3) neurons have longer spikes, do not discharge in bursts, and are weakly theta-modulated (non-rhythmic, weakly locking, rarely skipping), but spikes steeply phase-precess. The similarities between MEC L3 neurons and MEC L2 stellates on one hand and parasubicular neurons and MEC L2 pyramids on the other hand suggest two distinct streams of temporal coding in the parahippocampal cortex. We find cell type-specific differences in spike shape, burstiness, and phase precession In vivo cell type specificity does not match predictions from previous in vitro studies Anatomical identity is a major determinant of spike patterns in the parahippocampal cortex
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17
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Abstract
UNLABELLED The identity of phase-precessing cells in the entorhinal cortex is unknown. Here, we used a classifier derived from cell-attached recordings to separate putative pyramidal cells and putative stellate cells recorded extracellularly in layer II of the medial entorhinal cortex in rats. Using a novel method to identify single runs as temporal periods of elevated spiking activity, we find that both cell types show phase precession but putative stellate cells show steeper slopes of phase precession and larger phase ranges. As the two classes of cells have different projection patterns, phase precession is differentially passed on to different subregions of the hippocampal formation. SIGNIFICANCE STATEMENT It is a great challenge for neuroscience to reveal the cellular basis of cognitive functions. One such function is the ability to learn and recollect temporal sequences of events. The representation of sequences in the brain is thought to require temporally structured activity of nerve cells. How different types of neurons generate temporally structured activity is currently unknown. In the present study, we use a computational classification procedure to separate different cell types and find that a subpopulation of cells, so-called stellate neurons, exhibits clear temporal coding. Contrary to the stellate cells, pyramidal cells show weaker temporal coding. This discovery sheds light on the cellular basis of temporal coding in the brain.
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18
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Korte M, Schmitz D. Cellular and System Biology of Memory: Timing, Molecules, and Beyond. Physiol Rev 2016; 96:647-93. [PMID: 26960344 DOI: 10.1152/physrev.00010.2015] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The storage of information in the mammalian nervous systems is dependent on a delicate balance between change and stability of neuronal networks. The induction and maintenance of processes that lead to changes in synaptic strength to a multistep process which can lead to long-lasting changes, which starts and ends with a highly choreographed and perfectly timed dance of molecules in different cell types of the central nervous system. This is accompanied by synchronization of specific networks, resulting in the generation of characteristic "macroscopic" rhythmic electrical fields, whose characteristic frequencies correspond to certain activity and information-processing states of the brain. Molecular events and macroscopic fields influence each other reciprocally. We review here cellular processes of synaptic plasticity, particularly functional and structural changes, and focus on timing events that are important for the initial memory acquisition, as well as mechanisms of short- and long-term memory storage. Then, we cover the importance of epigenetic events on the long-time range. Furthermore, we consider how brain rhythms at the network level participate in processes of information storage and by what means they participating in it. Finally, we examine memory consolidation at the system level during processes of sleep.
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Affiliation(s)
- Martin Korte
- Zoological Institute, Division of Cellular Neurobiology, Braunschweig, Germany; Helmholtz Centre for Infection Research, AG NIND, Braunschweig, Germany; and Neuroscience Research Centre, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dietmar Schmitz
- Zoological Institute, Division of Cellular Neurobiology, Braunschweig, Germany; Helmholtz Centre for Infection Research, AG NIND, Braunschweig, Germany; and Neuroscience Research Centre, Charité Universitätsmedizin Berlin, Berlin, Germany
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19
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Silencing CA3 disrupts temporal coding in the CA1 ensemble. Nat Neurosci 2016; 19:945-51. [DOI: 10.1038/nn.4311] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 04/28/2016] [Indexed: 12/11/2022]
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20
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Sanders H, Rennó-Costa C, Idiart M, Lisman J. Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function. Trends Neurosci 2015; 38:763-775. [PMID: 26616686 DOI: 10.1016/j.tins.2015.10.004] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 09/25/2015] [Accepted: 10/18/2015] [Indexed: 12/16/2022]
Abstract
Much has been learned about the hippocampal/entorhinal system, but an overview of how its parts work in an integrated way is lacking. One question regards the function of entorhinal grid cells. We propose here that their fundamental function is to provide a coordinate system for producing mind-travel in the hippocampus, a process that accesses associations with upcoming positions. We further propose that mind-travel occurs during the second half of each theta cycle. By contrast, the first half of each theta cycle is devoted to computing current position using sensory information from the lateral entorhinal cortex (LEC) and path integration information from the medial entorhinal cortex (MEC). This model explains why MEC lesions can abolish hippocampal phase precession but not place fields.
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Affiliation(s)
- Honi Sanders
- Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA
| | - César Rennó-Costa
- Brain Institute, Federal University of Rio Grande do Norte, Natal, RN 59066, Brazil
| | - Marco Idiart
- Physics Institute, Federal University of Rio Grande do Sul, Avenida Bento Gonçalves 9500, Porto Alegre, RS, 91501-970, Brazil
| | - John Lisman
- Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.
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21
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D’Albis T, Jaramillo J, Sprekeler H, Kempter R. Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession on Structure Formation. Neural Comput 2015; 27:1624-72. [DOI: 10.1162/neco_a_00752] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
A place cell is a neuron that fires whenever the animal traverses a particular location of the environment—the place field of the cell. Place cells are found in two regions of the rodent hippocampus: CA3 and CA1. Motivated by the anatomical connectivity between these two regions and by the evidence for synaptic plasticity at these connections, we study how a place field in CA1 can be inherited from an upstream region such as CA3 through a Hebbian learning rule, in particular, through spike-timing-dependent plasticity (STDP). To this end, we model a population of CA3 place cells projecting to a single CA1 cell, and we assume that the CA1 input synapses are plastic according to STDP. With both numerical and analytical methods, we show that in the case of overlapping CA3 input place fields, the STDP learning rule leads to the formation of a place field in CA1. We then investigate the roles of the hippocampal theta modulation and phase precession on the inheritance process. We find that theta modulation favors the inheritance and leads to faster place field formation whereas phase precession changes the drift of CA1 place fields over time.
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Affiliation(s)
- Tiziano D’Albis
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany, and Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Jorge Jaramillo
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany, and Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Henning Sprekeler
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany, and Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Richard Kempter
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany, and Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
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22
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Schlesiger MI, Cannova CC, Boublil BL, Hales JB, Mankin EA, Brandon MP, Leutgeb JK, Leibold C, Leutgeb S. The medial entorhinal cortex is necessary for temporal organization of hippocampal neuronal activity. Nat Neurosci 2015; 18:1123-32. [PMID: 26120964 PMCID: PMC4711275 DOI: 10.1038/nn.4056] [Citation(s) in RCA: 122] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 06/04/2015] [Indexed: 01/05/2023]
Abstract
The superficial layers of the medial entorhinal cortex (MEC) are the major input to the hippocampus. The high proportion of spatially modulated cells, including grid cells and border cells, in these layers suggests that the MEC inputs to the hippocampus are critical for the representation of space in the hippocampus. However, selective manipulations of the MEC do not completely abolish hippocampal spatial firing. To therefore determine whether other hippocampal firing characteristics depend more critically on MEC inputs, we recorded from hippocampal CA1 cells in rats with MEC lesions. Strikingly, theta phase precession was substantially disrupted, even during periods of stable spatial firing. Our findings indicate that MEC inputs to the hippocampus are required for the temporal organization of hippocampal firing patterns and suggest that cognitive functions that depend on precise neuronal sequences within the hippocampal theta cycle are particularly dependent on the MEC.
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Affiliation(s)
- Magdalene I Schlesiger
- 1] Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, California, USA. [2] Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Christopher C Cannova
- Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, California, USA
| | - Brittney L Boublil
- Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, California, USA
| | - Jena B Hales
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Emily A Mankin
- Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, California, USA
| | - Mark P Brandon
- Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, California, USA
| | - Jill K Leutgeb
- Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, California, USA
| | - Christian Leibold
- Department Biology II, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Stefan Leutgeb
- 1] Neurobiology Section and Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, California, USA. [2] Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, California, USA
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23
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Place field expansion after focal MEC inactivations is consistent with loss of Fourier components and path integrator gain reduction. Proc Natl Acad Sci U S A 2015; 112:4116-21. [PMID: 25733884 DOI: 10.1073/pnas.1421963112] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Both hippocampal place fields and medial entorhinal cortex (MEC) grid fields increase in scale along the dorsoventral axis. Because the connections from MEC to hippocampus are topographically organized and divergent, it has been hypothesized that place fields are generated by a Fourier-like summation of inputs over a range of spatial scales. This hypothesis predicts that inactivation of dorsal MEC should cause place field expansion, whereas inactivation of ventral MEC should cause field contraction. Inactivation of dorsal MEC caused substantial expansion of place fields; however, as inactivations were made more ventrally, the effect diminished but never switched to contraction. Expansion was accompanied by proportional decreases in theta power, intrinsic oscillation frequencies, phase precession slopes, and firing rates. Our results are most consistent with the predicted loss of specific Fourier components coupled with a path integration gain reduction, which raises the overall place field scale and masks the contraction expected from ventral inactivations.
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24
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Ritter P, Born J, Brecht M, Dinse HR, Heinemann U, Pleger B, Schmitz D, Schreiber S, Villringer A, Kempter R. State-dependencies of learning across brain scales. Front Comput Neurosci 2015; 9:1. [PMID: 25767445 PMCID: PMC4341560 DOI: 10.3389/fncom.2015.00001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 01/06/2015] [Indexed: 01/09/2023] Open
Abstract
Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.
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Affiliation(s)
- Petra Ritter
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Charité University Medicine Berlin Berlin, Germany ; Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt-Universität zu Berlin Berlin, Germany
| | - Jan Born
- Department of Medical Psychology and Behavioral Neurobiology & Center for Integrative Neuroscience (CIN), University of Tübingen Tübingen, Germany
| | - Michael Brecht
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany
| | - Hubert R Dinse
- Neural Plasticity Lab, Institute for Neuroinformatics, Ruhr-University Bochum Bochum, Germany ; Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum Bochum, Germany
| | - Uwe Heinemann
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; NeuroCure Cluster of Excellence Berlin, Germany
| | - Burkhard Pleger
- Clinic for Cognitive Neurology, University Hospital Leipzig Leipzig, Germany ; Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany
| | - Dietmar Schmitz
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; NeuroCure Cluster of Excellence Berlin, Germany ; Neuroscience Research Center NWFZ, Charité University Medicine Berlin Berlin, Germany ; Max-Delbrück Center for Molecular Medicine, MDC Berlin, Germany ; Center for Neurodegenerative Diseases (DZNE) Berlin, Germany
| | - Susanne Schreiber
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Department of Biology, Institute for Theoretical Biology (ITB), Humboldt-Universität zu Berlin Berlin, Germany
| | - Arno Villringer
- Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt-Universität zu Berlin Berlin, Germany ; Clinic for Cognitive Neurology, University Hospital Leipzig Leipzig, Germany ; Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany
| | - Richard Kempter
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Department of Biology, Institute for Theoretical Biology (ITB), Humboldt-Universität zu Berlin Berlin, Germany
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25
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Chadwick A, van Rossum MCW, Nolan MF. Independent theta phase coding accounts for CA1 population sequences and enables flexible remapping. eLife 2015; 4. [PMID: 25643396 PMCID: PMC4383210 DOI: 10.7554/elife.03542] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2014] [Accepted: 02/01/2015] [Indexed: 12/27/2022] Open
Abstract
Hippocampal place cells encode an animal's past, current, and future location
through sequences of action potentials generated within each cycle of the network
theta rhythm. These sequential representations have been suggested to result from
temporally coordinated synaptic interactions within and between cell assemblies.
Instead, we find through simulations and analysis of experimental data that rate and
phase coding in independent neurons is sufficient to explain the organization of CA1
population activity during theta states. We show that CA1 population activity can be
described as an evolving traveling wave that exhibits phase coding, rate coding,
spike sequences and that generates an emergent population theta rhythm. We identify
measures of global remapping and intracellular theta dynamics as critical for
distinguishing mechanisms for pacemaking and coordination of sequential population
activity. Our analysis suggests that, unlike synaptically coupled assemblies,
independent neurons flexibly generate sequential population activity within the
duration of a single theta cycle. DOI:http://dx.doi.org/10.7554/eLife.03542.001 When we explore a new place, we naturally create a mental map of the location as we
go. This mental map is stored in a region of the brain called the hippocampus, which
contains cells called place cells. These cells can carry information about our past,
present, and future location in the form of electrical signals. They connect to each
other to form networks and it has been proposed that these connections can store the
information needed for the mental maps. Real-time maps are represented in the information carried by the electrical signals
themselves. A physical location is specified by the individual place cell that is
activated, and by the timing of the electrical signal it produces relative to a
‘brain wave’ called the theta rhythm. Brain waves are patterns of
electrical signals activated in sets of brain cells and the theta rhythm is produced
in the hippocampus of an animal as it explores its surroundings. Previous experiments suggested that when a rat explores an area, several sets of
brain cells in the hippocampus are activated in sequence within each cycle of the
theta rhythm. As the rat moves forward, the sequence shifts to different sets of
cells to reflect the upcoming locations ahead of the rat. It has been thought that
these sequences are triggered by the individual connections between the place
cells. Here, Chadwick et al. developed mathematical models of the electrical activity in the
brains of rats as they explored. They used these models to analyze data from previous
experiments and found that the sequences of electrical activity arise from the timing
of each cell's activity relative to the theta rhythm, rather than from the
connections between the cells. Chadwick et al.'s findings suggest that the mental map may be highly flexible,
allowing vast numbers of distinct memories to be stored within the same network of
place cells without interference. Future studies will involve investigating the role
of brain waves in the forming new mental maps and creating new memories. DOI:http://dx.doi.org/10.7554/eLife.03542.002
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Affiliation(s)
- Angus Chadwick
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark C W van Rossum
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Matthew F Nolan
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom
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26
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Wang Y, Romani S, Lustig B, Leonardo A, Pastalkova E. Theta sequences are essential for internally generated hippocampal firing fields. Nat Neurosci 2014; 18:282-8. [PMID: 25531571 DOI: 10.1038/nn.3904] [Citation(s) in RCA: 176] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 11/24/2014] [Indexed: 11/09/2022]
Abstract
Sensory cue inputs and memory-related internal brain activities govern the firing of hippocampal neurons, but which specific firing patterns are induced by either of the two processes remains unclear. We found that sensory cues guided the firing of neurons in rats on a timescale of seconds and supported the formation of spatial firing fields. Independently of the sensory inputs, the memory-related network activity coordinated the firing of neurons not only on a second-long timescale, but also on a millisecond-long timescale, and was dependent on medial septum inputs. We propose a network mechanism that might coordinate this internally generated firing. Overall, we suggest that two independent mechanisms support the formation of spatial firing fields in hippocampus, but only the internally organized system supports short-timescale sequential firing and episodic memory.
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Affiliation(s)
| | - Sandro Romani
- 1] Janelia Research Campus, Ashburn, Virginia, USA. [2] Center for Theoretical Neuroscience, Columbia University, New York, New York, USA
| | - Brian Lustig
- 1] Janelia Research Campus, Ashburn, Virginia, USA. [2] University of Chicago, Neuroscience Graduate Program, Chicago, Illinois, USA
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