1
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Roohi N, Valizadeh A. Role of Interaction Delays in the Synchronization of Inhibitory Networks. Neural Comput 2022; 34:1425-1447. [PMID: 35534004 DOI: 10.1162/neco_a_01500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 01/25/2022] [Indexed: 11/04/2022]
Abstract
Neural oscillations provide a means for efficient and flexible communication among different brain areas. Understanding the mechanisms of the generation of brain oscillations is crucial to determine principles of communication and information transfer in the brain circuits. It is well known that the inhibitory neurons play a major role in the generation of oscillations in the gamma range, in pure inhibitory networks, or in the networks composed of excitatory and inhibitory neurons. In this study, we explore the impact of different parameters and, in particular, the delay in the transmission of the signals between the neurons, on the dynamics of inhibitory networks. We show that increasing delay in a reasonable range increases the synchrony and stabilizes the oscillations. Unstable gamma oscillations characterized by a highly variable amplitude of oscillations can be observed in an intermediate range of delays. We show that in this range of delays, other experimentally observed phenomena such as sparse firing, variable amplitude and period, and the correlation between the instantaneous amplitude and period could be observed. The results broaden our understanding of the mechanism of the generation of the gamma oscillations in the inhibitory networks, known as the ING (interneuron-gamma) mechanism.
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Affiliation(s)
- Nariman Roohi
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences, Niavaran, Tehran, Iran
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2
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Braun W, Memmesheimer RM. High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1. PLoS Comput Biol 2022; 18:e1009891. [PMID: 35176028 PMCID: PMC8890743 DOI: 10.1371/journal.pcbi.1009891] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 03/02/2022] [Accepted: 02/02/2022] [Indexed: 11/19/2022] Open
Abstract
Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.
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Affiliation(s)
- Wilhelm Braun
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail: (WB); (R-MM)
| | - Raoul-Martin Memmesheimer
- Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
- * E-mail: (WB); (R-MM)
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3
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Tomar A, McHugh TJ. The impact of stress on the hippocampal spatial code. Trends Neurosci 2021; 45:120-132. [PMID: 34916083 DOI: 10.1016/j.tins.2021.11.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/10/2021] [Accepted: 11/18/2021] [Indexed: 12/12/2022]
Abstract
Hippocampal function is severely compromised by prolonged, uncontrollable stress. However, how stress alters neural representations of our surroundings and events that occur within them remains less clear. We review hippocampal place cell studies that examine how spatial coding is affected by acute and chronic stress, as well as by stress accompanying fear conditioning. Emerging data suggest that chronic stress disrupts the acuity and specificity of CA1 spatial coding, both in familiar and novel contexts, and alters hippocampal oscillations. By contrast, acute stress may have a facilitatory impact on spatial representations. These findings encourage a fresh look at the documented stress-induced changes in hippocampal anatomy and in vitro excitability, and offer a new perspective on the links between stress and memory.
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Affiliation(s)
- Anupratap Tomar
- Center for Synaptic Plasticity, School of Physiology, Pharmacology, and Neuroscience, University of Bristol, University Walk, Bristol BS8 1TD, UK.
| | - Thomas J McHugh
- Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, Japan.
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4
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Susin E, Destexhe A. Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states. PLoS Comput Biol 2021; 17:e1009416. [PMID: 34529655 PMCID: PMC8478196 DOI: 10.1371/journal.pcbi.1009416] [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: 05/04/2021] [Revised: 09/28/2021] [Accepted: 09/02/2021] [Indexed: 12/29/2022] Open
Abstract
Gamma oscillations are widely seen in the awake and sleeping cerebral cortex, but the exact role of these oscillations is still debated. Here, we used biophysical models to examine how Gamma oscillations may participate to the processing of afferent stimuli. We constructed conductance-based network models of Gamma oscillations, based on different cell types found in cerebral cortex. The models were adjusted to extracellular unit recordings in humans, where Gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which Gamma is generated by interneuron networks (ING) and third, a mechanism which relies on Gamma oscillations generated by pacemaker chattering neurons (CHING). We find that all three mechanisms generate features consistent with human recordings, but that the ING mechanism is most consistent with the firing rate change inside Gamma bursts seen in the human data. We next evaluated the responsiveness and resonant properties of these networks, contrasting Gamma oscillations with the asynchronous mode. We find that for both slowly-varying stimuli and precisely-timed stimuli, the responsiveness is generally lower during Gamma compared to asynchronous states, while resonant properties are similar around the Gamma band. We could not find conditions where Gamma oscillations were more responsive. We therefore predict that asynchronous states provide the highest responsiveness to external stimuli, while Gamma oscillations tend to overall diminish responsiveness. In the awake and attentive brain, the activity of neurons is typically asynchronous and irregular. It also occasionally displays oscillations in the Gamma frequency range (30–90 Hz), which are believed to be involved in information processing. Here, we use computational models to investigate how brain circuits generate oscillations in a manner consistent with microelectrode recordings in humans. We then study how these networks respond to external input, comparing asynchronous and oscillatory states. This is tested according to several paradigms, an integrative mode, where slowly varying inputs are progressively integrated, a coincidence detection mode, where brief inputs are processed according to the phase of the oscillations, and a resonance mode where the network is probed with oscillatory inputs. Surprisingly, we find that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness to external inputs. We discuss possible implications of this responsiveness decrease on information processing and propose new directions for further exploration.
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Affiliation(s)
- Eduarda Susin
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
- * E-mail:
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
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5
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Domhof JWM, Tiesinga PHE. Flexible Frequency Switching in Adult Mouse Visual Cortex Is Mediated by Competition Between Parvalbumin and Somatostatin Expressing Interneurons. Neural Comput 2021; 33:926-966. [PMID: 33513330 DOI: 10.1162/neco_a_01369] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 11/09/2020] [Indexed: 11/04/2022]
Abstract
Neuronal networks in rodent primary visual cortex (V1) can generate oscillations in different frequency bands depending on the network state and the level of visual stimulation. High-frequency gamma rhythms, for example, dominate the network's spontaneous activity in adult mice but are attenuated upon visual stimulation, during which the network switches to the beta band instead. The spontaneous local field potential (LFP) of juvenile mouse V1, however, mainly contains beta rhythms and presenting a stimulus does not elicit drastic changes in network oscillations. We study, in a spiking neuron network model, the mechanism in adult mice allowing for flexible switches between multiple frequency bands and contrast this to the network structure in juvenile mice that lack this flexibility. The model comprises excitatory pyramidal cells (PCs) and two types of interneurons: the parvalbumin-expressing (PV) and the somatostatinexpressing (SOM) interneuron. In accordance with experimental findings, the pyramidal-PV and pyramidal-SOM cell subnetworks are associated with gamma and beta oscillations, respectively. In our model, they are both generated via a pyramidal-interneuron gamma (PING) mechanism, wherein the PCs drive the oscillations. Furthermore, we demonstrate that large but not small visual stimulation activates SOM cells, which shift the frequency of resting-state gamma oscillations produced by the pyramidal-PV cell subnetwork so that beta rhythms emerge. Finally, we show that this behavior is obtained for only a subset of PV and SOM interneuron projection strengths, indicating that their influence on the PCs should be balanced so that they can compete for oscillatory control of the PCs. In sum, we propose a mechanism by which visual beta rhythms can emerge from spontaneous gamma oscillations in a network model of the mouse V1; for this mechanism to reproduce V1 dynamics in adult mice, balance between the effective strengths of PV and SOM cells is required.
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Affiliation(s)
- Justin W M Domhof
- Donders Centre for Neuroscience, Radboud University, 6500 GL Nijmegen, The Netherlands,
| | - Paul H E Tiesinga
- Donders Centre for Neuroscience, Radboud University, 6500 GL Nijmegen, The Netherlands,
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6
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Nourski KV, Steinschneider M, Rhone AE, Kovach CK, Banks MI, Krause BM, Kawasaki H, Howard MA. Electrophysiology of the Human Superior Temporal Sulcus during Speech Processing. Cereb Cortex 2020; 31:1131-1148. [PMID: 33063098 DOI: 10.1093/cercor/bhaa281] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/06/2020] [Accepted: 09/01/2020] [Indexed: 12/20/2022] Open
Abstract
The superior temporal sulcus (STS) is a crucial hub for speech perception and can be studied with high spatiotemporal resolution using electrodes targeting mesial temporal structures in epilepsy patients. Goals of the current study were to clarify functional distinctions between the upper (STSU) and the lower (STSL) bank, hemispheric asymmetries, and activity during self-initiated speech. Electrophysiologic properties were characterized using semantic categorization and dialog-based tasks. Gamma-band activity and alpha-band suppression were used as complementary measures of STS activation. Gamma responses to auditory stimuli were weaker in STSL compared with STSU and had longer onset latencies. Activity in anterior STS was larger during speaking than listening; the opposite pattern was observed more posteriorly. Opposite hemispheric asymmetries were found for alpha suppression in STSU and STSL. Alpha suppression in the STS emerged earlier than in core auditory cortex, suggesting feedback signaling within the auditory cortical hierarchy. STSL was the only region where gamma responses to words presented in the semantic categorization tasks were larger in subjects with superior task performance. More pronounced alpha suppression was associated with better task performance in Heschl's gyrus, superior temporal gyrus, and STS. Functional differences between STSU and STSL warrant their separate assessment in future studies.
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Affiliation(s)
- Kirill V Nourski
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, USA.,Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, USA
| | - Mitchell Steinschneider
- Departments of Neurology and Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Ariane E Rhone
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, USA
| | | | - Matthew I Banks
- Department of Anesthesiology, University of Wisconsin-Madison, Madison, WI 53705, USA.,Department of Neuroscience, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Bryan M Krause
- Department of Anesthesiology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Hiroto Kawasaki
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, USA
| | - Matthew A Howard
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, USA.,Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, USA.,Pappajohn Biomedical Institute, The University of Iowa, Iowa City, IA 52242, USA
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7
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Kim CM, Egert U, Kumar A. Dynamics of multiple interacting excitatory and inhibitory populations with delays. Phys Rev E 2020; 102:022308. [PMID: 32942361 DOI: 10.1103/physreve.102.022308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 07/15/2020] [Indexed: 11/07/2022]
Abstract
A network consisting of excitatory and inhibitory (EI) neurons is a canonical model for understanding local cortical network activity. In this study, we extended the local circuit model and investigated how its dynamical landscape can be enriched when it interacts with another excitatory (E) population with long transmission delays. Through analysis of a rate model and numerical simulations of a corresponding network of spiking neurons, we studied the transition from stationary to oscillatory states by analyzing the Hopf bifurcation structure in terms of two network parameters: (1) transmission delay between the EI subnetwork and the E population and (2) inhibitory couplings that induced oscillatory activity in the EI subnetwork. We found that the critical coupling strength can strongly modulate as a function of transmission delay, and consequently the stationary state can be interwoven intricately with the oscillatory state. Such a dynamical landscape gave rise to an isolated stationary state surrounded by multiple oscillatory states that generated different frequency modes, and cross-frequency coupling developed naturally at the bifurcation points. We identified the network motifs with short- and long-range inhibitory connections that underlie the emergence of oscillatory states with multiple frequencies. Thus, we provided a mechanistic explanation of how the transmission delay to and from the additional E population altered the dynamical landscape. In summary, our results demonstrated the potential role of long-range connections in shaping the network activity of local cortical circuits.
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Affiliation(s)
| | - Ulrich Egert
- Bernstein Center Freiburg, 79104 Freiburg, Germany.,Biomicrotechnology, IMTEK-Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany
| | - Arvind Kumar
- Bernstein Center Freiburg, 79104 Freiburg, Germany.,Department of Computational Science and Technology, School for Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Lindstedtsvägen 3, 11428 Stockholm, Sweden
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8
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Lu Y, Sarter M, Zochowski M, Booth V. Phasic cholinergic signaling promotes emergence of local gamma rhythms in excitatory-inhibitory networks. Eur J Neurosci 2020; 52:3545-3560. [PMID: 32293081 DOI: 10.1111/ejn.14744] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 03/02/2020] [Accepted: 03/30/2020] [Indexed: 02/06/2023]
Abstract
Recent experimental results have shown that the detection of cues in behavioral attention tasks relies on transient increases of acetylcholine (ACh) release in frontal cortex and cholinergically driven oscillatory activity in the gamma frequency band (Howe et al. Journal of Neuroscience, 2017, 37, 3215). The cue-induced gamma rhythmic activity requires stimulation of M1 muscarinic receptors. Using biophysical computational modeling, we show that a network of excitatory (E) and inhibitory (I) neurons that initially displays asynchronous firing can generate transient gamma oscillatory activity in response to simulated brief pulses of ACh. ACh effects are simulated as transient modulation of the conductance of an M-type K+ current which is blocked by activation of muscarinic receptors and has significant effects on neuronal excitability. The ACh-induced effects on the M current conductance, gKs , change network dynamics to promote the emergence of network gamma rhythmicity through a Pyramidal-Interneuronal Network Gamma mechanism. Depending on connectivity strengths between and among E and I cells, gamma activity decays with the simulated gKs transient modulation or is sustained in the network after the gKs transient has completely dissipated. We investigated the sensitivity of the emergent gamma activity to synaptic strengths, external noise and simulated levels of gKs modulation. To address recent experimental findings that cholinergic signaling is likely spatially focused and dynamic, we show that localized gKs modulation can induce transient changes of cellular excitability in local subnetworks, subsequently causing population-specific gamma oscillations. These results highlight dynamical mechanisms underlying localization of ACh-driven responses and suggest that spatially localized, cholinergically induced gamma may contribute to selectivity in the processing of competing external stimuli, as occurs in attentional tasks.
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Affiliation(s)
- Yiqing Lu
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA
| | - Martin Sarter
- Department of Psychology and Neuroscience Program, University of Michigan, Ann Arbor, MI, USA
| | - Michal Zochowski
- Departments of Physics and Biophysics, University of Michigan, Ann Arbor, MI, USA
| | - Victoria Booth
- Departments of Mathematics and Anesthesiology, University of Michigan, Ann Arbor, MI, USA
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9
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Pietras B, Devalle F, Roxin A, Daffertshofer A, Montbrió E. Exact firing rate model reveals the differential effects of chemical versus electrical synapses in spiking networks. Phys Rev E 2020; 100:042412. [PMID: 31771022 DOI: 10.1103/physreve.100.042412] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Indexed: 01/09/2023]
Abstract
Chemical and electrical synapses shape the dynamics of neuronal networks. Numerous theoretical studies have investigated how each of these types of synapses contributes to the generation of neuronal oscillations, but their combined effect is less understood. This limitation is further magnified by the impossibility of traditional neuronal mean-field models-also known as firing rate models or firing rate equations-to account for electrical synapses. Here, we introduce a firing rate model that exactly describes the mean-field dynamics of heterogeneous populations of quadratic integrate-and-fire (QIF) neurons with both chemical and electrical synapses. The mathematical analysis of the firing rate model reveals a well-established bifurcation scenario for networks with chemical synapses, characterized by a codimension-2 cusp point and persistent states for strong recurrent excitatory coupling. The inclusion of electrical coupling generally implies neuronal synchrony by virtue of a supercritical Hopf bifurcation. This transforms the cusp scenario into a bifurcation scenario characterized by three codimension-2 points (cusp, Takens-Bogdanov, and saddle-node separatrix loop), which greatly reduces the possibility for persistent states. This is generic for heterogeneous QIF networks with both chemical and electrical couplings. Our results agree with several numerical studies on the dynamics of large networks of heterogeneous spiking neurons with electrical and chemical couplings.
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Affiliation(s)
- Bastian Pietras
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences & Institute of Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, van der Boechorststraat 9, Amsterdam 1081 BT, The Netherlands.,Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom.,Institute of Mathematics, Technical University Berlin, 10623 Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Federico Devalle
- Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom.,Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Alex Roxin
- Centre de Recerca Matemàtica, Campus de Bellaterra, Edifici C, 08193 Bellaterra (Barcelona), Spain.,Barcelona Graduate School of Mathematics, 08193 Barcelona, Spain
| | - Andreas Daffertshofer
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences & Institute of Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, van der Boechorststraat 9, Amsterdam 1081 BT, The Netherlands
| | - Ernest Montbrió
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08003 Barcelona, Spain
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10
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Tikidji-Hamburyan RA, Leonik CA, Canavier CC. Phase response theory explains cluster formation in sparsely but strongly connected inhibitory neural networks and effects of jitter due to sparse connectivity. J Neurophysiol 2019; 121:1125-1142. [PMID: 30726155 DOI: 10.1152/jn.00728.2018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
We show how to predict whether a neural network will exhibit global synchrony (a one-cluster state) or a two-cluster state based on the assumption of pulsatile coupling and critically dependent upon the phase response curve (PRC) generated by the appropriate perturbation from a partner cluster. Our results hold for a monotonically increasing (meaning longer delays as the phase increases) PRC, which likely characterizes inhibitory fast-spiking basket and cortical low-threshold-spiking interneurons in response to strong inhibition. Conduction delays stabilize synchrony for this PRC shape, whereas they destroy two-cluster states, the former by avoiding a destabilizing discontinuity and the latter by approaching it. With conduction delays, stronger coupling strength can promote a one-cluster state, so the weak coupling limit is not applicable here. We show how jitter can destabilize global synchrony but not a two-cluster state. Local stability of global synchrony in an all-to-all network does not guarantee that global synchrony can be observed in an appropriately scaled sparsely connected network; the basin of attraction can be inferred from the PRC and must be sufficiently large. Two-cluster synchrony is not obviously different from one-cluster synchrony in the presence of noise and may be the actual substrate for oscillations observed in the local field potential (LFP) and the electroencephalogram (EEG) in situations where global synchrony is not possible. Transitions between cluster states may change the frequency of the rhythms observed in the LFP or EEG. Transitions between cluster states within an inhibitory subnetwork may allow more effective recruitment of pyramidal neurons into the network rhythm. NEW & NOTEWORTHY We show that jitter induced by sparse connectivity can destabilize global synchrony but not a two-cluster state with two smaller clusters firing alternately. On the other hand, conduction delays stabilize synchrony and destroy two-cluster states. These results hold if each cluster exhibits a phase response curve similar to one that characterizes fast-spiking basket and cortical low-threshold-spiking cells for strong inhibition. Either a two-cluster or a one-cluster state might provide the oscillatory substrate for neural computations.
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Affiliation(s)
- Ruben A Tikidji-Hamburyan
- Department of Cell Biology and Anatomy, Louisiana State University Health Sciences Center , New Orleans, Louisiana
| | - Conrad A Leonik
- Department of Cell Biology and Anatomy, Louisiana State University Health Sciences Center , New Orleans, Louisiana
| | - Carmen C Canavier
- Department of Cell Biology and Anatomy, Louisiana State University Health Sciences Center , New Orleans, Louisiana
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11
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Çalışkan G, Stork O. Hippocampal network oscillations at the interplay between innate anxiety and learned fear. Psychopharmacology (Berl) 2019; 236:321-338. [PMID: 30417233 DOI: 10.1007/s00213-018-5109-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 11/05/2018] [Indexed: 12/14/2022]
Abstract
The hippocampus plays a central role as a hub for episodic memory and as an integrator of multimodal sensory information in time and space. Thereby, it critically determines contextual setting and specificity of episodic memories. It is also a key site for the control of innate anxiety states and involved in psychiatric diseases with heightened anxiety and generalized fear memory such as post-traumatic stress disorder (PTSD). Expression of both innate "unlearned" anxiety and "learned" fear requires contextual processing and engagement of a brain-wide network including the hippocampus together with the amygdala and medial prefrontal cortex. Strikingly, the hippocampus is also the site of emergence of oscillatory rhythms that coordinate information processing and filtering in this network. Here, we review data on how the hippocampal network oscillations and their coordination with amygdalar and prefrontal oscillations are engaged in innate threat evaluation. We further explore how such innate oscillatory communication might have an impact on contextualization and specificity of "learned" fear. We illustrate the partial overlap of fear and anxiety networks that are built by the hippocampus in conjunction with amygdala and prefrontal cortex. We further propose that (mal)-adaptive interplay via (dis)-balanced oscillatory communication between the anxiety network and the fear network may determine the strength of fear memories and their resistance to extinction.
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Affiliation(s)
- Gürsel Çalışkan
- Department of Genetics & Molecular Neurobiology, Institute of Biology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany. .,Center for Behavioral Brain Sciences, Universitätsplatz 2, 39106, Magdeburg, Germany.
| | - Oliver Stork
- Department of Genetics & Molecular Neurobiology, Institute of Biology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany.,Center for Behavioral Brain Sciences, Universitätsplatz 2, 39106, Magdeburg, Germany
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12
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Viriyopase A, Memmesheimer RM, Gielen S. Analyzing the competition of gamma rhythms with delayed pulse-coupled oscillators in phase representation. Phys Rev E 2018; 98:022217. [PMID: 30253475 DOI: 10.1103/physreve.98.022217] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Indexed: 12/27/2022]
Abstract
Networks of neurons can generate oscillatory activity as result of various types of coupling that lead to synchronization. A prominent type of oscillatory activity is gamma (30-80 Hz) rhythms, which may play an important role in neuronal information processing. Two mechanisms have mainly been proposed for their generation: (1) interneuron network gamma (ING) and (2) pyramidal-interneuron network gamma (PING). In vitro and in vivo experiments have shown that both mechanisms can exist in the same cortical circuits. This raises the questions: How do ING and PING interact when both can in principle occur? Are the network dynamics a superposition, or do ING and PING interact in a nonlinear way and if so, how? In this article, we first generalize the phase representation for nonlinear one-dimensional pulse coupled oscillators as introduced by Mirollo and Strogatz to type II oscillators whose phase response curve (PRC) has zero crossings. We then give a full theoretical analysis for the regular gamma-like oscillations of simple networks consisting of two neural oscillators, an "E neuron" mimicking a synchronized group of pyramidal cells, and an "I neuron" representing such a group of interneurons. Motivated by experimental findings, we choose the E neuron to have a type I PRC [leaky integrate-and-fire (LIF) neuron], while the I neuron has either a type I or type II PRC (LIF or "sine" neuron). The phase representation allows us to define in a simple manner scenarios of interaction between the two neurons, which are independent of the types and the details of the neuron models. The presence of delay in the couplings leads to an increased number of scenarios relevant for gamma-like oscillatory patterns. We analytically derive the set of such scenarios and describe their occurrence in terms of parameter values such as synaptic connectivity and drive to the E and I neurons. The networks can be tuned to oscillate in an ING or PING mode. We focus particularly on the transition region where both rhythms compete to govern the network dynamics and compare with oscillations in reduced networks, which can only generate either ING or PING. Our analytically derived oscillation frequency diagrams indicate that except for small coexistence regions, the networks generate ING if the oscillation frequency of the reduced ING network exceeds that of the reduced PING network, and vice versa. For networks with the LIF I neuron, the network oscillation frequency slightly exceeds the frequencies of corresponding reduced networks, while it lies between them for networks with the sine I neuron. In networks oscillating in ING (PING) mode, the oscillation frequency responds faster to changes in the drive to the I (E) neuron than to changes in the drive to the E (I) neuron. This finding suggests a method to analyze which mechanism governs an observed network oscillation. Notably, also when the network operates in ING mode, the E neuron can spike before the I neuron such that relative spike times of the pyramidal cells and the interneurons alone are not conclusive for distinguishing ING and PING.
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Affiliation(s)
- Atthaphon Viriyopase
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.,Department of Biophysics, Faculty of Science, Radboud University Nijmegen, Nijmegen, The Netherlands.,Department of Neuroinformatics, Faculty of Science, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Raoul-Martin Memmesheimer
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.,Department of Neuroinformatics, Faculty of Science, Radboud University Nijmegen, Nijmegen, The Netherlands.,Center for Theoretical Neuroscience, Columbia University, New York, New York 10027, USA.,FIAS-Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany.,Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
| | - Stan Gielen
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.,Department of Biophysics, Faculty of Science, Radboud University Nijmegen, Nijmegen, The Netherlands
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Lowet E, Gips B, Roberts MJ, De Weerd P, Jensen O, van der Eerden J. Microsaccade-rhythmic modulation of neural synchronization and coding within and across cortical areas V1 and V2. PLoS Biol 2018; 16:e2004132. [PMID: 29851960 PMCID: PMC5997357 DOI: 10.1371/journal.pbio.2004132] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 06/12/2018] [Accepted: 05/01/2018] [Indexed: 12/13/2022] Open
Abstract
Primates sample their visual environment actively through saccades and microsaccades (MSs). Saccadic eye movements not only modulate neural spike rates but might also affect temporal correlations (synchrony) among neurons. Neural synchrony plays a role in neural coding and modulates information transfer between cortical areas. The question arises of how eye movements shape neural synchrony within and across cortical areas and how it affects visual processing. Through local field recordings in macaque early visual cortex while monitoring eye position and through neural network simulations, we find 2 distinct synchrony regimes in early visual cortex that are embedded in a 3- to 4-Hz MS-related rhythm during visual fixation. In the period shortly after an MS (“transient period”), synchrony was high within and between cortical areas. In the subsequent period (“sustained period”), overall synchrony dropped and became selective to stimulus properties. Only mutually connected neurons with similar stimulus responses exhibited sustained narrow-band gamma synchrony (25–80 Hz), both within and across cortical areas. Recordings in macaque V1 and V2 matched the model predictions. Furthermore, our modeling provides predictions on how (micro)saccade-modulated gamma synchrony in V1 shapes V2 receptive fields (RFs). We suggest that the rhythmic alternation between synchronization regimes represents a basic repeating sampling strategy of the visual system. During visual exploration, we continuously move our eyes in a quick, coordinated manner several times a second to scan our environment. These movements are called saccades. Even while we fixate on a visual object, we unconsciously execute small saccades that are termed microsaccades (MSs). Despite MSs being relatively small, they are suggested to be critical to maintain and support accurate perception during visual fixation. Here, we studied in macaques the influence of MSs on the synchronization of neural rhythms—which are important to regulate information flow in the brain—in areas of the cerebral cortex that are important for early processing of visual information, and we complemented the analysis with computational modeling. We found that synchronization properties shortly after an MS were distinct from synchronization in the later phase. Specifically, we found an early and spectrally broadband synchronization within and between visual cortices that was broadly tuned over the cortical space and stimulus properties. This was followed by narrow-band synchronization in the gamma range (25–80 Hz) that was spatially and stimulus specific. This suggests that the manner in which information is transmitted and integrated between early visual cortices depends on the timing relative to MSs. We illustrate this in a computational model showing that the receptive field (RF) of neurons in the secondary visual cortex are expected to be different depending on MS timing. Our results highlight the significance of MS timing for understanding cortical dynamics and suggest that the regulation of synchronization might be one mechanism by which MSs support visual perception.
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Affiliation(s)
- Eric Lowet
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
- * E-mail:
| | - Bart Gips
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
| | - Mark J. Roberts
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Peter De Weerd
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Ole Jensen
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Jan van der Eerden
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
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Çalışkan G, Stork O. Hippocampal network oscillations as mediators of behavioural metaplasticity: Insights from emotional learning. Neurobiol Learn Mem 2018; 154:37-53. [PMID: 29476822 DOI: 10.1016/j.nlm.2018.02.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 02/13/2018] [Accepted: 02/19/2018] [Indexed: 01/15/2023]
Abstract
Behavioural metaplasticity is evident in experience-dependent changes of network activity patterns in neuronal circuits that connect the hippocampus, amygdala and medial prefrontal cortex. These limbic regions are key structures of a brain-wide neural network that translates emotionally salient events into persistent and vivid memories. Communication in this network by-and-large depends on behavioural state-dependent rhythmic network activity patterns that are typically generated and/or relayed via the hippocampus. In fact, specific hippocampal network oscillations have been implicated to the acquisition, consolidation and retrieval, as well as the reconsolidation and extinction of emotional memories. The hippocampal circuits that contribute to these network activities, at the same time, are subject to both Hebbian and non-Hebbian forms of plasticity during memory formation. Further, it has become evident that adaptive changes in the hippocampus-dependent network activity patterns provide an important means of adjusting synaptic plasticity. We here summarise our current knowledge on how these processes in the hippocampus in interaction with amygdala and medial prefrontal cortex mediate the formation and persistence of emotional memories.
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Affiliation(s)
- Gürsel Çalışkan
- Department of Genetics & Molecular Neurobiology, Institute of Biology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany.
| | - Oliver Stork
- Department of Genetics & Molecular Neurobiology, Institute of Biology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany; Center for Behavioral Brain Sciences, Universitätsplatz 2, 39106 Magdeburg, Germany
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15
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Hippocampal Ripple Oscillations and Inhibition-First Network Models: Frequency Dynamics and Response to GABA Modulators. J Neurosci 2018; 38:3124-3146. [PMID: 29453207 DOI: 10.1523/jneurosci.0188-17.2018] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 01/25/2018] [Accepted: 02/05/2018] [Indexed: 11/21/2022] Open
Abstract
Hippocampal ripples are involved in memory consolidation, but the mechanisms underlying their generation remain unclear. Models relying on interneuron networks in the CA1 region disagree on the predominant source of excitation to interneurons: either "direct," via the Schaffer collaterals that provide feedforward input from CA3 to CA1, or "indirect," via the local pyramidal cells in CA1, which are embedded in a recurrent excitatory-inhibitory network. Here, we used physiologically constrained computational models of basket-cell networks to investigate how they respond to different conditions of transient, noisy excitation. We found that direct excitation of interneurons could evoke ripples (140-220 Hz) that exhibited intraripple frequency accommodation and were frequency-insensitive to GABA modulators, as previously shown in in vitro experiments. In addition, the indirect excitation of the basket-cell network enabled the expression of intraripple frequency accommodation in the fast-gamma range (90-140 Hz), as in vivo In our model, intraripple frequency accommodation results from a hysteresis phenomenon in which the frequency responds differentially to the rising and descending phases of the transient excitation. Such a phenomenon predicts a maximum oscillation frequency occurring several milliseconds before the peak of excitation. We confirmed this prediction for ripples in brain slices from male mice. These results suggest that ripple and fast-gamma episodes are produced by the same interneuron network that is recruited via different excitatory input pathways, which could be supported by the previously reported intralaminar connectivity bias between basket cells and functionally distinct subpopulations of pyramidal cells in CA1. Together, our findings unify competing inhibition-first models of rhythm generation in the hippocampus.SIGNIFICANCE STATEMENT The hippocampus is a part of the brain of humans and other mammals that is critical for the acquisition and consolidation of memories. During deep sleep and resting periods, the hippocampus generates high-frequency (∼200 Hz) oscillations called ripples, which are important for memory consolidation. The mechanisms underlying ripple generation are not well understood. A prominent hypothesis holds that the ripples are generated by local recurrent networks of inhibitory neurons. Using computational models and experiments in brain slices from rodents, we show that the dynamics of interneuron networks clarify several previously unexplained characteristics of ripple oscillations, which advances our understanding of hippocampus-dependent memory consolidation.
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16
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Rich S, Zochowski M, Booth V. Dichotomous Dynamics in E-I Networks with Strongly and Weakly Intra-connected Inhibitory Neurons. Front Neural Circuits 2017; 11:104. [PMID: 29326558 PMCID: PMC5733501 DOI: 10.3389/fncir.2017.00104] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 12/04/2017] [Indexed: 11/13/2022] Open
Abstract
The interconnectivity between excitatory and inhibitory neural networks informs mechanisms by which rhythmic bursts of excitatory activity can be produced in the brain. One such mechanism, Pyramidal Interneuron Network Gamma (PING), relies primarily upon reciprocal connectivity between the excitatory and inhibitory networks, while also including intra-connectivity of inhibitory cells. The causal relationship between excitatory activity and the subsequent burst of inhibitory activity is of paramount importance to the mechanism and has been well studied. However, the role of the intra-connectivity of the inhibitory network, while important for PING, has not been studied in detail, as most analyses of PING simply assume that inhibitory intra-connectivity is strong enough to suppress subsequent firing following the initial inhibitory burst. In this paper we investigate the role that the strength of inhibitory intra-connectivity plays in determining the dynamics of PING-style networks. We show that networks with weak inhibitory intra-connectivity exhibit variations in burst dynamics of both the excitatory and inhibitory cells that are not obtained with strong inhibitory intra-connectivity. Networks with weak inhibitory intra-connectivity exhibit excitatory rhythmic bursts with weak excitatory-to-inhibitory synapses for which classical PING networks would show no rhythmic activity. Additionally, variations in dynamics of these networks as the excitatory-to-inhibitory synaptic weight increases illustrates the important role that consistent pattern formation in the inhibitory cells serves in maintaining organized and periodic excitatory bursts. Finally, motivated by these results and the known diversity of interneurons, we show that a PING-style network with two inhibitory subnetworks, one strongly intra-connected and one weakly intra-connected, exhibits organized and periodic excitatory activity over a larger parameter regime than networks with a homogeneous inhibitory population. Taken together, these results serve to better articulate the role of inhibitory intra-connectivity in generating PING-like rhythms, while also revealing how heterogeneity amongst inhibitory synapses might make such rhythms more robust to a variety of network parameters.
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Affiliation(s)
- Scott Rich
- Applied and Interdisciplinary Mathematics, University of Michigan, Ann Arbor, MI, United States
| | - Michal Zochowski
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI, United States
| | - Victoria Booth
- Department of Mathematics and Anesthesiology, University of Michigan, Ann Arbor, MI, United States
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17
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Rich S, Booth V, Zochowski M. Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks. Front Neural Circuits 2016; 10:82. [PMID: 27812323 PMCID: PMC5071331 DOI: 10.3389/fncir.2016.00082] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Accepted: 10/03/2016] [Indexed: 12/05/2022] Open
Abstract
The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics.
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Affiliation(s)
- Scott Rich
- Applied and Interdisciplinary Mathematics, University of MichiganAnn Arbor, MI, USA
| | - Victoria Booth
- Departments of Mathematics and Anesthesiology, University of MichiganAnn Arbor, MI, USA
| | - Michal Zochowski
- Departments of Physics and Biophysics, University of MichiganAnn Arbor, MI, USA
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