A network of AOB mitral cells that produces infra-slow bursting (Zylbertal et al. 2017)

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Infra-slow rhythmic neuronal activity with very long (> 10 s) period duration was described in many brain areas but little is known about the role of this activity and the mechanisms that produce it. Here we combine experimental and computational methods to show that synchronous infra-slow bursting activity in mitral cells of the mouse accessory olfactory bulb (AOB) emerges from interplay between intracellular dynamics and network connectivity. In this novel mechanism, slow intracellular Na+ dynamics endow AOB mitral cells with a weak tendency to burst, which is further enhanced and stabilized by chemical and electrical synapses between them. Combined with the unique topology of the AOB network, infra-slow bursting enables integration and binding of multiple chemosensory stimuli over prolonged time scale. The example protocol simulates a two-glomeruli network with a single shared cell. Although each glomerulus is stimulated at a different time point, the activity of the entire population becomes synchronous (see paper Fig. 8)
1 . Zylbertal A, Yarom Y, Wagner S (2017) Synchronous Infra-Slow Bursting in the Mouse Accessory Olfactory Bulb Emerge from Interplay between Intrinsic Neuronal Dynamics and Network Connectivity. J Neurosci 37:2656-2672 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb (accessory) mitral cell;
Channel(s): I CAN; Na/Ca exchanger; Na/K pump; I Calcium; I Na,t;
Gap Junctions: Gap junctions;
Simulation Environment: NEURON; Python;
Model Concept(s): Bursting; Synchronization; Activity Patterns; Oscillations; Persistent activity; Olfaction;
Implementer(s): Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il];
Search NeuronDB for information about:  I Na,t; I CAN; I Calcium; Na/Ca exchanger; Na/K pump;
(C) Asaph Zylbertal 01.10.16, HUJI, Jerusalem, Israel

Basic model functions: stimulation and recording
If you use this model in your research please cite:


import numpy as np
import neuron

class mitral_neuron(object):

    def __del__(self):
        self.soma = None
        self.basl = None
        self.apic1 = None
        self.tuft1 = None
        self.apic2 = None
        self.tuft2 = None
        self.hlck = None
        self.iseg = None
        self.axon = None
        self.root = None

        neuron.h("forall delete_section()")

    # Application of a square pulse stimulation

    def init_square_stim(self, amp):

        stim = neuron.h.IClamp(self.root(0.5))

        stim.delay = 100
        stim.amp = amp
        stim.dur = 400
        self.stim = stim

    # Application of pulse train stimulation

    def init_train_stim(self, delay, duration, freq,
                        pulse_duration, amp, dc, limit_dc=False, noise_stdv=0):
        stim = []
        pulse_num = int(duration * freq)

        for i in range(pulse_num):

            stim[i].delay = delay + i / freq
            stim[i].amp = amp
            stim[i].dur = pulse_duration
        stim[i + 1].amp = dc
        if limit_dc:
            stim[i + 1].delay = delay
            stim[i + 1].dur = duration
            stim[i + 1].delay = 0
            stim[i + 1].dur = self.sim_time

        if noise_stdv > 0:


            stim[i + 2].delay = 0
            stim[i + 2].dur = self.sim_time

            noise_t = np.linspace(0, self.sim_time, self.sim_time)
            t_vec = neuron.h.Vector(noise_t)
            noise_vec = np.random.normal(0, noise_stdv, self.sim_time)
            self.nstim_vec = neuron.h.Vector(noise_vec)
            self.nstim_vec.play(stim[i + 2]._ref_amp, t_vec)

        self.stim = stim

    # Record voltage from a specific segment

    def init_recording(self, seg):

        self.rec_v = self.init_vec_recording(seg._ref_v)
        self.rec_t = self.init_vec_recording(neuron.h._ref_t)

    # Record arbitrary time series

    def init_vec_recording(self, ref):
        vec = neuron.h.Vector()
        return vec

    def init_vector_stim(self, t, vec):

        self.t_vec = neuron.h.Vector(t)
        self.stim_vec = neuron.h.Vector(vec)
        self.stim = neuron.h.IClamp(self.root(0.5))
        self.stim.delay = 0
        self.stim.dur = self.t_vec.x[len(self.t_vec.x) - 1]
        self.stim_vec.play(self.stim._ref_amp, self.t_vec)

    def stop_recording(self):

        if hasattr(self, 'rec_v'):
            del self.rec_v
        if hasattr(self, 'rec_t'):
            del self.rec_t
        if hasattr(self, 'rec_f'):
            del self.rec_f

    # Run the model untill a steady state is reached

    def init_steady_state(
            self, test_seg, init_run_chunk=500., min_slope=0.001, max_run=2000000.):

        v = neuron.h.Vector()
        t = neuron.h.Vector()

        self.steady = neuron.h.SaveState()    # define state object

        if self.cv.active() == 1:


        good_chunk = False
        failed = False

        chunks_so_far = 0
        chunk_start = 0
        while ((not good_chunk) and (not failed)):

            run_point = init_run_chunk * (chunks_so_far + 1)
            chunks_so_far += 1


            ta = np.array(t)[chunk_start:]
            va = np.array(v)[chunk_start:]

                has_spikes = np.max(va) > 30

                has_spikes = False
            if len(va) > 1:
                slope = abs(va[0] - va[-1:]) / init_run_chunk
                chunk_start = chunk_start + len(ta) + 1

                if (slope < min_slope) and (not has_spikes) and (len(ta) > 1):
                    good_chunk = True
                if (run_point > max_run):
                    failed = True

        del v
        del t
        del ta

        return (run_point)

    def run_model(self):

        neuron.h.t = 0.
        if self.cv.active() == 1: