© HumanBrainProject
83 electrophysiological neuron models published
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Using a data-driven model of a hippocampal microcircuit, we demonstrate that theta-gamma phase amplitude coupling (PAC) can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma..
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This Python code implements a phenomenological NMDA receptor-based voltage-dependent model of synaptic plasticity for CA3-CA1 synapse and shows weight changes of a synapse placed on a two-compartmental model of a hippocampal CA1 pyramidal neuron for spike-timing-dependent synaptic plasticity (STDP) and frequency-dependent synaptic plasticity stimulation protocols. The developed model predicts altered learning rules in synapses formed on the apical dendrites of the detailed compartmental model of CA1 pyramidal neuron in the presence of the GluN2B-NMDA receptor hypofunction.
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The model allows to connect point neurons based on probability clouds generated on morpho-anatomical landmarks
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Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding
the correct way to exit a maze following a sequence of cues, does not need a long training session. Just
a single or a few runs through a new environment is, in most cases, sufficient to learn an exit path
starting from anywhere in the maze. This ability is in striking contrast with the well-known difficulty
that any deep learning algorithm has in learning a trajectory through a sequence of objects. Being
able to learn an arbitrarily long sequence of objects to reach a specific place could take, in general,
prohibitively long training sessions. This is a clear indication that current artificial intelligence methods
are essentially unable to capture the way in which a real brain implements a cognitive function. In
previous work, we have proposed a proof-of-principle model demonstrating how, using hippocampal
circuitry, it is possible to learn an arbitrary sequence of known objects in a single trial. We called
this model SLT (Single Learning Trial). In the current work, we extend this model, which we will call
e-STL, to introduce the capability of navigating a classic four-arms maze to learn, in a single trial,
the correct path to reach an exit ignoring dead ends. We show the conditions under which the e-
SLT network, including cells coding for places, head-direction, and objects, can robustly and efficiently
implement a fundamental cognitive function. The results shed light on the possible circuit organization
and operation of the hippocampus and may represent the building block of a new generation of
artificial intelligence algorithms for spatial navigation.
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Gradual decline in cognitive and non-cognitive functions are considered clinical hallmarks of Alzheimer's Disease (AD). Post-mortem autoptic analysis shows the presence of amyloid ß deposits, neuroinflammation and severe brain atrophy. However, brain circuit alterations and cellular derailments, assessed in very early stages of AD, still remain elusive. The understanding of these early alterations is crucial to tackle defective mechanisms.
In a previous study we proved that the Tg2576 mouse model of AD displays functional deficits in the dorsal hippocampus and relevant behavioural AD-related alterations. We had shown that these deficits in Tg2576 mice correlate with the precocious degeneration of dopamine (DA) neurons in the Ventral Tegmental Area (VTA) and can be restored by L-DOPA treatment. Due to the distinct functionality and connectivity of dorsal versus ventral hippocampus, here we investigated neuronal excitability and synaptic functionality in the ventral CA1 hippocampal sub-region of Tg2576 mice. We found an age-dependent alteration of cell excitability and firing in pyramidal neurons starting at 3 months of age, that correlates with reduced levels in the ventral CA1 of tyrosine hydroxylase – the rate-limiting enzyme of DA synthesis. Additionally, at odds with the dorsal hippocampus, we found no alterations in basal glutamatergic transmission and long-term plasticity of ventral neurons in 8-month old Tg2576 mice compared to age-matched controls. Last, we used computational analysis to model the early derailments of firing properties observed and hypothesize that the neuronal alterations found could depend on dysfunctional sodium and potassium conductances, leading to anticipated depolarization-block of action potential firing. The present study depicts that impairment of cell excitability and homeostatic control of firing in ventral CA1 pyramidal neurons is a prodromal feature in Tg2576 AD mice.
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A number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highlight the importance of using the appropriate network connectivity to investigate epileptiform activity with computational models.
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ABSTRACT: In this work we highlight an electrophysiological feature, often observed in recordings from mouse CA1 pyramidal cells, which has been so far ignored by experimentalists and modelers. It consists of a large and dynamic increase in the depolarization baseline (i.e. the minimum value of the membrane potential between successive action potentials during a sustained input) in response to strong somatic current injections. Such an increase can directly affect neurotransmitter release properties and, more generally, efficacy of synaptic transmission. However, it cannot be explained by any currently available conductance-based computational model. Here we present a model addressing this issue, demonstrating that experimental recordings can be reproduced by assuming that an input current modifies, in a time-dependent manner, the electrical and permeability properties of the neuron membrane by shifting the ionic reversal potentials and channel kinetics. For this reason, we propose that any detailed model of ion channel kinetics, for neurons exhibiting this characteristic, should be adapted to correctly represent the response and the synaptic integration process during strong and sustained inputs.
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We simulated chirp current stimulation in the apical dendrites of 5 biophysically-detailed multi-compartment models of neocortical pyramidal tract neurons and found that a combination of HCN channels and TASK-like channels produced the best fit to experimental measurements of dendritic impedance. We then explored how HCN and TASK-like channels can shape the dendritic impedance as well as the voltage response to synaptic currents.
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In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions.
In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus. By directly following the natural system’s layout and circuitry, this type of implementation has the additional advantage that the results can be more easily compared to experimental data, allowing a deeper and more direct understanding of the mechanisms underlying cognitive functions and dysfunctions.
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Dorsal and ventral medial entorhinal cortex (mEC) regions have distinct neural network
firing patterns to differentially support functions such as spatial memory. Correspondingly,
mEC layer II stellate neuron action potential frequencies vary across the dorsal-ventral axis,
with dorsal neurons exhibiting lower firing rates than ventral neurons. This has been partly
attributed to higher densities of inhibitory conductances in dorsal compared to ventral
neurons. We asked whether additional conductances might also impact this dorsal-ventral
gradient in spike firing. We report that T-type Ca 2+ current amplitudes increased three-fold
along the dorsal-ventral axis in mEC layer II stellate neurons. Twice as much Ca V 3.2 mRNA
was also detected in ventral mEC compared with dorsal mEC. Unusually, as T-type Ca 2+
currents are only transiently active, long depolarizing stimuli applied to ventral, and not
dorsal, stellate neurons triggered these currents to cause a sustained rise in membrane voltage
and spike firing. This effect was due to T-type Ca 2+ currents acting in concert with persistent
Na + currents. T-type Ca 2+ currents themselves prolonged excitatory post-synaptic potentials
(EPSPs) to enhance the summation of EPSP trains and augment EPSP-spike coupling in
ventral neurons. In contrast, T-type Ca 2+ currents had no effect on dorsal EPSP spike-
coupling. These findings indicate that by preferentially regulating ventral neuron spike firing
and synaptic potential integration, T-type Ca 2+ currents critically influence the dorsal-ventral
gradient in mEC stellate neuron excitability and associated circuit activity.
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The brain’s structural connectivity plays a fundamental role in determining how neuron networks
generate, process, and transfer information within and between brain regions. The underlying
mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model
networks are of great help. However, the implementation of these models relies on experimental
findings that are often sparse and limited. Their predicting power ultimately depends on how closely
a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic
rules, widely used to build neuronal network models, may not be appropriate to represent the
dynamics of the corresponding biological system. To solve this problem, we propose to use a
new mathematical framework able to use sparse and limited experimental data to quantitatively
reproduce the structural connectivity of biological brain networks at cellular level.
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Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system.
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Most animal species operate according to a 24-hour period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCN modulates hippocampal-dependent memory, but the molecular and cellular mechanisms that account for this effect remain largely unknown. In McCauley et al. 2020 [1], we identify cell-type specific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors. Astrocytes change their proximity clustered excitatory synaptic inputs, ultimately shaping hippocampal-dependent learning in vivo. We identify to synapses. Together, these phenomena alter glutamate clearance, receptor activation and integration of temporally corticosterone as a key contributor to changes in synaptic strength. These findings highlight important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus and alter the temporal dynamics of cognitive processing.
[1] "Circadian modulation of neurons and astrocytes controls synaptic plasticity in hippocampal area CA1" by J.P. McCauley, M.A. Petroccione, L.Y. D’Brant, G.C. Todd, N. Affinnih, J.J. Wisnoski, S. Zahid, S. Shree, A.A. Sousa, R.M. De Guzman, R. Migliore, A. Brazhe, R.D. Leapman, A. Khmaladze, A. Semyanov, D.G. Zuloaga, M. Migliore and A. Scimemi.
Cell Reports (2020), https://doi.org/10.1016/j.celrep.2020.108255
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"The amyloid precursor protein (APP) is central to AD pathogenesis and we recently showed that its intracellular domain (AICD) could modify synaptic signal integration. We now hypothezise that AICD modifies neuron firing activity, thus contributing to the disruption of memory processes. Using cellular, electrophysiological and behavioural techniques, we showed that pathological AICD levels weakens CA1 neuron firing activity through a gene transcription-dependent mechanism. Furthermore, increased AICD production in hippocampal neurons modifies oscillatory activity, specifically in the gamma frequency range, and disrupts spatial memory task. Collectively, our data suggest that AICD pathological levels, observed in AD mouse models and in human patients, might contribute to progressive neuron homeostatic failure, driving the shift from normal ageing to AD."
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The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of pathological conditions. Limited experimental and modeling evidence suggests this might be implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1 pyramidal cells and interneurons.
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Storing memory traces in the brain is essential for learning and memory formation. Memory traces are created by joint electrical activity in neurons that are interconnected by synapses and allow transferring electrical activity from a sending (presynaptic) to a receiving (postsynaptic) neuron. During learning, neurons that are co-active can tune synapses to become more effective. This process is called synaptic plasticity or long-term potentiation (LTP). Timing-dependent LTP (t-LTP) is a physiologically relevant type of synaptic plasticity that results from repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. T-LTP is observed during learning in vivo and is a cellular correlate of memory formation. T-LTP can be elicited by different rhythms of synaptic activity that recruit distinct synaptic growth processes underlying t-LTP. The protein brain-derived neurotrophic factor (BDNF) is released at synapses and mediates synaptic growth in response to specific rhythms of t-LTP stimulation, while other rhythms mediate BDNF-independent t-LTP.
Here, we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP (pairing of 1 presynaptic with 1 postsynaptic AP) and BDNF-dependent 1:4 t-LTP (pairing of 1 presynaptic with 4 postsynaptic APs). The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover.
Since BDNF levels are decreased in demented patients, understanding the function of BDNF in memory processes is of utmost importance to counteract Alzheimer’s disease.
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"[...] experimental findings suggest that
MC and mTC may encode parallel and complementary odor representations. We
have analyzed the functional roles of these pathways by using a morphologically
and physiologically realistic three-dimensional model to explore the MC and
mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
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"The cellular processes underlying individual differences in the Woring Memory Capacity (WMC) of humans are essentially unknown. Psychological experiments suggest that subjects with lower working memory capacity (LWMC), with respect to subjects with higher capacity (HWMC), take more time to recall items from a list because they search through a larger set of items and are much more susceptible to interference during retrieval. ... In this paper, we investigate the possible underlying mechanisms at the single neuron level by using a computational model of hippocampal CA1 pyramidal neurons, which have been suggested to be deeply involved in the recognition of specific items. ..."
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"Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor
cortex layer 5B that project caudally via the medullary pyramids,
display distinct class-specific electrophysiological properties in
vitro: strong sag with hyperpolarization, lack of adaptation, and a
nearly linear frequency-current (FI) relationship. We used our
electrophysiological data to produce a pair of large archives of SPI
neuron computer models in two model classes: 1. Detailed models with
full reconstruction; 2. Simplified models with 6 compartments. We
used a PRAXIS and an evolutionary multiobjective optimization (EMO) in
sequence to determine ion channel conductances.
..."
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The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
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" ... Here we present a biophysical model of a DA VTA neuron based on 3D morphological reconstruction and electrophysiological data, showing how opiates withdrawal-driven morphological and electrophysiological changes could affect the firing rate and discharge pattern...."
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Model files from the paper "Early-Onset Epileptic Encephalopathy Caused by
Gain-of-Function Mutations in the Voltage Sensor of Kv7.2 and Kv7.3
Potassium Channel Subunits" by Francesco Miceli,
Maria Virginia Soldovieri, Paolo Ambrosino, Michela De Maria,
Michele Migliore, Rosanna Migliore, and Maurizio Taglialatela
J Neurosci. 2015 Mar 4;35(9):3782-93.
The file fig7C.hoc reproduces the simulations shown in Fig.7C of the paper.
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"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
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NEURON mod files from the paper: M. Migliore, et al. (2015).
In this paper, we investigate the possibility that the experimental protocols on synaptic plasticity may result in different consequences (e.g., LTD instead of LTP), according to the initial conditions of the stimulated synapses, and can generate confusing results. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we study how, why, and to what extent EPSPs observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation of why LTD induction is experimentally difficult, and they offer experimentally testable predictions on the stimulation protocols that may be more effective.
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The paper discusses the effects induced by an electric field at power lines frequency.
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This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper:
"Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
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Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of
memory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and
LTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether
hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach
to treat AD.
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" ... Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart.
Using a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area.
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In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments."
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Model files for the entry "Olfactory Computations in Mitral-Granule Cell Circuits" of the Springer Encyclopedia of Computational Neuroscience by Michele Migliore and Tom Mctavish.
The simulations illustrate two typical Mitral-Granule cell circuits in the olfactory bulb of vertebrates: distance-independent lateral inhibition and gating effects.
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Building on our previous work (Marasco et al., (2012)), we present a general reduction method based on Strahler's analysis of neuron
morphologies. We show that, without any fitting or tuning procedures, it is
possible to map any morphologically and biophysically accurate neuron model
into an equivalent reduced version. Using this method for Purkinje cells, we
demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated
synaptic inputs.
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" ... the investigation of AP backpropagation and its functional roles has greatly benefitted from computational models that use biophysically and morphologically accurate implementations. ..." This model entry recreates figures 2 and 4 from the paper illustrating how conductance densities of voltage gated channels (fig 2) and the timing of synaptic input with backpropagating action potentials (fig 4) affects membrane voltage trajectories.
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NEURON mod files from the paper:
Miceli et al, Genotype–phenotype correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits, PNAS 2013 Feb 25. [Epub ahead of print]
In this paper, functional studies revealed that in homomeric or heteromeric configuration with KV7.2 and/or KV7.3 subunits, R213W and
R213Q mutations markedly destabilized the open state, causing a dramatic decrease in channel voltage sensitivity.
Modeling these channels in CA1 hippocampal pyramidal cells revealed that both mutations increased cell firing frequency,
with the R213Q mutation prompting more dramatic functional changes compared with the R213W mutation.
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The role of amyloid beta (Aß) in brain function and in the pathogenesis of Alzheimer’s disease remains elusive.
Recent publications reported that an increase in Aß concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased Aß can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer’s disease.
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" ... Here, we first quantified the morphological variability of branch points from two-photon images of rat CA1 pyramidal neurons. We then investigated the geometrical features affecting spike initiation, propagation, and timing with a computational model validated by glutamate uncaging experiments. The results suggest that even subtle membrane readjustments at branch point could drastically alter the ability of synaptic input to generate, propagate, and time action potentials."
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"... Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to >40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions."
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The model predicts possible therapeutic treatments of Alzheimers's Disease in terms of pharmacological manipulations of channels' kinetic and activation properties. The results suggest how and which mechanism can be targeted by a drug to restore the original firing conditions. The simulations reproduce somatic membrane potential in control conditions, when 90% of membrane is affected by AD (Fig.4A of the paper), and after treatment (Fig.4B of the paper).
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The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
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NEURON files from the paper:
Migliore M, Migliore R (2012) Know Your Current Ih: Interaction with a Shunting Current Explains the Puzzling Effects of Its Pharmacological or
Pathological Modulations. PLoS ONE 7(5): e36867.
doi:10.1371/journal.pone.0036867.
Experimental findings on the effects of Ih current modulation, which is particularly involved in epilepsy, appear to be inconsistent. In the paper, using a realistic model we show how and why a shunting current, such as that carried by TASK-like channels, dependent on the Ih peak conductance is able to explain virtually all experimental findings on Ih up- or down-regulation by modulators or pathological conditions.
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NEURON mod files from the paper:
Shah et al., 2011.
In this study, using a combination of electrophysiology
and computational modelling, we show that these channels selectively influence peri-somatic but not dendritic post-synaptic excitatory synaptic potential (EPSP) integration in CA1 pyramidal cells. This may be important for their relative contributions to physiological processes such as synaptic plasticity as well as patho-physiological conditions such as epilepsy.
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This model considers synchrony between mitral cells induced via shared
granule cell interneurons while taking into account the spatial
constraints of the system. In particular, since inhibitory inputs
decay passively along the lateral dendrites, this model demonstrates
that an optimal arrangement of the inhibitory synapses will be near
the cell bodies of the relevant mitral cells.
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NEURON files from the paper: On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons
by D.Bianchi, A. Marasco, A.Limongiello, C.Marchetti, H.Marie,B.Tirozzi, M.Migliore (2012). J Comput. Neurosci. In press. DOI: 10.1007/s10827-012-0383-y.
Experimental findings shown that under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block.
We analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment.
The results show the specic ion channel properties and kinetics that are needed to
reproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to depolarization block.
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This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).
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NEURON files from the paper: A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. by M. Migliore, I. De Blasi, D. Tegolo, R. Migliore, Neural Networks,(2011), doi:10.1016/j.neunet.2011.01.001. Starting from the experimentally supported assumption on hippocampal neurons we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a contextdependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for features recognition, leading to a distorted perception of objects.
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The model was used to reproduce experimentally determined mean synaptic response characteristics of unitary AMPA and NMDA synaptic stimulations in CA3 pyramidal cells with the objective of inferring the most likely response properties of the corresponding types of synapses. The model is primarily concerned with passive cells, but models of active dendrites are included.
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Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb.
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In this review some of the recent work carried out in our laboratory concerning the functional
role of GABAergic signalling at immature mossy fibres (MF)-CA3 principal cell synapses has
been highlighted. To compare the relative strength of CA3 pyramidal cell
output in relation to their MF glutamatergic or GABAergic inputs in postnatal
development, a realistic model was constructed taking into account the different
biophysical properties of these synapses.
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The model demonstrates that CA1 pyramidal neurons support rebound spikes mediated by hyperpolarization-activated inward current (Ih), and normally masked by A-type potassium channels (KA). Partial KA reduction confined to one or few branches of the apical tuft may be sufficient to elicit a local spike following a train of synaptic inhibition. These data suggest that the plastic regulation of KA can provide a dynamic switch to unmask post-inhibitory spiking in CA1 pyramidal neurons, further increasing the signal processing power of the CA1 synaptic microcircuitry.
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Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the “addicted brain”.
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In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems.
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In this paper, the model was used to show how neuroactive drugs targeting different neuronal mechanisms affect the signal integration in CA1 pyramidal neuron. Ferrante M, Blackwell KT, Migliore M, Ascoli GA (2008)
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NEURON mod files from the paper:
Miceli et al, Neutralization of a unique, negatively-charged residue in the voltage sensor
of K(V)7.2 subunits in a sporadic case of benign familial neonatal seizures, Neurobiol Dis., in press (2009).
In this paper, the model revealed that the gating changes introduced by a mutation in K(v)7.2
genes encoding for the neuronal KM current in a case of benign familial neonatal seizures,
increased cell firing frequency, thereby triggering the neuronal hyperexcitability which underlies the observed neonatal epileptic condition.
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In this paper, the model was used to show how the temporal summation of excitatory inputs in CA3 pyramidal neurons was affected by the presence of Ih in the dendrites in a frequency- and distance-dependent fashion.
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This model was used to confirm and support experimental data
suggesting that the neuronal/circuitry changes associated with temporal lobe epilepsy,
including Ih-dependent inductive mechanisms, can disrupt hippocampal theta function.
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In the olfactory bulb, the processing units for odor discrimination are believed
to involve dendrodendritic synaptic interactions between mitral and granule cells.
There is increasing anatomical evidence that these cells are organized in columns,
and that the columns processing a given odor are arranged in widely distributed arrays.
Experimental evidence is lacking on the underlying learning mechanisms for how these
columns and arrays are formed.
We have used a simplified realistic circuit model to test the hypothesis that
distributed connectivity can self-organize through an activity-dependent dendrodendritic
synaptic mechanism.
The results point to action potentials propagating in the mitral cell lateral dendrites
as playing a critical role in this mechanism, and suggest a novel and robust learning
mechanism for the development of distributed processing units in a cortical structure.
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The model used in this paper confirmed the experimental findings suggesting that axonal Kv7 channels are critically and uniquely required for determining the inherent spontaneous firing of
hippocampal CA1 pyramids, independently of alterations in synaptic activity.
The model predicts that the axonal Kv7 density could be 3-5 times that at the soma.
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We use a realistic computational model of dopaminergic neurons in vivo to suggest
that ethanol, through its effects on Ih, modifies the temporal structure of the spiking
activity. The model predicts that the dopamine level may increase much more during bursting
than pacemaking activity, especially in those brain regions with a slow dopamine clearance rate.
The results suggest that a selective pharmacological remedy could thus be devised against the
rewarding effects of ethanol that are postulated to mediate alcohol abuse and addiction,
targeting the specific HCN genes expressed in dopaminergic neurons.
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NEURON mod files from the paper:
Cassarà AM, Hagberg GE, Bianciardi M, Migliore M, Maraviglia B.
Realistic simulations of neuronal activity: A contribution to the debate on direct detection of neuronal currents by MRI.
Neuroimage. 39:87-106 (2008).
In this paper, we use a detailed calculation of the magnetic field produced by the neuronal
currents propagating over a hippocampal CA1 pyramidal neuron placed inside a cubic MR voxel of
length 1.2 mm to estimate the Magnetic Resonance signal.
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In the paper, this model was used to identify how relative differences in K+ conductances,
specifically KC, KM, & KD, between cells contribute to the different characteristics of the
three types of firing patterns observed experimentally.
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This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells.
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The model supports the experimental findings showing that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading to a wide range of interneuronal communication within the hippocampal network.
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NEURON files from the paper:
Single neuron binding properties and the magical number 7,
by M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008).
In an extensive series of simulations with realistic morphologies and active properties,
we demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs
to generate an output signal.
The results suggest a possible neural code as the most effective n-ple of dendrites that
can be used for short-term memory recollection of persons, objects, or places.
Our analysis predicts a straightforward physiological explanation for the observed
puzzling limit of about 7 short-term memory items that can be stored by humans.
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The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.
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The NEURON simulation environment has been extended to support parallel network simulations.
The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters.
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NEURON model files from the papers:
Shimazaki et al, Chem. Senses, epub ahead of print (2006)
Electrophysiological properties and modeling of murine vomeronasal
sensory neurons in acute slice preparations.
The model reproduces quantitatively the experimentally observed
firing rates of these neurons under a wide range of input currents.
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NEURON mod files from the paper:
M. Migliore, M. Ferrante, GA Ascoli (2005).
The model shows how the back- and forward propagation of action potentials in the oblique dendrites of CA1 neurons could be modulated by local properties such as morphology or active conductances.
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The model supports the experimental findings on the effects of postnatal odor deprivation, and shows that a -10mV shift in the
Na activation or a reduction in the dendritic length of newborn GC
could independently explain the observed increase in excitability.
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NEURON mod files from the paper:
Sonia Gasparini, Michele Migliore, and Jeffrey C. Magee
On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons,
J. Neurosci., J. Neurosci. 24:11046-11056 (2004).
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In a realistic model of two electrically connected mitral cells,
the paper shows that the somatically-measured experimental properties
of Gap Junctions (GJs) may correspond to a variety of different local coupling strengths
and dendritic distributions of GJs in the tuft. The model suggests
that the propagation of the GJ-induced local tuft depolarization
is a major mechanim for intraglomerular synchronization of mitral cells.
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Protracted presynaptic activity can induce long-term potentiation
(LTP) or long-term depression (LTD) of the synaptic strength. However,
virtually all the experiments testing how LTP and LTD depend on the
conditioning input are carried out with trains of stimuli at constant
frequencies, whereas neurons in vivo most likely experience a stochastic
variation of interstimulus intervals. We used a computational model of
synaptic transmission to test if and to what extent the stochastic
fluctuations of an input signal could alter the probability to change the
state of a synapse. See paper for conclusions.
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NEURON mod files from the paper:
M. Migliore, L. Messineo, M. Ferrante
Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons, J.Comput. Neurosci. 16:5-13 (2004).
The model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents
when they are asynchronously and distally activated.
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The model shows how using a CA1-like distribution of active dendritic conductances in a CA3 morphology results in dendritic initiation of spikes during a burst.
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The model shows how the experimentally observed increase in the dendritic density of Ih and IA could have a major role in constraining the temporal integration window for the main CA1
synaptic inputs.
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NEURON mod files from N. Poolos, M. Migliore, and D. Johnston, Nature Neuroscience (2002).
The experimental and modeling results in this paper demonstrate for the first time that neuronal excitability can be altered by pharmaceuticals acting selectively on dendrites, and suggest an important role for Ih in controlling dendritic excitability and epileptogenesis.
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Model simulation file from the paper
M.Migliore and Gordon M. Shepherd
Emerging rules for distributions of active dendritic properties underlying specific neuronal functions. Nature Rev. Neurosci. 3, 362-370 (2002).
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Model files from the paper Watanabe S, Hoffman DA, Migliore M,
Johnston D (2002). The experimental and modeling results support the
hypothesis that
dendritic K-A channels and the boosting of back-propagating action
potentials
contribute to the induction of LTP in CA1 neurons.
See the paper for details.
Questions about the model may be addressed to Michele Migliore:
michele.migliore@cnr.it
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Model files from the paper:
M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer
simulations of morphologically reconstructed CA3 hippocampal neurons, J.
Neurophysiol. 73, 1157-1168 (1995).
Demonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium.
Contact: michele.migliore@cnr.it if you have any questions about the implementation of the model.
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NEURON model files from the paper
M. Migliore, L. Messineo, M. Cardaci, G.F. Ayala,
Quantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001).
Contact: michele.migliore@cnr.it if you have any questions about the implementation of the model.
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Model files from the paper:
M. Migliore, Modeling the attenuation and failure of action potentials in
the dendrites of hippocampal neurons, Biophys. J. 71:2394-403 (1996). .
Contact: michele.migliore@cnr.it
if you have any questions about the implementation of the model.
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Hippocampal CA1 pyramidal neuron model from the paper
M.Migliore, D.A Hoffman, J.C. Magee and D. Johnston (1999) Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons,
J. Comput. Neurosci. 7, 5-15.
Contact: michele.migliore@cnr.it if you have any questions about the implementation of the model.
