A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior.

The neural mechanisms underlying schizophrenic behavior are unknown and very difficult to investigate experimentally, although a few experimental and modeling studies suggested possible causes for some of the typical psychotic symptoms related to this disease. The brain region most involved in these processes seems to be the hippocampus, because of its critical role in establishing memories for objects or events in the context in which they occur. In particular, a hypofunction of the N-methyl-D-aspartate (NMDA) component of the synaptic input on the distal dendrites of CA1 pyramidal neurons has been suggested to play an important role for the emergence of schizophrenic behavior. Modeling st…

A Molecular Dynamics Study of the Structure of an Aqueous KC1 Solution

A molecular dynamics simulation of a 2.2 molal aqueous KCl solution has been performed using the ST2 water model. The simulation extended over 5ps at an average temperature of 288 K. The basic box has a side length of 18.74 A and contained 200 water molecules, 8 cations and 8 anions. The structure of the solution is discussed by radial distribution functions, the orientation of the water molecules, and their geometrical arrangement in the first hydration shells. The first shells of K+ and Cl- extend up to 3.52 and 3.84 A, respectively, with the corresponding hydration numbers 7.8 and 7.6. The results are compared with recent neutron and X-ray diffraction data and with findings of previous M…

The role of network connectivity on epileptiform activity.

AbstractA 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 highligh…

A Kinetic Model of Short- and Long-Term Potentiation

We present a kinetic model that can account for several experimental findings on short- and long-term potentiation (STP and LTP) and their pharmacological modulation. The model, which is consistent with Hebb's postulate, uses the hypothesis that part of the origin of LTP may be a consequence of an increased release of neurotransmitter due to a retrograde signal. The operation of the model is expressed by a set of irreversible reactions, each of which should be thought of as equivalent to a set of more complex reactions. We show that a retrograde signal alone is not sufficient to maintain LTP unless long-term change of the rate constant of some of the reactions is caused by high-frequency s…

Progressive effect of beta amyloid peptides accumulation on CA1 pyramidal neurons: a model study suggesting possible treatments

Several independent studies show that accumulation of β-amyloid (Aβ) peptides, one of the characteristic hallmark of Alzheimer's Disease (AD), can affect normal neuronal activity in different ways. However, in spite of intense experimental work to explain the possible underlying mechanisms of action, a comprehensive and congruent understanding is still lacking. Part of the problem might be the opposite ways in which Aβ have been experimentally found to affect the normal activity of a neuron; for example, making a neuron more excitable (by reducing the A- or DR-type K(+) currents) or less excitable (by reducing synaptic transmission and Na(+) current). The overall picture is therefore confus…

Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry.

ABSTRACTIn 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, which allow the model to reach a level of efficiency and accuracy that, to the best of our…

Single neuron binding properties and the magical number 7

When we observe a scene, we can almost instantly recognize a familiar object or can quickly distinguish among objects differing by apparently minor details. Individual neurons in the medial temporal lobe of humans have been shown to be crucial for the recognition process, and they are selectively activated by different views of known individuals or objects. However, how single neurons could implement such a sparse and explicit code is unknown and almost impossible to investigate experimentally. Hippocampal CA1 pyramidal neurons could be instrumental in this process. Here, in an extensive series of simulations with realistic morphologies and active properties, we demonstrate how n radial (ob…

A model study for the progressive disruption of CA1 firing properties during Alzheimer’s disease

Several independent studies show that β-Amyloid (Aβ) peptides accumulation, one of the characteristic hallmark of Alzheimer’s Disease (AD), can affect the normal neuronal activity in different ways causing an increase or a decrease in neuronal membrane excitability. For example, experimental evidence for a negative impact on neuronal membrane in animal models of AD has been obtained in dual patch recordings in rat hippocampal tissue slices, in which Aβ blocked K channels in pyramidal cell dendrites, causing an increase in dendritic membrane excitability. The resulting increased Ca2+ influx and excitoxicity may lead to dendritic degeneration. However, further experimental evidence suggests t…

Visualization of Simulated Arrhythmias due to Gap Junctions

New computational models are able to simulate details of cardiac cell networks. Their results allow a better understanding of the functionality of the heart and suggest possible actions to reduce non-fatal premature beats that can give rise to serious diseases. We developed a user-friendly interface to organize Neuron simulations and to present in real-time a three-dimensional representation of the electrical activity due to the gap junctions which interconnect the cells inside cardiac tissues. All physiological parameters were set according to real experimental observations and compared against different types of arrhythmias, retrieved from the Physionet Data Base.

Sparse Distributed Representation of Odors in a Large-scale Olfactory Bulb Circuit

In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and g…

Spatial graphs and Convolutive Models

In the last two decades, many complex systems have benefited from the use of graph theory, and these approaches have shown robust applicability in the field of finance, computer circuits and in biological systems. Large scale models of brain systems make also a great use of random graph models. Graph theory can be instrumental in modeling the connectivity and spatial distribution of neurons, through a characterization of the relative topological properties. However, all approaches in studying brain function have been so far limited to use experimental constraints obtained at a macroscopic level (e.g. fMRI, EEG, MEG, DTI, DSI). In this contribution, we present a microscopic use (i.e. at the …

Solute-induced Water Structure: Computer Simulation on a Model System

Abstract Two series of Monte Carlo simulations have been carried out on a system consisting of 125 water molecules, one of which is kept fixed to simulate a water molecule whose mobility is restricted by a solute. The results are checked against similar simulations without restrictions, used as a control, and they show how the blocked molecule helps increase both the structural order and the connectivity of the hydrogen bond network. Cooperativity originating from proton polarizability of H-bonds and/or from many-body terms of interaction potentials cannot be involved since we use a rigid water model and ab initio pair potentials. The present findings are interpreted as indicative of a moti…

On the cellular mechanisms underlying working memory capacity in humans

The cellular processes underlying individual differences in the Working 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. However, a more precise link between psychological experiments and cellular properties is lacking and very difficult to investigate experimentally. In this paper, we investigate the possible underlying mechanisms at the single neuron level by using a computational mod…

A model for long-term potentiation and depression

A computational model of long-term potentiation (LTP) and long-term depression (LTD) in the hippocampus is presented. The model assumes the existence of retrograde signals, is in good agreement with several experimental data on LTP, LTD, and their pharmacological manipulations, and shows how a simple kinetic scheme can capture the essential characteristics of the processes involved in LTP and LTD. We propose that LTP and LTD could be two different but conceptually similar processes, induced by the same class of retrograde signals, and maintained by two distinct mechanisms. An interpretation of a number of experiments in terms of the molecular processes involved in LTP and LTD induction and …

An algorithm to find all paths between two nodes in a graph

Effects of low frequency electric fields on synaptic integration in hippocampal CA1 pyramidal neurons: implications for power line emissions

The possible cognitive effects of low frequency external electric fields, such as those generated by power lines, are poorly understood. Their functional consequences for mechanisms at the single neuron level are very difficult to study and identify experimentally, especially in vivo. The major open problem is that experimental investigations on humans have given inconsistent or contradictory results, making it difficult to estimate the possible effects of external low frequency electric fields on cognitive functions. Here we investigate this issue with a realistic model of hippocampal CA1 pyramidal neurons. The model suggests how and why external electric fields, with environmentally obser…

Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons.

The active dendritic conductances shape the input-output properties of many principal neurons in different brain regions, and the various ways in which they regulate neuronal excitability need to be investigated to better understand their functional consequences. Using a realistic model of a hippocampal CA1 pyramidal neuron, we show a major role for the hyperpolarization-activated current, I-h, in regulating the spike probability of a neuron when independent synaptic inputs are activated with different degrees of synchronization and at different distances from the soma. The results allowed us to make the experimentally testable prediction that the I-h in these neurons is needed to reduce ne…

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qu…

Biomolecular-solvent stereodynamic coupling probed by deuteration.

Thermodynamic interpretation of experiments with isotopically perturbed solvent supports the view that solvent stereodynamics is directly relevant to thermodynamic stability of biomolecules. According with the current understanding of the structure of the aqueous solvent, in any stereodynamic configuration of the latter, connectivity pathways are identifiable for their topologic and order properties. Perturbing the solvent by isotopic substitution or, e.g., by addition of co-solvents, can therefore be viewed as reinforcing or otherwise perturbing these topologic structures. This microscopic model readily visualizes thermodynamic interpretation. In conclusion, the topologic stereodynamic str…

Energy efficient modulation of dendritic processing functions

The voltage dependent ionic conductances and the passive properties of the neural membrane determine how external inputs are processed by the dendritic tree, and define the computational characteristics of neurons. However, what controls these characteristics and how they are implemented at the single neuron level, in such a way that an external input results in the coding of the appropriate output, is essentially unknown. We show here that a slow inactivation of the Na+ channel, involved in the attenuation and/or failure of APs in the dendrites, acts as an active and energy efficient filter of synaptic input, and results in an activity-dependent control of the properties of individual neur…

On the structural connectivity of large-scale models of brain networks at cellular level

AbstractThe 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 …