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 studies have investigated this issue at the network and cellular level. Here, starting from the experimentally supported assumption that hippocampal neurons are very specific, sparse, and invariant in their firing, we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a context-dependent 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 feature recognition, leading to a distorted perception of objects.
"Interestingly, the computational model proposed by Hoffman showed that this dysfunction was maximized if the retrieval cues were not similar to the stored memory and if the number of synapses on to each neuron was reduced, resulting in decreased system capacity. In agreement with these results was the computational evidence that alterations of the distal input of CA1 pyramidal neurons in the hippocampus could generate hallucinations by altering the normal recall of objects on which the neurons have been previously tuned (Migliore et al. 2011 ) . Finally, these models of excessive pruning constitute an interesting way to account for the emergence of auditory-verbal hallucinations during a speci fi c developmental window, such as adolescence, which is the peak incidence period for schizophrenia (Paus et al. 2008 ; Rolls and Deco 2011 ) . "
[Show abstract][Hide abstract] ABSTRACT: Recent advances in theoretical neuroscience have provided new insights into information processing within large brain-like networks operating in an uncertain world. The computational framework can overcome some of the complexity within the object of study by predicting how basic changes in neural architecture may lead to systems-level changes that translate into changes in behavior. Computational models offer ways to unify basic neurochemical ﬁndings with data from more macroscopic levels and to start to apply these ﬁndings to cognitive sciences and psychiatry. Some of these approaches have been used to investigate the underlying mechanisms of subjective experiences, such as hallucinations, which can spontaneously emerge into consciousness in the absence of any corresponding external stimuli. This chapter describes some recent theoretical studies on four categories of positive symptoms of schizophrenia: neurodynamics, noise, disconnectivity, and Bayesian models of hallucinations. Results from simulations of these neural networks as well as the potential alterations leading to aberrant experiences are presented and discussed.
The Neuroscience of Hallucinations, Edited by Renaud Jardri, Arnaud Cachia, Pierre Thomas, Delphine Pins, 01/2013: chapter Computational Models of Hallucinations: pages 289-313; Springer New-York.
"Chater & Oaksford, 2008; Huys, 2007; Maia & Frank, 2011; Williams & Dayan, 2005). Crucially, these approaches link psychopathology to normal psychology and to basic neuroscience, for instance investigating how changes in GABA or NMDA signalling, supported by work in genetics and animal models, may explain the perceptual features of schizophrenia (Loh, Rolls, & Deco, 2007; Migliore, Blasi, Tegolo, & Migliore, 2011). Computational models, more than any other approach, allow us to relate findings to general principles that tap the core of the brain's raison d'être, which is to compute and process information, rather than say produce a stream of internal experiences. "
[Show abstract][Hide abstract] ABSTRACT: Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to approach psychiatric problems; and (d) the issue of communicating clinical concepts to theoreticians and vice versa. We argue that CMs have yet to influence psychiatric practice, but that they help psychiatric research in two fundamental ways: (a) to build better theories integrating psychiatry with neuroscience; and (b) to enforce explicit, global and efficient testing of hypotheses through more powerful analytical methods. CMs allow the complexity of a hypothesis to be rigorously weighed against the complexity of the data. The paper concludes with a discussion of the path ahead. It points to stumbling blocks, like the poor communication between theoretical and medical communities. But it also identifies areas in which the contributions of CMs will likely be pivotal, like an understanding of social influences in psychiatry, and of the co-morbidity structure of psychiatric diseases.
Neural networks: the official journal of the International Neural Network Society 03/2011; 24(6):544-51. DOI:10.1016/j.neunet.2011.03.001 · 2.71 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The proximity effects of microstrip lines near a substrate edge
are estimated by using the rectangular boundary division method for
effectively designing high-packing-density MMICs (monolithic microwave
integrated circuits). Simple CAD (computer-aided design) formulas of
edge-compensated microstrip lines (ECM lines) are introduced which can
be applied to circumvent the proximity effects on the characteristic
impedance. The practical design parameters of the ECM lines are given in
the form of numerical data and simple polynomials for CAD work with a
curve-fitting procedure. Results of capacitance measurements are
compared with this theory
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.