Balazs Ujfalussy

Impaired associative learning in schizophrenia: behavioral and computational studies

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.

Robust path integration in the entorhinal grid cell system with hippocampal feed-back

Animals are able to update their knowledge about their current position solely by integrating the speed and the direction of their movement, which is known as path integration. Recent discoveries suggest that grid cells in the medial entorhinal cortex might perform some of the essential underlying computations of path integration. However, a major concern over path integration is that as the measurement of speed and direction is inaccurate, the representation of the position will become increasingly unreliable. In this paper, we study how allothetic inputs can be used to continually correct the accumulating error in the path integrator system. We set up the model of a mobile agent equipped with the entorhinal representation of idiothetic (grid cell) and allothetic (visual cells) information and simulated its place learning in a virtual environment. Due to competitive learning, a robust hippocampal place code emerges rapidly in the model. At the same time, the hippocampo-entorhinal feed-back connections are modified via Hebbian learning in order to allow hippocampal place cells to influence the attractor dynamics in the entorhinal cortex. We show that the continuous feed-back from the integrated hippocampal place representation is able to stabilize the grid cell code.

Constrained density functional theory for first principles spin dynamics

Constrained density functional theory is used to formulate a theory of general noncollinear spin systems which makes it possible to implement first principles spin dynamics in a manner that is firmly grounded in density functional theory. At each time step, local constraining fields are calculated from a self-consistent algorithm. In addition to discussing the conceptual basis of the resulting constrained local moment model we illustrate the theory by explicit calculations for the relative rotation of the corner and body center moments of bcc iron.

Higher-order contributions to the Rashba-Bychkov effect with application to the Bi/Ag(111) surface alloy

In order to explain the anisotropic Rashba-Bychkov effect observed in several metallic-surface-state systems, we use k⋅p perturbation theory with a simple group-theoretical analysis and construct effective Rashba Hamiltonians for different point groups up to third order in the wave number. We perform relativistic ab initio calculations for the (3√×3√)R30∘ Bi/Ag(111) surface alloy, and from the calculated splitting of the band dispersion we find evidence of the predicted third-order terms. Furthermore, we derive expressions for the corresponding third-order Rashba parameters to provide a simple explanation of the qualitative difference concerning the Rashba-Bychkov splitting of the surface states at Au(111) and Bi/Ag(111).

Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells.

On the magnetic structure of γ-FeMn alloys

The alloy γ-FeMn is a rare example of a fcc antiferromagnet. It has become a prototype for pinning layer studies in magnetoelectronic devices. Here we report the results of first principles calculations of the magnetic structure of γ-FeMn based on large cell models of the disordered alloy. The calculations are based on the constrained local moment model and use of first principles spin dynamics to obtain the ground state orientational configuration. In agreement with previous layer KKR-CPA studies, we find the 3Q-state to be lowest of the three prototype structures studied (1Q,2Q,3Q). However, the constraining fields introduced into the theory to maintain a specific orientational configuration are not zero indicating that even the 3Q-structure is not the ground state. Subsequent optimization of the magnetic configuration using first principles spin dynamics yields a state that is lower in energy by 2.5 meV/atom.

Location-dependent synaptic plasticity rules by dendritic spine cooperativity

Nonlinear interactions between coactive synapses enable neurons to discriminate between spatiotemporal patterns of inputs. Using patterned postsynaptic stimulation by two-photon glutamate uncaging, here we investigate the sensitivity of synaptic Ca2+ signalling and long-term plasticity in individual spines to coincident activity of nearby synapses. We find a proximodistally increasing gradient of nonlinear NMDA receptor (NMDAR)-mediated amplification of spine Ca2+ signals by a few neighbouring coactive synapses along individual perisomatic dendrites. This synaptic cooperativity does not require dendritic spikes, but is correlated with dendritic Na+ spike propagation strength. Furthermore, we show that repetitive synchronous subthreshold activation of small spine clusters produces input specific, NMDAR-dependent cooperative long-term potentiation at distal but not proximal dendritic locations. The sensitive synaptic cooperativity at distal dendritic compartments shown here may promote the formation of functional synaptic clusters, which in turn can facilitate active dendritic processing and storage of information encoded in spatiotemporal synaptic activity patterns.

How do glutamatergic and GABAergic cells contribute to synchronization in the medial septum

The medial septum-diagonal band (MSDB) complex is considered as a pacemaker for the hippocampal theta rhythm. Identification of the different cell types, their electro-physiological properties and their possible function in the generation of a synchronized activity in the MSDB is a hot topic. A recent electro-physiological study showed the presence of two antiphasically firing populations of parvalbumin containing GABAergic neurons in the MSDB. Other papers described a network of cluster-firing glutamatergic neurons, which is able to generate synchronized activity in the MSDB. We propose two different computer models for the generation of synchronized population theta oscillation in the MSDB and compare their properties. In the first model GABAergic neurons are intrinsically theta periodic cluster-firing cells; while in the second model GABAergic cells are fast-firing cells and receive periodic input from local glutamatergic neurons simulated as cluster-firing cells. Using computer simulations we show that the GABAergic neurons in both models are capable of generating antiphasic theta periodic population oscillation relying on local, septal mechanisms. In the first model antiphasic theta synchrony could emerge if GABAergic neurons form two populations preferentially innervate each other. In the second model in-phase synchronization of glutamatergic neurons does not require specific network structure, and the network of these cells are able to act as a theta pacemaker for the local fast-firing GABAergic circuit. Our simulations also suggest that neurons being non-cluster-firing in vitro might exhibit clustering properties when connected into a network in vivo.

Pharmacological and computational analysis of alpha-subunit preferential GABAA positive allosteric modulators on the rat septo-hippocampal activity

Clinically most active anxiolytic drugs are positive allosteric modulators (PAMs) of GABA(A) receptors, represented by benzodiazepine compounds. Due to their non-selective profile, however, they potently modulate several sup-type specific GABA(A) receptors, contributing to their broad-range side effects. Based on observations in genetically altered mice, however, it has been proposed that anxiolytic action of benzodiazepines is predominantly mediated by GABA(A) alpha2/3 subunit-containing receptors. In the present study we analyzed the actions of the preferential GABA(A) alpha1 and alpha2/3 PAMs, zolpidem and L-838417, respectively on hippocampal EEG and medial septum neuronal activity in anesthetized rats. In parallel, a computational model was constructed to model pharmacological actions of these compounds on the septo-hippocampal circuitry. The present results demonstrated that zolpidem inhibited theta oscillation both in the hippocampus and septum, and profoundly inhibited firing activity of septal neurons. L-838417 also inhibited hippocampal and septal theta oscillation, however, it did not significantly alter firing rate activity of septal neurons. Our computational model showed that cessation of periodic firing of hippocampo-septal neurons, representing absence of hippocampal theta activity, disrupted oscillation of septal units, without altering their overall firing activity, similar to changes observed in our in vivo experiments following administration of L-838417. Understanding the correlation between changes in septo-hippocampal activity and actions of selective modulators of GABA(A) subtype receptor modulators would further advance design of anxiolytic drugs.

Multi-level Models

The brain is a prototype of a hierarchical system, as Fig. 1 shows. More precisely, it is hierarchical dynamical system. To specify a dynamical system, characteristic state variables and evolution equations governing the change of state should be defined. At the molecular level, the dynamic laws can be identified with chemical kinetics, at the channel level with biophysically detailed equations for the membrane potential, and at the synaptic and network levels with learning rules to describe the dynamics of synaptic modifiability (see Table 1).