Publications (3)2.64 Total impact
Article: Anatomically-constrained effective connectivity among layers in a cortical column modeled and estimated from local field potentials.[show abstract] [hide abstract]
ABSTRACT: We propose a neural mass model for anatomically-constrained effective connectivity among neuronal populations residing in four layers (L2/3, L4, L5 and L6) within a cortical column. Eight neuronal populations in a given column--an excitatory population and an inhibitory population per layer--are assumed to be coupled via effective connections of unknown strengths that need to be estimated. The effective connections are constrained to anatomical connections that have been shown to exist in previous anatomical studies. The neural input to a cortical column is directed into the two populations in L4. The anatomically-constrained effective connectivity is captured by a system of 16 stochastic differential equations. Solving these equations yields the average postsynaptic potentials and transmembrane currents generated in each population. The current source density (CSD) responses in each layer, which serve as the model observations, are equated in the model to the sum of all currents generated within that layer. The model is implemented in a continuous-discrete state-space framework, and the innovation method is used for estimating the model parameters from CSD data. To this end, local field potential (LFP) responses to forepaw stimulation were recorded in rat area S1 using multi-channel linear probes. LFPs were converted to CSD signals, which were averaged within each layer, yielding one CSD response per layer. To estimate the effective strengths of connections between all cortical layers, the model was fitted to these CSD signals. The results show that the pattern of effective interactions is strongly influenced by the pattern of strengths of the anatomical connections; however, these two patterns are not identical. The estimated anatomically-constrained effective connectivity matrix and the anatomical connectivity matrix shared five of their six strongest connections, although rankings according to connection strength differed. The strongest effective connections were from excitatory neurons in layer 4 to excitatory neurons in layer 2/3. Our study shows the feasibility of estimating anatomically-constrained effective connectivity within a cortical column, and indicates that there is a strong influence of anatomical connectivity on effective connectivity between cortical layers.Journal of Integrative Neuroscience 12/2010; 9(4):355-79. · 0.76 Impact Factor
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ABSTRACT: Our goal is to model the behavior of an ensemble of interacting neurons and astrocytes (the neural-glial mass). For this, a model describing N tripartite synapses is proposed. Each tripartite synapse consists of presynaptic and postsynaptic nerve terminals, as well as the synaptically associated astrocytic microdomain, and is described by a system of 13 stochastic differential equations. Then, by applying the dynamical mean field approximation (DMA) (Hasegawa, 2003a , 2003b ) the system of 13N equations is reduced to 13(13 + 2) = 195 deterministic differential equations for the means and the second-order moments of local and global variables. Simulations are carried out for studying the response of the neural-glial mass to external inputs applied to either the presynaptic terminals or the astrocytes. Three cases were considered: the astrocytes influence only the presynaptic terminal, only the postsynaptic terminal, or both the presynaptic and postsynaptic terminals. As a result, a wide range of responses varying from singles spikes to train of spikes was evoked on presynaptic and postsynaptic terminals. The experimentally observed phenomenon of spontaneous activity in astrocytes was replicated on the neural-glial mass. The model predicts that astrocytes can have a strong and activity-dependent influence on synaptic transmission. Finally, simulations show that the dynamics of astrocytes influences the synchronization ratio between neurons, predicting a peak in the synchronization for specific values of the astrocytes' parameters.Neural Computation 12/2009; 22(4):969-97. · 1.88 Impact Factor
Article: Dynamical Mean Field approximation of a canonical cortical model for studying inter-population synchrony[show abstract] [hide abstract]
ABSTRACT: The goal of this paper is twofold. We propose and explore a model to study the synchronization among populations in the canonical model of the neocortex proposed previously by (R.J. Douglas, K.A.C. Martin, A functional microcircuit for cat visual cortex. J.Physiol. 440(1991) 735–769). For this, a model describing N synapses of each m-population (m = 1, 2,3) is proposed. Each synapse is described by a system of 2 stochastic differential equations (SDEs). Then, by using the dynamical mean field approximation (DMA) (H. Hasegawa, Dynamical mean-field theory of spiking neuron ensembles: Response to a single spike with independent noises, Phys. Rev. E. (2003)1-19.) the system of several SDEs is reduced to 12 ordinary differential equations for the means and the second-order moments of global variables. The connectivity among populations is obtained by summarizing in the canonical model the detailed information from a quantitative description of the circuits formed in cat area 17 given in (T.Binzegger, R.J. Douglas, K.A. Martin, A Quantitative Map of the Circuit of Cat Primary Visual Cortex, J. Neurosci. 24 (2004) 8441- 8453). In the framework of the used DMA we propose a measure for inter-population synchronization. Simulations are carried out for exploring how inter-population synchrony is related to the variation of firing frequency of each population. Our results suggest that superficial pyramidal clusters appear to have a predominant influence on the synchronization process among pyramidal populations as well as put forward the active role of inhibition in the rest of the synchronizations between populations.Nature Precedings.