Energy efficiency of information transmission by electrically coupled neurons

The generation of spikes by neurons is energetically a costly process. This paper studies the consumption of energy and the information entropy in the signalling activity of a model neuron both when it is supposed isolated and when it is coupled to another neuron by an electrical synapse. The neuron has been modelled by a four dimensional Hindmarsh-Rose type kinetic model for which an energy function has been deduced. For the isolated neuron values of energy consumption and information entropy at different signalling regimes have been computed. For two neurons coupled by a gap junction we have analyzed the roles of the membrane and synapse in the contribution of the energy that is required for their organized signalling. Computational results are provided for cases of identical and nonidentical neurons coupled by unidirectional and bidirectional gap junctions. One relevant result is that there are values of the coupling strength at which the organized signalling of two neurons induced by the gap junction takes place at relatively low values of energy consumption and the ratio of mutual information to energy consumption is relatively high. Therefore, communicating at these coupling values could be energetically the most efficient option

Self-Organized Criticality model for Brain Plasticity

Networks of living neurons exhibit an avalanche mode of activity, experimentally found in organotypic cultures. Here we present a model based on self-organized criticality and taking into account brain plasticity, which is able to reproduce the spectrum of electroencephalograms (EEG). The model consists in an electrical network with threshold firing and activity-dependent synapse strenghts. The system exhibits an avalanche activity power law distributed. The analysis of the power spectra of the electrical signal reproduces very robustly the power law behaviour with the exponent 0.8, experimentally measured in EEG spectra. The same value of the exponent is found on small-world lattices and for leaky neurons, indicating that universality holds for a wide class of brain models.Comment: 4 pages, 3 figure

Mutations in shaking-B prevent electrical synapse formation in the Drosophila giant fiber system

The giant fiber system (GFS) is a simple network of neurons that mediates visually elicited escape behavior in Drosophila. The giant fiber (GF), the major component of the system, is a large, descending interneuron that relays visual stimuli to the motoneurons that innervate the tergotrochanteral jump muscle (TTM) and dorsal longitudinal flight muscles (DLMs). Mutations in the neural transcript from the shaking-B locus abolish the behavioral response by disrupting transmission at some electrical synapses in the GFS. This study focuses on the role of the gene in the development of the synaptic connections. Using an enhancer-trap line that expresses lacZ in the GFs, we show that the neurons develop during the first 30 hr of metamorphosis. Within the next 15 hr, they begin to form electrical synapses, as indicated by the transfer of intracellularly injected Lucifer yellow. The GFs dye-couple to the TTM motoneuron between 30 and 45 hr of metamorphosis, to the peripherally synapsing interneuron that drives the DLM motoneurons at approximately 48 hr, and to giant commissural interneurons in the brain at approximately 55 hr. Immunocytochemistry with shaking-B peptide antisera demonstrates that the expression of shaking-B protein in the region of GFS synapses coincides temporally with the onset of synaptogenesis; expression persists thereafter. The mutation shak-B2, which eliminates protein expression, prevents the establishment of dye coupling shaking-B, therefore, is essential for the assembly and/or maintenance of functional gap junctions at electrical synapses in the GFS

Beta-rhythm oscillations and synchronization transition in network models of Izhikevich neurons: effect of topology and synaptic type

Despite their significant functional roles, beta-band oscillations are least understood. Synchronization in neuronal networks have attracted much attention in recent years with the main focus on transition type. Whether one obtains explosive transition or a continuous transition is an important feature of the neuronal network which can depend on network structure as well as synaptic types. In this study we consider the effect of synaptic interaction (electrical and chemical) as well as structural connectivity on synchronization transition in network models of Izhikevich neurons which spike regularly with beta rhythms. We find a wide range of behavior including continuous transition, explosive transition, as well as lack of global order. The stronger electrical synapses are more conducive to synchronization and can even lead to explosive synchronization. The key network element which determines the order of transition is found to be the clustering coefficient and not the small world effect, or the existence of hubs in a network. These results are in contrast to previous results which use phase oscillator models such as the Kuramoto model. Furthermore, we show that the patterns of synchronization changes when one goes to the gamma band. We attribute such a change to the change in the refractory period of Izhikevich neurons which changes significantly with frequency.Comment: 7 figures, 1 tabl