Leonardo Lyra Gollo me incentivou a retomar o blog. Obrigado pelo incentivo, Leo!
Single-Neuron Criticality Optimizes Analog Dendritic Computation
Neurons are thought of as the building blocks of excitable brain tissue. However, at the single neuron level, the neuronal membrane, the dendritic arbor and the axonal projections can also be considered an extended active medium. Active dendritic branchlets enable the propagation of dendritic spikes, whose computational functions, despite several proposals, remain an open question. Here we propose a concrete function to the active channels in large dendritic trees. By using a probabilistic cellular automaton approach, we model the input-output response of large active dendritic arbors subjected to complex spatio-temporal inputs, and exhibiting non-stereotyped dendritic spikes. We find that, if dendritic spikes have a non-deterministic duration, the dendritic arbor can undergo a continuous phase transition from a quiescent to an active state, thereby exhibiting spontaneous and self-sustained localized activity as suggested by experiments. Analogously to the critical brain hypothesis, which states that neuronal networks self-organize near a phase transition to take advantage of specific properties of the critical state, here we propose that neurons with large dendritic arbors optimize their capacity to distinguish incoming stimuli at the critical state. We suggest that “computation at the edge of a phase transition” is more compatible with the view that dendritic arbors perform an analog and dynamical rather than a symbolic and digital dendritic computation.
| Comments: | 11 pages, 6 figures |
| Subjects: | Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:1304.4676 [q-bio.NC] |
| (or arXiv:1304.4676v1 [q-bio.NC] for this version) |
Mechanisms of Zero-Lag Synchronization in Cortical Motifs
Zero-lag synchronization between distant cortical areas has been observed in a diversity of experimental data sets and between many different regions of the brain. Several computational mechanisms have been proposed to account for such isochronous synchronization in the presence of long conduction delays: Of these, the phenomena of “dynamical relaying” – a mechanism that relies on a specific network motif (M9) – has proven to be the most robust with respect to parameter and system noise. Surprisingly, despite a contrary belief in the community, the common driving motif (M3) is an unreliable means of establishing zero-lag synchrony. Although dynamical relaying has been validated in empirical and computational studies, the deeper dynamical mechanisms and comparison to dynamics on other motifs is lacking. By systematically comparing synchronization on a variety of small motifs, we establish that the presence of a single reciprocally connected pair – a “resonance pair” – plays a crucial role in disambiguating those motifs that foster zero-lag synchrony in the presence of conduction delays (such as dynamical relaying, M9) from those that do not (such as the common driving triad, M3). Remarkably, minor structural changes to M3 that incorporate a reciprocal pair (hence M6, M9, M3+1) recover robust zero-lag synchrony. The findings are observed in computational models of spiking neurons, populations of spiking neurons and neural mass models, and arise whether the oscillatory systems are periodic, chaotic, noise-free or driven by stochastic inputs. The influence of the resonance pair is also robust to parameter mismatch and asymmetrical time delays amongst the elements of the motif. We call this manner of facilitating zero-lag synchrony resonance-induced synchronization and propose that it may be a general mechanism to promote zero-lag synchrony in the brain.
| Comments: | 27 pages, 8 figures |
| Subjects: | Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:1304.5008 [q-bio.NC] |
| (or arXiv:1304.5008v1 [q-bio.NC] for this version) |
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