3,062 research outputs found
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
A Superconducting Nanowire-based Architecture for Neuromorphic Computing
Neuromorphic computing is poised to further the success of software-based
neural networks by utilizing improved customized hardware. However, the
translation of neuromorphic algorithms to hardware specifications is a problem
that has been seldom explored. Building superconducting neuromorphic systems
requires extensive expertise in both superconducting physics and theoretical
neuroscience. In this work, we aim to bridge this gap by presenting a tool and
methodology to translate algorithmic parameters into circuit specifications. We
first show the correspondence between theoretical neuroscience models and the
dynamics of our circuit topologies. We then apply this tool to solve linear
systems by implementing a spiking neural network with our superconducting
nanowire-based hardware.Comment: 29 pages, 10 figure
Point singularities and suprathreshold stochastic resonance in optimal coding
Motivated by recent studies of population coding in theoretical neuroscience,
we examine the optimality of a recently described form of stochastic resonance
known as suprathreshold stochastic resonance, which occurs in populations of
noisy threshold devices such as models of sensory neurons. Using the mutual
information measure, it is shown numerically that for a random input signal,
the optimal threshold distribution contains singularities. For large enough
noise, this distribution consists of a single point and hence the optimal
encoding is realized by the suprathreshold stochastic resonance effect.
Furthermore, it is shown that a bifurcational pattern appears in the optimal
threshold settings as the noise intensity increases. Fisher information is used
to examine the behavior of the optimal threshold distribution as the population
size approaches infinity.Comment: 11 pages, 3 figures, RevTe
Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and Attractor Networks
A long tradition in theoretical neuroscience casts sensory processing in the
brain as the process of inferring the maximally consistent interpretations of
imperfect sensory input. Recently it has been shown that Gamma-band inhibition
can enable neural attractor networks to approximately carry out such a sampling
mechanism. In this paper we propose a novel neural network model based on
irregular gating inhibition, show analytically how it implements a Monte-Carlo
Markov Chain (MCMC) sampler, and describe how it can be used to model networks
of both neural attractors as well as of single spiking neurons. Finally we show
how this model applied to spiking neurons gives rise to a new putative
mechanism that could be used to implement stochastic synaptic weights in
biological neural networks and in neuromorphic hardware
Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons
Networks of stochastic spiking neurons are interesting models in the area of
Theoretical Neuroscience, presenting both continuous and discontinuous phase
transitions. Here we study fully connected networks analytically, numerically
and by computational simulations. The neurons have dynamic gains that enable
the network to converge to a stationary slightly supercritical state
(self-organized supercriticality or SOSC) in the presence of the continuous
transition. We show that SOSC, which presents power laws for neuronal
avalanches plus some large events, is robust as a function of the main
parameter of the neuronal gain dynamics. We discuss the possible applications
of the idea of SOSC to biological phenomena like epilepsy and dragon king
avalanches. We also find that neuronal gains can produce collective
oscillations that coexists with neuronal avalanches, with frequencies
compatible with characteristic brain rhythms.Comment: 16 pages, 16 figures divided into 7 figures in the articl
Theoretical neuroscience: modeling the activation mechanism of potassium channels in neurons
We have modeled the electrostatic interaction between the S4 segment of the potassium channel molecule and the surrounding water molecules on both the intracellular and extracellular sides of the neural axon cell membrane. Two methods were used to approximate this interaction: (i) a macroscopic evaluation in which the water was treated as a dielectric medium with dielectric constant 80; (ii) a microscopic evaluation considering the effects of each individual water molecule fixed in position within the water pockets surrounding the S4 segment. The potential energy of the S4 due to the water pockets was plotted against the rotation of the S4 segment, while keeping the water pockets in their fixed positions. Although the two methods gave some differing results, both methods produced single well potential energy curves of ~6-9 eV depth. Based on this energy curve, we show that other forces on the S4 must create an effective torsional spring force with spring constant k~3-5 eV in order to produce a two well potential energy curve in qualitative agreement with experimental data
- …