284 research outputs found
Form, function, mind: what doesn't compute (and what might)
The applicability of computational and dynamical systems models to organisms
is scrutinized, using examples from developmental biology and cognition.
Developmental morphogenesis is dependent on the inherent material properties of
developing tissues, a non-computational modality, but cell differentiation,
which utilizes chromatin-based revisable memory banks and program-like
function-calling, via the developmental gene co-expression system unique to
metazoans, has a quasi-computational basis. Multi-attractor dynamical models
are argued to be misapplied to global properties of development, and it is
suggested that along with computationalism, dynamicism is similarly unsuitable
to accounting for cognitive phenomena. Proposals are made for treating brains
and other nervous tissues as novel forms of excitable matter with inherent
properties which enable the intensification of cell-based basal cognition
capabilities present throughout the tree of life
Field Effects in the CNS Play Functional Roles
An endogenous electrical field effect, i.e., ephaptic transmission, occurs when an electric field associated with activity occurring in one neuron polarizes the membrane of another neuron. It is well established that field effects occur during pathological conditions, such as epilepsy, but less clear if they play a functional role in the healthy brain. Here, we describe the principles of field effect interactions, discuss identified field effects in diverse brain structures from the teleost Mauthner cell to the mammalian cortex, and speculate on the function of these interactions. Recent evidence supports that relatively weak endogenous and exogenous field effects in laminar structures reach significance because they are amplified by network interactions. Such interactions may be important in rhythmogenesis for the cortical slow wave and hippocampal sharp wave–ripple, and also during transcranial stimulation
The investigation of variable nernst equilibria on isolated neurons and coupled neurons forming discrete and continuous networks
Since the introduction of the Hodgkin-Huxley equations, used to describe the excitation of neurons, the Nernst equilibria for individual ion channels have assumed to be constant in time. Recent biological recordings call into question the validity of this assumption. Very little theoretical work has been done to address the issue of accounting for these non-static Nernst equilibria using the Hodgkin-Huxley formalism. This body of work incorporates non-static Nernst equilibria into the generalized Hodgkin-Huxley formalism by considering the first-order effects of the Nernst equation. It is further demonstrated that these effects are likely dominate in neurons with diameters much smaller than that of the squid giant axon and permeate important information processing regions of the brain such as the hippocampus. Particular results of interest include single-cell bursting due to the interplay of spatially separated neurons, pattern formation via spiral waves within a soliton-like regime, and quantifiable shifts in the multifractality of hippocampal neurons under the administration of various drugs at varying dosages. This work provides a new perspective on the variability of Nernst equilibria and demonstrates its utility in areas such as pharmacology and information processing
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
Conedy: a scientific tool to investigate Complex Network Dynamics
We present Conedy, a performant scientific tool to numerically investigate
dynamics on complex networks. Conedy allows to create networks and provides
automatic code generation and compilation to ensure performant treatment of
arbitrary node dynamics. Conedy can be interfaced via an internal script
interpreter or via a Python module
Biocomputing Model Using Tripartite Synapses Provides Reliable Neuronal Logic Gating with Spike Pattern Diversity
Biocomputing technologies exploit biological communication mechanisms
involving cell-cell signal propagation to perform computations. Researchers
recently worked toward realising logic gates made by neurons to develop novel
devices such as organic neuroprostheses or brain implants made by cells, herein
termed living implants. Several challenges arise from this approach, mainly
associated with the stochastic nature and noise of neuronal communication.
Since astrocytes play a crucial role in the regulation of neurons activity,
there is a possibility whereby astrocytes can be engineered to control synapses
favouring reliable biocomputing. This work proposes a mathematical model of
neuronal logic gates involving neurons and astrocytes, realising OR and AND
gating. We use a shallow coupling of both the Izhikevich and Postnov models to
characterise gating responses with spike pattern variability and astrocyte
synaptic regulation. Logic operation error ratio and accuracy assess the AND
and OR gates' performances at different synaptic Gaussian noise levels. Our
results demonstrate that the astrocyte regulating activity can effectively be
used as a denoising mechanism, paving the way for highly reliable biocomputing
implementations.Comment: Submitted for journal publication 202
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
A biophysical model explains the spontaneous bursting behavior in the developing retina
During early development, waves of activity propagate across the retina and
play a key role in the proper wiring of the early visual system. During the
stage II these waves are triggered by a transient network of neurons, called
Starburst Amacrine Cells (SACs), showing a bursting activity which disappears
upon further maturation. While several models have attempted to reproduce
retinal waves, none of them is able to mimic the rhythmic autonomous bursting
of individual SACs and reveal how these cells change their intrinsic properties
during development. Here, we introduce a mathematical model, grounded on
biophysics, which enables us to reproduce the bursting activity of SACs and to
propose a plausible, generic and robust, mechanism that generates it. The core
parameters controlling repetitive firing are fast depolarizing -gated
calcium channels and hyperpolarizing -gated potassium channels. The
quiescent phase of bursting is controlled by a slow after hyperpolarization
(sAHP), mediated by calcium-dependent potassium channels. Based on a
bifurcation analysis we show how biophysical parameters, regulating calcium and
potassium activity, control the spontaneously occurring fast oscillatory
activity followed by long refractory periods in individual SACs. We make a
testable experimental prediction on the role of voltage-dependent potassium
channels on the excitability properties of SACs and on the evolution of this
excitability along development. We also propose an explanation on how SACs can
exhibit a large variability in their bursting periods, as observed
experimentally within a SACs network as well as across different species, yet
based on a simple, unique, mechanism. As we discuss, these observations at the
cellular level have a deep impact on the retinal waves description.Comment: 25 pages, 13 figures, submitte
Multiple-Color Optical Activation, Silencing, and Desynchronization of Neural Activity, with Single-Spike Temporal Resolution
The quest to determine how precise neural activity patterns mediate computation, behavior, and pathology would be greatly aided by a set of tools for reliably activating and inactivating genetically targeted neurons, in a temporally precise and rapidly reversible fashion. Having earlier adapted a light-activated cation channel, channelrhodopsin-2 (ChR2), for allowing neurons to be stimulated by blue light, we searched for a complementary tool that would enable optical neuronal inhibition, driven by light of a second color. Here we report that targeting the codon-optimized form of the light-driven chloride pump halorhodopsin from the archaebacterium Natronomas pharaonis (hereafter abbreviated Halo) to genetically-specified neurons enables them to be silenced reliably, and reversibly, by millisecond-timescale pulses of yellow light. We show that trains of yellow and blue light pulses can drive high-fidelity sequences of hyperpolarizations and depolarizations in neurons simultaneously expressing yellow light-driven Halo and blue light-driven ChR2, allowing for the first time manipulations of neural synchrony without perturbation of other parameters such as spiking rates. The Halo/ChR2 system thus constitutes a powerful toolbox for multichannel photoinhibition and photostimulation of virally or transgenically targeted neural circuits without need for exogenous chemicals, enabling systematic analysis and engineering of the brain, and quantitative bioengineering of excitable cells
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