412 research outputs found
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
Non-destructive whole-brain monitoring using nanorobots : neural electrical data rate requirements
Neuronanorobotics, a promising future medical technology, may provide the ultimate tool for achieving comprehensive non-destructive real-time in vivo monitoring of the many information channels in the human brain. This paper focuses on the electrical information channel and employs a novel electrophysiological approach to estimate the data rate requirements, calculated to be (5.52 ± 1.13) x 1016 bits/sec in an entire living human brain, for acquiring, transmitting, and storing singleneuron electrical information using medical nanorobots, corresponding to an estimated synapticprocessed spike rate of
(4.31 ± 0.86) x 1015 spikes/sec.Centro de Matemática da Universidade do Minho (CMAT). The principal author (NRBM) thanks the Fundação para a Ciência e Tecnologia (FCT) for their financial support of this work (grant SFRH/BD/69660/2010)
Mecanismos de codificación y procesamiento de información en redes basadas en firmas neuronales
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 21-02-202
Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role
The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN . These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing
Noise-induced behaviors in neural mean field dynamics
The collective behavior of cortical neurons is strongly affected by the
presence of noise at the level of individual cells. In order to study these
phenomena in large-scale assemblies of neurons, we consider networks of
firing-rate neurons with linear intrinsic dynamics and nonlinear coupling,
belonging to a few types of cell populations and receiving noisy currents.
Asymptotic equations as the number of neurons tends to infinity (mean field
equations) are rigorously derived based on a probabilistic approach. These
equations are implicit on the probability distribution of the solutions which
generally makes their direct analysis difficult. However, in our case, the
solutions are Gaussian, and their moments satisfy a closed system of nonlinear
ordinary differential equations (ODEs), which are much easier to study than the
original stochastic network equations, and the statistics of the empirical
process uniformly converge towards the solutions of these ODEs. Based on this
description, we analytically and numerically study the influence of noise on
the collective behaviors, and compare these asymptotic regimes to simulations
of the network. We observe that the mean field equations provide an accurate
description of the solutions of the network equations for network sizes as
small as a few hundreds of neurons. In particular, we observe that the level of
noise in the system qualitatively modifies its collective behavior, producing
for instance synchronized oscillations of the whole network, desynchronization
of oscillating regimes, and stabilization or destabilization of stationary
solutions. These results shed a new light on the role of noise in shaping
collective dynamics of neurons, and gives us clues for understanding similar
phenomena observed in biological networks
Modular architecture facilitates noise-driven control of synchrony in neuronal networks
H.Y., A.H.-I., and S.S. acknowledge MEXT Grant-in-Aid for Transformative Research
Areas (B) “Multicellular Neurobiocomputing” (21H05164), JSPS KAKENHI (18H03325, 19H00846,
20H02194, 20K20550, 22H03657, 22K19821, 22KK0177, and 23H03489), JST-PRESTO
(JMPJPR18MB), JST-CREST (JPMJCR19K3), and Tohoku University RIEC Cooperative Research
Project Program for financial support. F.P.S., V.P., and J.Z. received support from the Max-Planck-Society. F.P.S. acknowledges funding by SMARTSTART, the joint training program in
computational neuroscience by the VolkswagenStiftung and the Bernstein Network. F.P.S. and
V.P. were funded by the German Research Foundation (Deutsche Forschungsgemeinschaft,
DFG), SFB-1528–Cognition of Interaction. V.P. was supported by the DFG under Germany’s
Excellence Strategy EXC 2067/1- 390729940. V.B. and A.L. were supported by a Sofja
Kovalevskaja Award from the Alexander von Humboldt Foundation, endowed by the Federal
Ministry of Education and Research. A.L. is a member of the Machine Learning Cluster of
Excellence EXC 2064/1- 39072764. M.A.M. acknowledges the Spanish Ministry and Agencia
Estatal de investigación (AEI) through Project of I + D + i (PID2020-113681GB-I00), financed by
MICIN/AEI/10.13039/501100011033 and FEDER “A way to make Europe”, and the Consejería de
Conocimiento, Investigación Universidad, Junta de Andalucía and European Regional
Development Fund (P20-00173) for financial support. J.Z. received financial support from the
Joachim Herz Stiftung. J.S. acknowledges Horizon 2020 Future and Emerging Technologies
(grant agreement 964877-NEUChiP), Ministerio de Ciencia, Innovación y Universidades
(PID2019-108842GB-C21), and Departament de Recerca i Universitats, Generalitat de Catalunya
(2017-SGR-1061 and 2021-SGR-00450) for financial support.Supplementary Materials This PDF file includes: Supplementary Text, file:///D:/Modular-architecture-facilitates-.pdfHigh-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.D+i:
P20-00173, PID2020-113681GB-I00Innovación y Universidades
PID2019-108842GB-C21Horizon2020 Future and Emerging Technologies 964877-NEUChiPMinisterio de Ciencia, Innovación y Universidades
(PID2019-108842GB-C21)Departament de Recerca i Universitats, Generalitat de Catalunya
(2017-SGR-1061, 2021-SGR-00450)MICIN/AEI/10.13039/501100011033FEDER “A way to make Europe”Junta de AndalucíaEuropean Regional
Development Fun
On the functions, mechanisms, and malfunctions of intracortical contextual modulation
A broad neuron-centric conception of contextual modulation is reviewed and re-assessed in the light of recent neurobiological studies of amplification, suppression, and synchronization. Behavioural and computational studies of perceptual and higher cognitive functions that depend on these processes are outlined, and evidence that those functions and their neuronal mechanisms are impaired in schizophrenia is summarized. Finally, we compare and assess the long-term biological functions of contextual modulation at the level of computational theory as formalized by the theories of coherent infomax and free energy reduction. We conclude that those theories, together with the many empirical findings reviewed, show how contextual modulation at the neuronal level enables the cortex to flexibly adapt the use of its knowledge to current circumstances by amplifying and grouping relevant activities and by suppressing irrelevant activities
Clinical Electroencephalography for Anesthesiologists
The widely used electroencephalogram-based indices for depth-of-Anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant TR01-GM104948
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules
Most elementary behaviors such as moving the arm to grasp an object or
walking into the next room to explore a museum evolve on the time scale of
seconds; in contrast, neuronal action potentials occur on the time scale of a
few milliseconds. Learning rules of the brain must therefore bridge the gap
between these two different time scales.
Modern theories of synaptic plasticity have postulated that the co-activation
of pre- and postsynaptic neurons sets a flag at the synapse, called an
eligibility trace, that leads to a weight change only if an additional factor
is present while the flag is set. This third factor, signaling reward,
punishment, surprise, or novelty, could be implemented by the phasic activity
of neuromodulators or specific neuronal inputs signaling special events. While
the theoretical framework has been developed over the last decades,
experimental evidence in support of eligibility traces on the time scale of
seconds has been collected only during the last few years.
Here we review, in the context of three-factor rules of synaptic plasticity,
four key experiments that support the role of synaptic eligibility traces in
combination with a third factor as a biological implementation of neoHebbian
three-factor learning rules
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