19 research outputs found
Computational paradigm for dynamic logic-gates in neuronal activity
In 1943 McCulloch and Pitts suggested that the brain is composed of reliable
logic-gates similar to the logic at the core of today's computers. This
framework had a limited impact on neuroscience, since neurons exhibit far
richer dynamics. Here we propose a new experimentally corroborated paradigm in
which the truth tables of the brain's logic-gates are time dependent, i.e.
dynamic logicgates (DLGs). The truth tables of the DLGs depend on the history
of their activity and the stimulation frequencies of their input neurons. Our
experimental results are based on a procedure where conditioned stimulations
were enforced on circuits of neurons embedded within a large-scale network of
cortical cells in-vitro. We demonstrate that the underlying biological
mechanism is the unavoidable increase of neuronal response latencies to ongoing
stimulations, which imposes a nonuniform gradual stretching of network delays.
The limited experimental results are confirmed and extended by simulations and
theoretical arguments based on identical neurons with a fixed increase of the
neuronal response latency per evoked spike. We anticipate our results to lead
to better understanding of the suitability of this computational paradigm to
account for the brain's functionalities and will require the development of new
systematic mathematical methods beyond the methods developed for traditional
Boolean algebra.Comment: 32 pages, 14 figures, 1 tabl
Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity
The increasing number of recording electrodes enhances the capability of
capturing the network's cooperative activity, however, using too many monitors
might alter the properties of the measured neural network and induce noise.
Using a technique that merges simultaneous multi-patch-clamp and
multi-electrode array recordings of neural networks in-vitro, we show that the
membrane potential of a single neuron is a reliable and super-sensitive probe
for monitoring such cooperative activities and their detailed rhythms.
Specifically, the membrane potential and the spiking activity of a single
neuron are either highly correlated or highly anti-correlated with the
time-dependent macroscopic activity of the entire network. This surprising
observation also sheds light on the cooperative origin of neuronal burst in
cultured networks. Our findings present an alternative flexible approach to the
technique based on a massive tiling of networks by large-scale arrays of
electrodes to monitor their activity.Comment: 36 pages, 9 figure
An experimental evidence-based computational paradigm for new logic-gates in neuronal activity
We propose a new experimentally corroborated paradigm in which the
functionality of the brain's logic-gates depends on the history of their
activity, e.g. an OR-gate that turns into a XOR-gate over time. Our results are
based on an experimental procedure where conditioned stimulations were enforced
on circuits of neurons embedded within a large-scale network of cortical cells
in-vitro. The underlying biological mechanism is the unavoidable increase of
neuronal response latency to ongoing stimulations, which imposes a non-uniform
gradual stretching of network delays.Comment: 10 pages, 4 figures, 1 tabl
Fast Reversible Learning based on Neurons functioning as Anisotropic Multiplex Hubs
Neural networks are composed of neurons and synapses, which are responsible
for learning in a slow adaptive dynamical process. Here we experimentally show
that neurons act like independent anisotropic multiplex hubs, which relay and
mute incoming signals following their input directions. Theoretically, the
observed information routing enriches the computational capabilities of neurons
by allowing, for instance, equalization among different information routes in
the network, as well as high-frequency transmission of complex time-dependent
signals constructed via several parallel routes. In addition, this kind of hubs
adaptively eliminate very noisy neurons from the dynamics of the network,
preventing masking of information transmission. The timescales for these
features are several seconds at most, as opposed to the imprint of information
by the synaptic plasticity, a process which exceeds minutes. Results open the
horizon to the understanding of fast and adaptive learning realities in higher
cognitive functionalities of the brain.Comment: 6 pages, 4 figure
Broadband Macroscopic Cortical Oscillations Emerge from Intrinsic Neuronal Response Failures
Broadband spontaneous macroscopic neural oscillations are rhythmic cortical
firing which were extensively examined during the last century, however, their
possible origination is still controversial. In this work we show how
macroscopic oscillations emerge in solely excitatory random networks and
without topological constraints. We experimentally and theoretically show that
these oscillations stem from the counterintuitive underlying mechanism - the
intrinsic stochastic neuronal response failures. These neuronal response
failures, which are characterized by short-term memory, lead to cooperation
among neurons, resulting in sub- or several- Hertz macroscopic oscillations
which coexist with high frequency gamma oscillations. A quantitative interplay
between the statistical network properties and the emerging oscillations is
supported by simulations of large networks based on single-neuron in-vitro
experiments and a Langevin equation describing the network dynamics. Results
call for the examination of these oscillations in the presence of inhibition
and external drives.Comment: 21 pages, 5 figure
Sudden synchrony leaps accompanied by frequency multiplications in neuronal activity
A classical view of neural coding relies on temporal firing synchrony among
functional groups of neurons; however the underlying mechanism remains an
enigma. Here we experimentally demonstrate a mechanism where time-lags among
neuronal spiking leap from several tens of milliseconds to nearly zero-lag
synchrony. It also allows sudden leaps out of synchrony, hence forming short
epochs of synchrony. Our results are based on an experimental procedure where
conditioned stimulations were enforced on circuits of neurons embedded within a
large-scale network of cortical cells in vitro and are corroborated by
simulations of neuronal populations. The underlying biological mechanisms are
the unavoidable increase of the neuronal response latency to ongoing
stimulations and temporal or spatial summation required to generate evoked
spikes. These sudden leaps in and out of synchrony may be accompanied by
multiplications of the neuronal firing frequency, hence offering reliable
information-bearing indicators which may bridge between the two principal
neuronal coding paradigms.Comment: 23 pages, 3 figure
Neuronal Response Impedance Mechanism Implementing Cooperative Networks with Low Firing Rates and Microseconds Precision
Realizations of low firing rates in neural networks usually require globally
balanced distributions among excitatory and inhibitory links, while feasibility
of temporal coding is limited by neuronal millisecond precision. We show that
cooperation, governing global network features, emerges through nodal
properties, as opposed to link distributions. Using in vitro and in vivo
experiments we demonstrate microsecond precision of neuronal response timings
under low stimulation frequencies, whereas moderate frequencies result in a
chaotic neuronal phase characterized by degraded precision. Above a critical
stimulation frequency, which varies among neurons, response failures were found
to emerge stochastically such that the neuron functions as a low pass filter,
saturating the average inter-spike-interval. This intrinsic neuronal response
impedance mechanism leads to cooperation on a network level, such that firing
rates are suppressed towards the lowest neuronal critical frequency
simultaneously with neuronal microsecond precision. Our findings open up
opportunities of controlling global features of network dynamics through few
nodes with extreme properties.Comment: 35 pages, 13 figure