3,706 research outputs found
Snakes and ladders in an inhomogeneous neural field model
Continuous neural field models with inhomogeneous synaptic connectivities are
known to support traveling fronts as well as stable bumps of localized
activity. We analyze stationary localized structures in a neural field model
with periodic modulation of the synaptic connectivity kernel and find that they
are arranged in a snakes-and-ladders bifurcation structure. In the case of
Heaviside firing rates, we construct analytically symmetric and asymmetric
states and hence derive closed-form expressions for the corresponding
bifurcation diagrams. We show that the ideas proposed by Beck and co-workers to
analyze snaking solutions to the Swift-Hohenberg equation remain valid for the
neural field model, even though the corresponding spatial-dynamical formulation
is non-autonomous. We investigate how the modulation amplitude affects the
bifurcation structure and compare numerical calculations for steep sigmoidal
firing rates with analytic predictions valid in the Heaviside limit
Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
The occurrence of sleep passed through the evolutionary sieve and is
widespread in animal species. Sleep is known to be beneficial to cognitive and
mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the
importance of the phenomenon, a complete understanding of its functions and
underlying mechanisms is still lacking. In this paper, we show interesting
effects of deep-sleep-like slow oscillation activity on a simplified
thalamo-cortical model which is trained to encode, retrieve and classify images
of handwritten digits. During slow oscillations,
spike-timing-dependent-plasticity (STDP) produces a differential homeostatic
process. It is characterized by both a specific unsupervised enhancement of
connections among groups of neurons associated to instances of the same class
(digit) and a simultaneous down-regulation of stronger synapses created by the
training. This hierarchical organization of post-sleep internal representations
favours higher performances in retrieval and classification tasks. The
mechanism is based on the interaction between top-down cortico-thalamic
predictions and bottom-up thalamo-cortical projections during deep-sleep-like
slow oscillations. Indeed, when learned patterns are replayed during sleep,
cortico-thalamo-cortical connections favour the activation of other neurons
coding for similar thalamic inputs, promoting their association. Such mechanism
hints at possible applications to artificial learning systems.Comment: 11 pages, 5 figures, v5 is the final version published on Scientific
Reports journa
Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility
Experimental data have revealed that neuronal connection efficacy exhibits
two forms of short-term plasticity, namely, short-term depression (STD) and
short-term facilitation (STF). They have time constants residing between fast
neural signaling and rapid learning, and may serve as substrates for neural
systems manipulating temporal information on relevant time scales. The present
study investigates the impact of STD and STF on the dynamics of continuous
attractor neural networks (CANNs) and their potential roles in neural
information processing. We find that STD endows the network with slow-decaying
plateau behaviors-the network that is initially being stimulated to an active
state decays to a silent state very slowly on the time scale of STD rather than
on the time scale of neural signaling. This provides a mechanism for neural
systems to hold sensory memory easily and shut off persistent activities
gracefully. With STF, we find that the network can hold a memory trace of
external inputs in the facilitated neuronal interactions, which provides a way
to stabilize the network response to noisy inputs, leading to improved accuracy
in population decoding. Furthermore, we find that STD increases the mobility of
the network states. The increased mobility enhances the tracking performance of
the network in response to time-varying stimuli, leading to anticipative neural
responses. In general, we find that STD and STP tend to have opposite effects
on network dynamics and complementary computational advantages, suggesting that
the brain may employ a strategy of weighting them differentially depending on
the computational purpose.Comment: 40 pages, 17 figure
Spatially structured oscillations in a two-dimensional excitatory neuronal network with synaptic depression
We study the spatiotemporal dynamics of a two-dimensional excitatory neuronal network with synaptic depression. Coupling between populations of neurons is taken to be nonlocal, while depression is taken to be local and presynaptic. We show that the network supports a wide range of spatially structured oscillations, which are suggestive of phenomena seen in cortical slice experiments and in vivo. The particular form of the oscillations depends on initial conditions and the level of background noise. Given an initial, spatially localized stimulus, activity evolves to a spatially localized oscillating core that periodically emits target waves. Low levels of noise can spontaneously generate several pockets of oscillatory activity that interact via their target patterns. Periodic activity in space can also organize into spiral waves, provided that there is some source of rotational symmetry breaking due to external stimuli or noise. In the high gain limit, no oscillatory behavior exists, but a transient stimulus can lead to a single, outward propagating target wave
Short term synaptic depression improves information transfer in perceptual multistability
Competitive neural networks are often used to model the dynamics of
perceptual bistability. Switching between percepts can occur through
fluctuations and/or a slow adaptive process. Here, we analyze switching
statistics in competitive networks with short term synaptic depression and
noise. We start by analyzing a ring model that yields spatially structured
solutions and complement this with a study of a space-free network whose
populations are coupled with mutual inhibition. Dominance times arising from
depression driven switching can be approximated using a separation of
timescales in the ring and space-free model. For purely noise-driven switching,
we use energy arguments to justify how dominance times are exponentially
related to input strength. We also show that a combination of depression and
noise generates realistic distributions of dominance times. Unimodal functions
of dominance times are more easily differentiated from one another using
Bayesian sampling, suggesting synaptic depression induced switching transfers
more information about stimuli than noise-driven switching. Finally, we analyze
a competitive network model of perceptual tristability, showing depression
generates a memory of previous percepts based on the ordering of percepts.Comment: 26 pages, 15 figure
Sisyphus Effect in Pulse Coupled Excitatory Neural Networks with Spike-Timing Dependent Plasticity
The collective dynamics of excitatory pulse coupled neural networks with
spike timing dependent plasticity (STDP) is studied. Depending on the model
parameters stationary states characterized by High or Low Synchronization can
be observed. In particular, at the transition between these two regimes,
persistent irregular low frequency oscillations between strongly and weakly
synchronized states are observable, which can be identified as infraslow
oscillations with frequencies 0.02 - 0.03 Hz. Their emergence can be explained
in terms of the Sisyphus Effect, a mechanism caused by a continuous feedback
between the evolution of the coherent population activity and of the average
synaptic weight. Due to this effect, the synaptic weights have oscillating
equilibrium values, which prevents the neuronal population from relaxing into a
stationary macroscopic state.Comment: 18 pages, 24 figures, submitted to Physical Review
Phase Diagram of Spiking Neural Networks
In computer simulations of spiking neural networks, often it is assumed that
every two neurons of the network are connected by a probability of 2\%, 20\% of
neurons are inhibitory and 80\% are excitatory. These common values are based
on experiments, observations, and trials and errors, but here, I take a
different perspective, inspired by evolution, I systematically simulate many
networks, each with a different set of parameters, and then I try to figure out
what makes the common values desirable. I stimulate networks with pulses and
then measure their: dynamic range, dominant frequency of population activities,
total duration of activities, maximum rate of population and the occurrence
time of maximum rate. The results are organized in phase diagram. This phase
diagram gives an insight into the space of parameters -- excitatory to
inhibitory ratio, sparseness of connections and synaptic weights. This phase
diagram can be used to decide the parameters of a model. The phase diagrams
show that networks which are configured according to the common values, have a
good dynamic range in response to an impulse and their dynamic range is robust
in respect to synaptic weights, and for some synaptic weights they oscillate in
or frequencies, even in absence of external stimuli.Comment: oscillations are studied in this versio
Moving bumps in theta neuron networks
We consider large networks of theta neurons on a ring, synaptically coupled
with an asymmetric kernel. Such networks support stable "bumps" of activity,
which move along the ring if the coupling kernel is asymmetric. We investigate
the effects of the kernel asymmetry on the existence, stability and speed of
these moving bumps using continuum equations formally describing infinite
networks. Depending on the level of heterogeneity within the network we find
complex sequences of bifurcations as the amount of asymmetry is varied, in
strong contrast to the behaviour of a classical neural field model.Comment: To appear in Chao
- …