22,306 research outputs found
Adaptation to criticality through organizational invariance in embodied agents
Many biological and cognitive systems do not operate deep within one or other
regime of activity. Instead, they are poised at critical points located at
phase transitions in their parameter space. The pervasiveness of criticality
suggests that there may be general principles inducing this behaviour, yet
there is no well-founded theory for understanding how criticality is generated
at a wide span of levels and contexts. In order to explore how criticality
might emerge from general adaptive mechanisms, we propose a simple learning
rule that maintains an internal organizational structure from a specific family
of systems at criticality. We implement the mechanism in artificial embodied
agents controlled by a neural network maintaining a correlation structure
randomly sampled from an Ising model at critical temperature. Agents are
evaluated in two classical reinforcement learning scenarios: the Mountain Car
and the Acrobot double pendulum. In both cases the neural controller appears to
reach a point of criticality, which coincides with a transition point between
two regimes of the agent's behaviour. These results suggest that adaptation to
criticality could be used as a general adaptive mechanism in some
circumstances, providing an alternative explanation for the pervasive presence
of criticality in biological and cognitive systems.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0525
Attractor Modulation and Proliferation in 1+ Dimensional Neural Networks
We extend a recently introduced class of exactly solvable models for
recurrent neural networks with competition between 1D nearest neighbour and
infinite range information processing. We increase the potential for further
frustration and competition in these models, as well as their biological
relevance, by adding next-nearest neighbour couplings, and we allow for
modulation of the attractors so that we can interpolate continuously between
situations with different numbers of stored patterns. Our models are solved by
combining mean field and random field techniques. They exhibit increasingly
complex phase diagrams with novel phases, separated by multiple first- and
second order transitions (dynamical and thermodynamic ones), and, upon
modulating the attractor strengths, non-trivial scenarios of phase diagram
deformation. Our predictions are in excellent agreement with numerical
simulations.Comment: 16 pages, 15 postscript figures, Late
Effects of Noise in a Cortical Neural Model
Recently Segev et al. (Phys. Rev. E 64,2001, Phys.Rev.Let. 88, 2002) made
long-term observations of spontaneous activity of in-vitro cortical networks,
which differ from predictions of current models in many features. In this paper
we generalize the EI cortical model introduced in a previous paper (S.Scarpetta
et al. Neural Comput. 14, 2002), including intrinsic white noise and analyzing
effects of noise on the spontaneous activity of the nonlinear system, in order
to account for the experimental results of Segev et al.. Analytically we can
distinguish different regimes of activity, depending from the model parameters.
Using analytical results as a guide line, we perform simulations of the
nonlinear stochastic model in two different regimes, B and C. The Power
Spectrum Density (PSD) of the activity and the Inter-Event-Interval (IEI)
distributions are computed, and compared with experimental results. In regime B
the network shows stochastic resonance phenomena and noise induces aperiodic
collective synchronous oscillations that mimic experimental observations at 0.5
mM Ca concentration. In regime C the model shows spontaneous synchronous
periodic activity that mimic activity observed at 1 mM Ca concentration and the
PSD shows two peaks at the 1st and 2nd harmonics in agreement with experiments
at 1 mM Ca. Moreover (due to intrinsic noise and nonlinear activation function
effects) the PSD shows a broad band peak at low frequency. This feature,
observed experimentally, does not find explanation in the previous models.
Besides we identify parametric changes (namely increase of noise or decreasing
of excitatory connections) that reproduces the fading of periodicity found
experimentally at long times, and we identify a way to discriminate between
those two possible effects measuring experimentally the low frequency PSD.Comment: 25 pages, 10 figures, to appear in Phys. Rev.
Generating functionals for autonomous latching dynamics in attractor relict networks
Coupling local, slowly adapting variables to an attractor network allows to destabilize all attractors, turning them into attractor ruins. The resulting attractor relict network may show ongoing autonomous latching dynamics. We propose to use two generating functionals for the construction of attractor relict networks, a Hopfield energy functional generating a neural attractor network and a functional based on information-theoretical principles, encoding the information content of the neural firing statistics, which induces latching transition from one transiently stable attractor ruin to the next. We investigate the influence of stress, in terms of conflicting optimization targets, on the resulting dynamics. Objective function stress is absent when the target level for the mean of neural activities is identical for the two generating functionals and the resulting latching dynamics is then found to be regular. Objective function stress is present when the respective target activity levels differ, inducing intermittent bursting latching dynamics
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