7,360 research outputs found
Design for a Darwinian Brain: Part 1. Philosophy and Neuroscience
Physical symbol systems are needed for open-ended cognition. A good way to
understand physical symbol systems is by comparison of thought to chemistry.
Both have systematicity, productivity and compositionality. The state of the
art in cognitive architectures for open-ended cognition is critically assessed.
I conclude that a cognitive architecture that evolves symbol structures in the
brain is a promising candidate to explain open-ended cognition. Part 2 of the
paper presents such a cognitive architecture.Comment: Darwinian Neurodynamics. Submitted as a two part paper to Living
Machines 2013 Natural History Museum, Londo
Emergent complex neural dynamics
A large repertoire of spatiotemporal activity patterns in the brain is the
basis for adaptive behaviour. Understanding the mechanism by which the brain's
hundred billion neurons and hundred trillion synapses manage to produce such a
range of cortical configurations in a flexible manner remains a fundamental
problem in neuroscience. One plausible solution is the involvement of universal
mechanisms of emergent complex phenomena evident in dynamical systems poised
near a critical point of a second-order phase transition. We review recent
theoretical and empirical results supporting the notion that the brain is
naturally poised near criticality, as well as its implications for better
understanding of the brain
The brain: What is critical about it?
We review the recent proposal that the most fascinating brain properties are
related to the fact that it always stays close to a second order phase
transition. In such conditions, the collective of neuronal groups can reliably
generate robust and flexible behavior, because it is known that at the critical
point there is the largest abundance of metastable states to choose from. Here
we review the motivation, arguments and recent results, as well as further
implications of this view of the functioning brain.Comment: Proceedings of BIOCOMP2007 - Collective Dynamics: Topics on
Competition and Cooperation in the Biosciences. Vietri sul Mare, Italy (2007
Dynamical principles in neuroscience
Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA
Multiple firing coherence resonances in excitatory and inhibitory coupled neurons
The impact of inhibitory and excitatory synapses in delay-coupled
Hodgkin--Huxley neurons that are driven by noise is studied. If both synaptic
types are used for coupling, appropriately tuned delays in the inhibition
feedback induce multiple firing coherence resonances at sufficiently strong
coupling strengths, thus giving rise to tongues of coherency in the
corresponding delay-strength parameter plane. If only inhibitory synapses are
used, however, appropriately tuned delays also give rise to multiresonant
responses, yet the successive delays warranting an optimal coherence of
excitations obey different relations with regards to the inherent time scales
of neuronal dynamics. This leads to denser coherence resonance patterns in the
delay-strength parameter plane. The robustness of these findings to the
introduction of delay in the excitatory feedback, to noise, and to the number
of coupled neurons is determined. Mechanisms underlying our observations are
revealed, and it is suggested that the regularity of spiking across neuronal
networks can be optimized in an unexpectedly rich variety of ways, depending on
the type of coupling and the duration of delays.Comment: 7 two-column pages, 6 figures; accepted for publication in
Communications in Nonlinear Science and Numerical Simulatio
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks
We study the collective dynamics of a Leaky Integrate and Fire network in
which precise relative phase relationship of spikes among neurons are stored,
as attractors of the dynamics, and selectively replayed at differentctime
scales. Using an STDP-based learning process, we store in the connectivity
several phase-coded spike patterns, and we find that, depending on the
excitability of the network, different working regimes are possible, with
transient or persistent replay activity induced by a brief signal. We introduce
an order parameter to evaluate the similarity between stored and recalled
phase-coded pattern, and measure the storage capacity. Modulation of spiking
thresholds during replay changes the frequency of the collective oscillation or
the number of spikes per cycle, keeping preserved the phases relationship. This
allows a coding scheme in which phase, rate and frequency are dissociable.
Robustness with respect to noise and heterogeneity of neurons parameters is
studied, showing that, since dynamics is a retrieval process, neurons preserve
stablecprecise phase relationship among units, keeping a unique frequency of
oscillation, even in noisy conditions and with heterogeneity of internal
parameters of the units
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