Reproducible sequence generation in random neural ensembles.

Little is known about the conditions that neural circuits have to satisfy to generate reproducible sequences. Evidently, the genetic code cannot control all the details of the complex circuits in the brain. In this Letter, we give the conditions on the connectivity degree that lead to reproducible and robust sequences in a neural population of randomly coupled excitatory and inhibitory neurons. In contrast to the traditional theoretical view we show that the sequences do not need to be learned. In the framework proposed here just the averaged characteristics of the random circuits have to be under genetic control. We found that rhythmic sequences can be generated if random networks are in the vicinity of an excitatory-inhibitory synaptic balance. Reproducible transient sequences, on the other hand, are found far from a synaptic balance.

[1]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[2]  Haim Sompolinsky,et al.  Chaotic Balanced State in a Model of Cortical Circuits , 1998, Neural Computation.

[3]  L. Glass Combinatorial and topological methods in nonlinear chemical kinetics , 1975 .

[4]  G. Laurent,et al.  Odor encoding as an active, dynamical process: experiments, computation, and theory. , 2001, Annual review of neuroscience.

[5]  R Huerta,et al.  Dynamical encoding by networks of competing neuron groups: winnerless competition. , 2001, Physical review letters.

[6]  B. Derrida,et al.  Random networks of automata: a simple annealed approximation , 1986 .

[7]  Ramón Huerta,et al.  Dynamical encoding by networks of competing neuron groups: winnerless competition. , 2001 .

[8]  J. W. Aldridge,et al.  Coding of Serial Order by Neostriatal Neurons: A “Natural Action” Approach to Movement Sequence , 1998, The Journal of Neuroscience.

[9]  H. Markram,et al.  Coding and learning of behavioral sequences , 2004, Trends in Neurosciences.

[10]  L. Glass,et al.  Stable oscillations in mathematical models of biological control systems , 1978 .

[11]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[12]  Nils Bertschinger,et al.  Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks , 2004, Neural Computation.

[13]  Richard Hans Robert Hahnloser,et al.  An ultra-sparse code underliesthe generation of neural sequences in a songbird , 2002, Nature.

[14]  A Selverston,et al.  General principles of rhythmic motor pattern generation derived from invertebrate CPGs. , 1999, Progress in brain research.

[15]  J. Cowan,et al.  A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue , 1973, Kybernetik.