Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Critical behavior and memory function in a model of spiking neurons with a reservoir of spatio-temporal patterns

Two intriguing phenomena characterize cortical dynamics both in-vitro 6 and in-vivo: (1) the memory function, with the cue-induced and spontaneous reac7 tivation of precise dynamical spatio-temporal patterns of spikes, and (2) critical8 ity and scale-free neuronal avalanches, characterized by power law distributions of 9 bursts of spikes both in-vitro and in the resting spontaneous activity. In this paper 10 we review recent results which link together both these features, memory function 11 and critical behavior, in a model with leaky neurons whose structured connectiv12 ity comes from learning multiple spatio-temporal phase-coded patterns using a rule 13 based on Spike-Timing-Dependent Plasticity. We first study the stimulus-driven re14 play of stored patterns, measuring the storage capacity, i.e. the number of spatio15 temporal patterns of spikes that can be stored and selectively reactivated. Then we 16 focus on the noise-evoked spontaneous dynamics, in absence of stimulation. Collec17 tive patterns, which replay some of the patterns used to build the connectivity, spon18 taneously emerge. Near the phase transition between persistent replay and no-replay 19 regimes of the spontaneous dynamics, critical phenomena and neural avalanches are 20 observed, with critical exponents close to the ones experimentally measured. Previ21 ous studies have separately addressed the topics of phase-coded memory storage and 22 neuronal avalanches, and this is one of the few works which show how these ideas 23 converge in a single cortical model. This work therefore helps to link the bridge 24 between criticality and the need to have a reservoir of spatio-temporal metastable 25 memorie

The persistence of regional new business formation-activity over time – assessing the potential of policy promotion programs

New businesses, Entrepreneurship, Growth regimes, Time lags, M13, O1, O18, R11,

Effects of coordinated strategies on product and process R&D

Coordination, R&D, Innovation, Efficiency, Cost reduction, Product differentiation, C72, L1, L13, O32,