Abstract
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.
Highlights
Converging threads of evidence suggest that neural ensemble dynamics consist of intermittent population bursts with abrupt sequential transitions occurring on the order of hundreds of milliseconds
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
How can one begin to understand these complicated synergies in a principled way? We propose that an attractor memory network could learn temporal sequences using the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule [35]
Summary
Converging threads of evidence suggest that neural ensemble dynamics consist of intermittent population bursts with abrupt sequential transitions occurring on the order of hundreds of milliseconds. Similar population activity regimes are observed in cortical reactivations during sleep following motor performance or sensory exposure while the animal is awake [12,13,14]. These patterns routinely arise in the form of transient neuronal coalitions that experience concomitant shifts in their firing rates and manifest as stereotypical trajectories taken through the state space of neocortical networks. Spike-based procedures for learning the connectivity required to support attractor dynamics in this model have not yet been considered, and temporal associations between different attractor states remain absent
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