Modelling the Replay of Dynamic Memories from Cortical Alpha Oscillations with the Sync-Fire / deSync Model

ABSTRACTWe here propose a neural network model to explore how neural oscillations might regulate the replay of memory traces. We simulate the encoding and retrieval of a series of events, where temporal sequences are uniquely identifiable by analysing population activity, as several recent EEG/MEG studies have shown. Our model comprises three parts, each considering distinct hypotheses. A cortical region actively represents sequences through the disruption of an intrinsically generated alpha rhythm, where a desynchronisation marks information-rich operations as the literature predicts. A binding region converts each event into a discrete index, enabling repetitions through a sparse encoding of events. We also instantiate a temporal region, where an oscillatory “ticking-clock” made up of hierarchical synfire chains discretely indexes a moment in time. By encoding the absolute timing between events, we show how one can use cortical desynchronisations to dynamically detect unique temporal signatures as they are replayed in the brain.

Graph analysis of looming-selective networks in the tectum, and its replication in a simple computational model

Looming stimuli evoke behavioral responses in most animals, yet the mechanisms of looming detection in vertebrates are poorly understood. Here we hypothesize that looming detection in the tectum may rely on spontaneous emergence of synfire chains: groups of neurons connected to each other in the same sequence in which they are activated during a loom. We then test some specific consequences of this hypothesis. First, we use high-speed calcium imaging to reconstruct functional connectivity of small networks within the tectum of Xenopus tadpoles. We report that reconstructed directed graphs are clustered and hierarchical, that their modularity increases in development, and that looming-selective cells tend to collect activation within these graphs. Second, we describe spontaneous emergence of looming selectivity in a computational developmental model of the tectum, governed by both synaptic and intrinsic plasticity, and driven by structured visual inputs. We show that synfire chains contribute to looming detection in the model; that structured inputs are critical for the emergence of selectivity, and that biological tectal networks follow most, but not all predictions of the model. Finally, we propose a conceptual scheme for understanding the emergence and fine-tuning of collision detection in developing aquatic animals.

A hippocampal model for behavioral time acquisition and fast bidirectional replay of spatio-temporal memory sequences

The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.

Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population Activity

Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains. Such reuse of neurons has been experimentally observed and is necessary for high capacity. Sparse networks prevent the chains from being short and cyclic and show that the formation of specific synapses is not essential for chain formation. Analysis of the learning rule in a simple network of binary threshold neurons reveals the asymptotically optimal length of the emerging chains. The theoretical results generalize to simulated networks of conductance-based leaky integrate-and-fire (LIF) neurons. As an application of the emerged chain, we propose a one-shot memory for sequences of precisely timed neuronal activity.