Development of Neural Circuitry for Precise Temporal Sequences through Spontaneous Activity, Axon Remodeling, and Synaptic Plasticity

Abstract

Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas, including songbird premotor nucleus, cat visual cortex, and primary motor cortex. Synfire chains—networks in which groups of neurons are connected via excitatory synapses into a unidirectional chain—are thought to underlie the generation of such sequences. It is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. Here, we show through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and axon remodeling—the pruning of prolific weak connections that follows the emergence of a finite number of strong connections. The formation process begins with a random network. A subset of neurons, called training neurons, intermittently receive superthreshold external input. Gradually, a synfire chain emerges through a recruiting process, in which neurons within the network connect to the tail of the chain started by the training neurons. The model is robust to varying parameters, as well as natural events like neuronal turnover and massive lesions. Our model suggests that long synfire chain can form during the development through self-organization, and axon remodeling, ubiquitous in developing neural circuits, is essential in the process.

Neural representation of time in cortico-basal ganglia circuits

“…However, additional mechanisms are needed to transform the dispersed visual responses into a variety of neural response profiles, including the time-stamp responses, that could encode unique time points in tasks with durations far beyond the visual response range. We suggest that the time representations we observed reflect an intrinsic tendency of the brain to form the basis of temporal computations, perhaps through a reward-independent selforganizing process, similar to the formation of cortical feature maps driven by natural visual stimuli (43)(44)(45), even in situations in which precise timing is not critical for performance.…”

Networks that learn the precise timing of event sequences

“…Typical computational models of sequence learning employ networks of neurons (Jun and Jin, 2007;Fiete et al, 2010;Brea et al, 2013) or populations (Abbott and Blum, 1996) that are each active for equal amounts of time during replay. However, sensory and motor processes can be governed by networks whose neurons have a fixed stimulus tuning (Xu et al, 2012;Gavornik and Bear, 2014).…”

“…Our approach is most similar to previous studies that utilize discrete populations or neurons to represent serial order (Grossberg and Merrill, 1992;Abbott and Blum, 1996;Fiete et al, 2010;Brea et al, 2013). Namely, we assume that the memory of each individual event duration is learned in parallel with the others as in Fiete et al (2010), in contrast to the serial building of chains demonstrated in the model of Jun and Jin (2007). Reset models of sequence replay are supported by comparisons of human behavioral data to models that mark event durations using a clock that is reset after each event (McAuley and Jones, 2003), suggesting errors are made locally in time, rather than accumulated event-to-event.…”

“…Various neural mechanisms have been proposed for learning the duration of a single event (Buonomano, 2000;Rao and Sejnowski, 2001;Durstewitz, 2003;Reutimann et al, 2004;Karmarkar and Buonomano, 2007;Gavornik et al, 2009), as well as the order of events in a sequence (Amari, 1972;Kleinfeld, 1986;Wang and Arbib, 1990;Abbott and Blum, 1996;Jun and Jin, 2007;Fiete et al, 2010;Brea et al, 2013). However, mechanisms for learning the precise timing of multiple events in a sequence remain largely unexplored.…”

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

“…It represents a transient state of synchronization in which spontaneous NSs can occur at a much higher rate than observed at prestimulation baseline, in a way that resembles the induced frequency of stimulation. A theoretical treatment of the role of STDP in sequence learning is beyond the scope of the current work and can be found in previous studies (Suri and Sejnowski, 2002;Yoshioka, 2002;Chao and Chen, 2005;Aoki and Aoyagi, 2007;Jun and Jin, 2007;Masuda and Kori, 2007;Hosaka et al, 2008;Kube et al, 2008;Masquelier et al, 2008;). Consistent with this work, our results argue that STDP may be a key mechanism for learning temporal sequences; indeed, a simulation that removes STDP but retains short-term adaptation does not exhibit an echoic trace that mimics the frequency of induced stimulation (Fig.…”