Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

Abstract

When a sensory cue was repeatedly followed by a behavioral event with fixed delays, pairs of premotor and primary motor neurons showed significant increases of coincident spikes at times a monkey was expecting the event. These results provided evidence that neuronal firing synchrony has predictive power. To elucidate the underlying mechanism, here we argue some nontrivial characteristics of the predictive synchronous firing developed by spike-timing-dependent plasticity in a paradigm similar to classical conditioning. We find that the computationally developed synchrony shows the modulations of temporal precision, which are quite similar to those observed experimentally. Thus, our model suggests that the important characteristics of predictive synchronous firing, which were previously attributed to an animal's higher cognitive function, can emerge from a synaptic-level mechanism.It is clearly advantageous for animals to detect and remember the causal relationships between events, to predict their occurrence and to prepare for a response (Barto et al. 1983;Desmond and Moore 1988: Tanji andShima 1994;Schultz et al. 1997;Schultz 1998). Recent studies show that rate changes and synchronous firing of cortical neurons differentially engage in these cognitive functions. Riehle et al. (1997) trained monkeys on a delayed response task in which a GO signal to instruct a motor response repeatedly followed a sensory cue at several possible, fixed delays. Spikes from pairs of premotor and primary motor (PM/MI) neurons coincided more frequently than expected by chance, particularly at those times when the monkeys were expecting the GO signal. If the GO signal was actually presented at one of those times, the synchronous firing was followed by rate modulations.

Computer simulations of synchrony and oscillations evoked by two coherent inputs

“…In other words, periodic synchronies were much observed in the neural network model with STDP, while aperiodic spike patterns were remarkably observed in the model without STDP. This result is correspondent with the results of previous numerical studies (Kitano and Fukai 2004;Hosaka et al 2008). In the previous study, the transformation function of STDP from a spatiotemporal pattern into a synchrony has been examined and analyzed using computer simulations (Hosaka et al 2008).…”

“…If sensory and top-down signals are input to the interconnected neural network model with spike-timing dependent synaptic plasticity (STDP) (Markram et al 1996;Bi and Poo 1999), the network learns the transformation from the input spatiotemporal pattern into a synchronous spike (Kitano and Fukai 2004;Hosaka et al 2008). As the previous studies, STDP is assumed to be applied only to the connections between excitatory neurons.…”

Local cortical circuit model inferred from power-law distributed neuronal avalanches

“…We may regard the developed network as an ensemble of predominantly feedforward subnetworks ("synfire chains") comprising overlapping cell assemblies. Synfire chains accounted for precisely-timed spike sequences observed in in vivo and in vitro cortical networks (Abeles, 1991;Prut et al, 1998;Reyes, 2003;Ikegaya et al, 2004;Kitano and Fukai, 2004), and were recently revived as a candidate of the avian neuronal circuits engaging in song learning (Hahnloser et al, 2002;Kimpo et al, 2003). While a purely-feedforward synfire chain has extensively been studied in computational models (Diesmann et al, 1999;Cateau and Fukai, 2001), its generalization with a more realistic wiring pattern of cortical neurons seems difficult (Mehring et al, 2003;Vogels et al, 2005).…”

Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model

“…Most of the available spiking neuron models with STDP assume a default symmetric profile at all synapses with the same learning windows (e.g. Kitano & Fukai 2004, Rao & Sejnowski 2001, Van Rosssum et al 2000. Given that STDP learning rules can modify sensory processing, for example modify receptive fields and orientation preferences in adult cats (Yao & Dan 2001, Fu et al 2002, it is likely that the temporal window of learning plays an important role.…”