This occurs because

sequential activation of neurons in a recurrent network drives LTP at synapses in the forward direction but LTD in the reverse, thus creating directional connections (Clopath et al., 2010). The result is tuning for learned sequences, direction-selective visual responses, spontaneous repeated spike sequences Perifosine cost for motor patterning, and the ability to predict future events from past stimuli (e.g., Mehta et al., 2000; Buchs and Senn, 2002; Engert et al., 2002; Fiete et al., 2010). STDP also enforces synchronous spiking during signal propagation in feedforward networks, which is a common feature in vivo. To understand this, consider a feedforward network in which neurons exhibit a range of spike latencies to a synchronous network input. With STDP, feedforward synapses onto neurons that spike earliest are weakened, thereby increasing spike latency, while

synapses onto neurons that spike later are strengthened, reducing their spike latency (Gerstner et al., 1996; Suri and Sejnowski, 2002). This has been directly observed in the insect olfactory system (Cassenaer and Laurent, 2007). find more STDP can also mediate temporal difference learning (Rao and Sejnowski, 2003) and reinforcement learning (Farries and Fairhall, 2007; Izhikevich, 2007; Cassenaer and Laurent, 2012) and can tune neurons for temporal features of input (Masquelier et al., 2009). For anti-Hebbian STDP, fewer computational properties are understood. In the cerebellum-like electrosensory lobe of electric fish,

the LTD component of this plasticity (anti-Hebbian LTD) stores negative images of predicted sensory input, so that novel (unexpected) sensory inputs can be better represented (Roberts and Bell, 2000; Requarth and Sawtell, 2011). Anti-Hebbian LTD at parallel fiber-Purkinje cell synapses in mammalian cerebellum may perform a similar computation. Anti-Hebbian STDP is also prominent in distal dendrites of pyramidal cells (Sjöström and Häusser, 2006; Letzkus et al., 2006). This may serve to not strengthen late-spiking distal (layer 1) inputs which would have been weakened under Hebbian STDP (Rumsey and Abbott, 2004). Alternatively, anti-Hebbian LTD may keep distal synapses weak, thereby requiring greater firing synchrony for effective transmission and specializing distal versus proximal synapses for different computations (Sjöström and Häusser, 2006). Theory has also shed light on the basis and functional properties of multi-factor STDP. In an early study, the firing rate and timing dependence of plasticity was predicted from dynamic activation and calcium-dependent inactivation of NMDA receptors during pre- and postsynaptic spike trains (Senn et al., 2001). More recent biophysically realistic models of NMDA receptors, AMPA receptors, and cannabinoid signaling support and extend this unified model of plasticity (Shouval et al., 2002; Badoual et al., 2006; Rachmuth et al., 2011; Graupner and Brunel, 2012).