, 2001). AMPA “evoked mini-EPSCs” were recorded at −70 mV holding potential after the exchange of Ca2+ for Sr2+in the ACSF, and mini-EPSCs were analyzed with Mini Analysis (Synaptosoft). ON-01910 ic50 In vivo electrophysiology was performed on P9–P12 mice using a 16-site linear silicon probe (NeuroNexus Technologies)
and analyzed using Spike2 (Cambridge Electronic Design). Whisker stimulation with puffs of air was applied using a Picospritzer III (Parker). We thank Y. Zhang for her excellent technical support and members of the Crair lab for their continual feedback and valuable comments on the manuscript. This work was supported by a Brown-Coxe fellowship to H.L.; NIH grants K01 DA026504 to T.H.; R01 MH50712 to R.E.; R01 NS054273 to N.S.; R01 EY015788, 5-FU purchase T32 NS007224, and R01 MH062639 to M.C.C.; and by the family of William Ziegler III. “
“Understanding the mechanisms underlying complex behaviors requires bridging the gap between cellular properties and circuit-level interactions that drive system function. This problem is particularly acute in short-term memory systems, where the identified kinetics of synaptic and intrinsic cellular processes operate on
a much shorter time scale (typically one to hundreds of milliseconds) than the observed behavior. A neural correlate of short-term memory over the seconds to tens of seconds time scale has been identified in the persistent firing of neuronal populations during memory periods following the offset of a stimulus. Such activity has been recorded across a wide range of brain regions and tasks and has been shown to maintain representations of both discrete and graded stimuli (for review, see Brody et al., 2003, Durstewitz et al., 2000, Major and Tank, 2004 and Wang, 2001). Many explanations have been proposed
for how persistent neural activity is generated. Various studies have hypothesized roles for intrinsic neuronal properties (Egorov et al., 2002, Fall and Rinzel, 2006, Koulakov et al., 2002 and Lisman et al., 1998), synaptic mechanisms (Mongillo et al., 2008, much Shen, 1989 and Wang et al., 2006), or specialized anatomical architectures (for review, see Brody et al., 2003, Goldman, 2009 and Wang, 2001). More likely, however, the generation of memory-storing neural activity reflects a combination of cellular, synaptic, and network properties (Major and Tank, 2004). Thus, fully understanding the mechanisms underlying memory-guided behaviors will require methods that combine data from experiments probing neural circuits at each of these levels in order to relate neuronal responses to behavior. Computational modeling has been used to bridge the gap between cellular physiology, circuit interactions, and memory function. However, modeling the responses of neurons in recurrent circuits is highly challenging because each neuron’s activity influences, and is influenced by, potentially every other neuron in the circuit.