Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

Abstract

Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network stabilizes.

How reduction of theta rhythm by medial septum inactivation may covary with disruption of entorhinal grid cell responses due to reduced cholinergic transmission

“…In this regard, in vivo observations that cholinergic neurons in MS, compared to GABAergic neurons, have low firing rates that vary on slow time scales of several tens of seconds (Zhang et al, 2010) are not inconsistent with this conclusion, because grid cell recordings ensued 5 min after the start of lidocaine infusions into MS for the Koenig et al (2011) study, and 2 min after the start of muscimol infusions into MS for the Brandon et al (2011) study. Ongoing modeling is starting to characterize how ACh-dependent modulations of various ion channels, notably slow, medium, and fast AHP currents, can influence biophysical properties of individual spiking neurons (Palma et al, 2012b) and networks of such neurons (Palma et al, 2012a). Such studies may in the future be applied to a spiking network model of grid cells (Pilly and Grossberg, 2013) to more completely analyze the disruptive effects on grid cells of silencing the MS.…”

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum

“…Three main classes of AHP currents have been identified in a variety of mammalian species and brain regions: fast (fAHP), medium (mAHP), and slow (sAHP, Storm, 1987 ; Schwindt et al, 1988 ; Lorenzon and Foehring, 1992 ; Lee et al, 2005 ). Simulations in multi-compartment, spiking cortical cells show that ACh can shift the neuron's transfer function by diminishing sAHP and mAHP, while boosting fAHP (Palma et al, 2012a , b ), as supported by physiological recordings directly (Storm, 1987 ; Lorenzon and Foehring, 1992 ; Vogalis et al, 2003 ) or indirectly (Prakriya et al, 1996 ; Bordey et al, 2000 ; Matthews et al, 2009 ). The net effect of ACh stimulation is a leftward shift of the transfer function of neurons.…”

“…Recent modeling work demonstrates how acetylcholine can control the shape of neural input/output transfer functions by regulating AHP currents, defined as spike-dependent, hyperpolarizing currents that occur following action potentials (Palma et al, 2012a , b ). Three main classes of AHP currents have been identified in a variety of mammalian species and brain regions: fast (fAHP), medium (mAHP), and slow (sAHP, Storm, 1987 ; Schwindt et al, 1988 ; Lorenzon and Foehring, 1992 ; Lee et al, 2005 ).…”

Editorial: State-dependent brain computation

“…The question whether rhythmic entrainment represents a general mechanism of computation in the brain is raised and ways are pointed out how to address this question through empirical work in the future (Miller et al, 2012 ). The theoretical impact of neuromodulation on memory formation in spiking recurrent cortical networks is systematically evaluated (Palma et al, 2012 ). In a perspective article, a systematic account is provided how intrinsic properties of neurons and neuromodulation relates to firing patterns, functional correlations and behavior in rats (Quilichini and Bernard, 2012 ).…”