Temporally Irregular Mnemonic Persistent Activity in Prefrontal Neurons of Monkeys During a Delayed Response Task

Compte, Albert; Constantinidis, Christos; Tegnér, Jesper; Raghavachari, Sridhar; Chafee, Matthew V.; Goldman‐Rakic, Patricia S.; Wang, Xiao Jing

doi:10.1152/jn.00949.2002

Cited by 251 publications

(295 citation statements)

References 82 publications

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“…The ISI distributions shown here are consistent with these observations. Poisson-like exponential tailed ISI distributions are also consistent with many experimental and modeling studies of neuronal firing patterns (Tomko and Crapper, 1974;Softky and Koch, 1993;Holt et al, 1996;Van Vreeswijk and Sompolinsky, 1996;Amit and Brunel, 1997;Shadlen and Newsome, 1998;Compte et al, 2003;Miura et al, 2007;Renart et al, 2007;Barbieri and Brunel, 2008).…”

Section: Highly Variable Low Firing Ratessupporting

confidence: 82%

Sequentially Switching Cell Assemblies in Random Inhibitory Networks of Spiking Neurons in the Striatum

Ponzi¹,

Wickens²

2010

J. Neurosci.

101

135

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The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

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Section: Highly Variable Low Firing Ratessupporting

confidence: 82%

Sequentially Switching Cell Assemblies in Random Inhibitory Networks of Spiking Neurons in the Striatum

Ponzi¹,

Wickens²

2010

J. Neurosci.

101

135

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“…5A, red vs. blue line). Such power spectra are observed in cortical cells (Bair et al 1994;Baddeley et al 1997;Compte et al 2003). The burst-like input leads to extended positive input correlations, which, similar to the case of positive power-law correlations, result in a more skewed ISI distribution and pronounced positive serial correlations (Fig.…”

Section: Presynaptic Burstinesssupporting

confidence: 53%

“…For regular presynaptic spike trains (C V,in < 1), the spectrum of the total synaptic input current exhibits a dip at low frequencies and a maximum near the firing rate of presynaptic neurons (Fig. 4A) -effects of the refractory period that are seen in a large fraction of cortical cells (Bair et al 1994;Franklin and Bair 1995;Compte et al 2003). In the corresponding input correlation function the refractory period becomes manifest by a negative trough (Fig.…”

Section: Presynaptic Refractorinessmentioning

confidence: 96%

“…Also the synapse with its conductance dynamics (Koch 1999) as well as short-term synaptic plasticity (Fortune and Rose 2001) contribute to correlate the effective input seen by the postsynaptic cell. As a consequence of both the nonlinear neural dynamics and the temporally structured driving, the interspike-interval (ISI) statistics of the driven cell is complex (Bair et al 1994;Baddeley et al 1997;Compte et al 2003;Nawrot et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

2015

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Nerve cells in the brain generate sequences of action potentials with a complex statistics. Theoretical attempts to understand this statistics were largely limited to the case of a temporally uncorrelated input (Poissonian shot noise) from the neurons in the surrounding network. However, the stimulation from thousands of other neurons has various sorts of temporal structure. Firstly, input spike trains are temporally correlated because their firing rates can carry complex signals and because of cell-intrinsic properties like neural refractoriness, bursting, or adaptation. Secondly, at the connections between neurons, the synapses, usagedependent changes in the synaptic weight (short-term plasticity) further shape the correlation structure of the effective input to the cell. From the theoretical side, it is poorly understood how these correlated stimuli, socalled colored noise, affect the spike train statistics. In particular, no standard method exists to solve the associated first-passage-time problem for the interspikeinterval statistics with an arbitrarily colored noise. Assuming that input fluctuations are weaker than the mean neuronal drive, we derive simple formulas for the essential interspike-interval statistics for a canon- ical model of a tonically firing neuron subjected to arbitrarily correlated input from the network. We verify our theory by numerical simulations for three paradigmatic situations that lead to input correlations: (i) ratecoded naturalistic stimuli in presynaptic spike trains; (ii) presynaptic refractoriness or bursting; (iii) synaptic short-term plasticity. In all cases, we find severe effects on interval statistics. Our results provide a framework for the interpretation of firing statistics measured in vivo in the brain.

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“…The remaining excitatory neurons are in a nonselective pool. [These neurons have some spontaneous firing and help to introduce some noise into the simulation, which aids in generating the almost Poisson spike firing patterns of neurons in the simulation that are a property of many neurons recorded in the brain (Compte et al, 2003;Brunel & Wang, 2001)]. All the inhibitory neurons are clustered into a common inhibitory pool, so that there is global competition throughout the network.…”

Section: Prefrontal Architecturementioning

confidence: 99%

Sequential Memory: A Putative Neural and Synaptic Dynamical Mechanism

Deco

Rolls

2005

Journal of Cognitive Neuroscience

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Abstract& A key issue in the neurophysiology of cognition is the problem of sequential learning. Sequential learning refers to the ability to encode and represent the temporal order of discrete elements occurring in a sequence. We show that the short-term memory for a sequence of items can be implemented in an autoassociation neural network. Each item is one of the attractor states of the network. The autoassociation network is implemented at the level of integrate-and-fire neurons so that the contributions of different biophysical mechanisms to sequence learning can be investigated. It is shown that if it is a property of the synapses or neurons that support each attractor state that they adapt, then everytime the network is made quiescent (e.g., by inhibition), then the attractor state that emerges next is the next item in the sequence. We show with numerical simulations implementations of the mechanisms using (1) a sodium inactivation-based spike-frequency-adaptation mechanism, (2) a Ca 2+ -activated K + current, and (3) short-term synaptic depression, with sequences of up to three items. The network does not need repeated training on a particular sequence and will repeat the items in the order that they were last presented. The time between the items in a sequence is not fixed, allowing the items to be read out as required over a period of up to many seconds. The network thus uses adaptation rather than associative synaptic modification to recall the order of the items in a recently presented sequence. &

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Temporally Irregular Mnemonic Persistent Activity in Prefrontal Neurons of Monkeys During a Delayed Response Task

Cited by 251 publications

References 82 publications

Sequentially Switching Cell Assemblies in Random Inhibitory Networks of Spiking Neurons in the Striatum

Sequentially Switching Cell Assemblies in Random Inhibitory Networks of Spiking Neurons in the Striatum

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

Sequential Memory: A Putative Neural and Synaptic Dynamical Mechanism

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