Can attractor network models account for the statistics of firing during persistent activity in prefrontal cortex?

Barbieri, Francesca; Brunel, Nicolas

doi:10.3389/neuro.01.003.2008

Cited by 52 publications

(54 citation statements)

References 46 publications

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“…Many neurons combined in a network with recurrent connections exhibit so-called attractor dynamics (Hopfield 1982;Barbieri and Brunel 2008) attributed mainly to the hippocampus (Rolls and Kesner 2006;Lansner 2009;Cutsuridis and Wennekers 2009). This means that different activity patterns (e.g., incomplete input patterns) lead to the same (complete) activity pattern or cell assembly that can be interpreted as a memory item.…”

Section: Physiological Mechanismmentioning

confidence: 99%

“…If these neural networks are stimulated by an input, then the complexity "stores" the input in the neural network's activity pattern for a certain duration. A similar approach are attractor networks (Barbieri and Brunel 2008), which, however, require a specific, biologically unconfirmed, signal to turn off working memory activity and previously stored knowledge. (3) Working memory-short-term plasticity (theoretical) Short-term plasticity is a physiological candidate for working memory in neuronal networks (Barak and Tsodyks 2007;Mongillo et al 2008).…”

Section: Links Between Time Scales Of Memory and Physiologymentioning

confidence: 99%

“…These polychronous neural groups are then interpreted as working-memory items (Szatmáry and Izhikevich 2010). In attractor networks, the activation of previously learned activity patterns are also interpreted as working-memory items (Barbieri and Brunel 2008;Rolls 2010). (5) Long-/short-term memory-long-term plasticity (experimental) For this link there exist much evidence [e.g., (Agranoff et al 1966;Morris 1989;RioultPedotti et al 1998)], of which some, however, are contradictory (Martin et al 2000).…”

Section: Links Between Time Scales Of Memory and Physiologymentioning

confidence: 99%

See 2 more Smart Citations

Time scales of memory, learning, and plasticity

et al. 2012

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If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

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Section: Physiological Mechanismmentioning

confidence: 99%

Section: Links Between Time Scales Of Memory and Physiologymentioning

confidence: 99%

Section: Links Between Time Scales Of Memory and Physiologymentioning

confidence: 99%

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Time scales of memory, learning, and plasticity

et al. 2012

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“…For instance, it has been reported that in ODR tasks, the coefficient of variation of the interspike interval distribution is close to 1, and even Ͼ1 for many of the neurons (Compte et al, 2003). Previous modeling works have attempted to account for this irregularity (Brunel, 2000;van Vreeswijk and Sompolinsky, 2004;Renart et al, 2007;Roudi and Latham, 2007;Barbieri and Brunel, 2008). However, in all these models, unless parameters are tuned, the neurons fire much too regularly in memory states.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Plasticity Explains Irregular Persistent Activity in Working Memory Tasks

Hansel¹,

2013

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Persistent activity in cortex is the neural correlate of working memory (WM). In persistent activity, spike trains are highly irregular, even more than in baseline. This seemingly innocuous feature challenges our current understanding of the synaptic mechanisms underlying WM. Here we argue that in WM the prefrontal cortex (PFC) operates in a regime of balanced excitation and inhibition and that the observed temporal irregularity reflects this regime. We show that this requires that nonlinearities underlying the persistent activity are primarily in the neuronal interactions between PFC neurons. We also show that short-term synaptic facilitation can be the physiological substrate of these nonlinearities and that the resulting mechanism of balanced persistent activity is robust, in particular with respect to changes in the connectivity. As an example, we put forward a computational model of the PFC circuit involved in oculomotor delayed response task. The novelty of this model is that recurrent excitatory synapses are facilitating. We demonstrate that this model displays direction-selective persistent activity. We find that, even though the memory eventually degrades because of the heterogeneities, it can be stored for several seconds for plausible network size and connectivity. This model accounts for a large number of experimental findings, such as the findings that have shown that firing is more irregular during the persistent state than during baseline, that the neuronal responses are very diverse, and that the preferred directions during cue and delay periods are strongly correlated but tuning widths are not.

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“…The above firing rate model had the advantage of being much more efficient than a spiking model, while Fisher information in spiking models (Seriès et al 2004) and rate models (Spiridon and Gerstner 2001) have been found to be qualitatively similar. Moreover, implementation of realistic variability across a wide range of firing rates is challenging in spiking models (for a review see e.g., Barbieri and Brunel 2008).…”

Section: Network Setupmentioning

confidence: 99%

The effect of neural adaptation on population coding accuracy

Cortes¹,

Marinazzo²,

Seriès³

et al. 2011

J Comput Neurosci

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Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.

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Can attractor network models account for the statistics of firing during persistent activity in prefrontal cortex?

Cited by 52 publications

References 46 publications

Time scales of memory, learning, and plasticity

Time scales of memory, learning, and plasticity

Short-Term Plasticity Explains Irregular Persistent Activity in Working Memory Tasks

The effect of neural adaptation on population coding accuracy

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