‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework
Authors: Mark G. Stokes
Journal: Trends in Cognitive Sciences (2015)

•WM is thought to depend on persistent maintenance of stationary activity states.•However, population-level analyses reveal that brain activity is highly dynamic.•Accumulating evidence implicates activity-silent neural states for WM.•Dynamic coding suggests that WM is encoded in patterns of functional connectivity. Working memory (WM) provides the functional backbone to high-level cognition. Maintenance in WM is often assumed to depend on the stationary persistence of neural activity patterns that represent memory content. However, accumulating evidence suggests that persistent delay activity does not always accompany WM maintenance but instead seems to wax and wane as a function of the current task relevance of memoranda. Furthermore, new methods for measuring and analysing population-level patterns show that activity states are highly dynamic. At first glance, these dynamics seem at odds with the very nature of WM. How can we keep a stable thought in mind while brain activity is constantly changing? This review considers how neural dynamics might be functionally important for WM maintenance. Working memory (WM) provides the functional backbone to high-level cognition. Maintenance in WM is often assumed to depend on the stationary persistence of neural activity patterns that represent memory content. However, accumulating evidence suggests that persistent delay activity does not always accompany WM maintenance but instead seems to wax and wane as a function of the current task relevance of memoranda. Furthermore, new methods for measuring and analysing population-level patterns show that activity states are highly dynamic. At first glance, these dynamics seem at odds with the very nature of WM. How can we keep a stable thought in mind while brain activity is constantly changing? This review considers how neural dynamics might be functionally important for WM maintenance. There is little doubt that the PFC is important for WM [1Goldman-Rakic P.S. Cellular basis of working memory.Neuron. 1995; 14: 477-485Abstract Full Text PDF PubMed Scopus (1851) Google Scholar]. Some of the earliest studies in nonhuman primates demonstrated that the structural [2Butters N. Pandya D. Retention of delayed-alternation: effect of selective lesions of sulcus principalis.Science. 1969; 165: 1271-1273Crossref PubMed Scopus (106) Google Scholar] and functional [3Stamm J.S. Electrical stimulation of monkeys' prefrontal cortex during delayed-response performance.J. Comp. Physiol. Psychol. 1969; 67: 535-546Crossref PubMed Scopus (64) Google Scholar] integrity of the PFC is necessary for successful maintenance of information over short delays. Although other brain areas presumably contribute some aspects of WM [4Pasternak T. Greenlee M.W. Working memory in primate sensory systems.Nat. Rev. Neurosci. 2005; 6: 97-107Crossref PubMed Scopus (489) Google Scholar, 5Postle B.R. 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Neurosci. 2012; 32: 4-11Crossref PubMed Scopus (38) Google Scholar]). This review focuses on neurophysiological recordings in nonhuman primates to disentangle the functional principles of the PFC that mediate WM. Early recordings around the principal sulcus in the monkey PFC discovered an apparent neurophysiological correlate of the WM engram: task-specific delay activity [9Fuster J.M. Alexander G.E. Neuron activity related to short-term memory.Science. 1971; 173: 652-654Crossref PubMed Scopus (1337) Google Scholar, 10Kubota K. Niki H. Prefrontal cortical unit activity and delayed alternation performance in monkeys.J. Neurophysiol. 1971; 34: 337-347PubMed Google Scholar]. For example, in a delayed reaching task some prefrontal cells increased firing during the delay period, indeed remained active for the duration that response-related information needed to be held in mind, and returned to baseline after the response was executed [9Fuster J.M. Alexander G.E. Neuron activity related to short-term memory.Science. 1971; 173: 652-654Crossref PubMed Scopus (1337) Google Scholar]. A similar sustained firing pattern is observed in a memory-guided saccade (MGS) task [11Funahashi S. et al.Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex.J. Neurophysiol. 1989; 61: 331-349PubMed Google Scholar]. In this now-classic WM task, the subject is presented with a spatial cue at the beginning of the trial that indicates the target for a saccade that can be executed only after a memory delay of a few seconds (Figure 1A) . Activity in this memory delay is selective for the specific saccade location, corresponding to the contents of WM (Figure 1B). Prefrontal delay activity is not limited to motor preparation, but also responds selectively to visual memoranda [12Miller E.K. et al.Neural mechanisms of visual working memory in prefrontal cortex of the macaque.J. Neurosci. 1996; 16: 5154-5167PubMed Google Scholar]. Similar to the delayed response tasks, PFC activity during visual delay match-to-sample tasks also covaries with the duration of maintenance in WM. Moreover, some cells even maintain their mnemonic role after the presentation of distracting items, indicating a potential correlate of distracter-resistant WM [12Miller E.K. et al.Neural mechanisms of visual working memory in prefrontal cortex of the macaque.J. Neurosci. 1996; 16: 5154-5167PubMed Google Scholar]. Evidence for persistent content-specific delay activity has motivated numerous theoretical [1Goldman-Rakic P.S. Cellular basis of working memory.Neuron. 1995; 14: 477-485Abstract Full Text PDF PubMed Scopus (1851) Google Scholar, 13Curtis C.E. D'Esposito M. Persistent activity in the prefrontal cortex during working memory.Trends Cogn. Sci. 2003; 7: 415-423Abstract Full Text Full Text PDF PubMed Scopus (1351) Google Scholar, 14Wang X.J. 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It seems that when the temporal structure of the task can be predicted, neural activity is reserved for the most relevant time point: that is, preparation for the expected memory probe. So-called ramp-up activity implies that delay activity could optimise WM-guided behaviour by preparing for the processing demands of the behavioural response (e.g., temporal orienting of attention [21Nobre A.C. et al.The hazards of time.Curr. Opin. Neurobiol. 2007; 17: 465-470Crossref PubMed Scopus (373) Google Scholar]). However, the relatively silent moments between encoding and response preparation additionally suggest that the continuity of vigorous delay activity is not always necessary for the continuity of the mental representation [20Barak O. et al.Neuronal population coding of parametric working memory.J. Neurosci. 2010; 30: 9424-9430Crossref PubMed Scopus (114) Google Scholar]. A recent neurophysiological study reported further evidence suggesting that persistent delay activity might not be necessary for accurate WM-guided behaviour [22Watanabe K. Funahashi S. Neural mechanisms of dual-task interference and cognitive capacity limitation in the prefrontal cortex.Nat. Neurosci. 2014; 17: 601-611Crossref PubMed Scopus (119) Google Scholar]. Critically, this study added a dual task demand to the classic MGS task [1Goldman-Rakic P.S. Cellular basis of working memory.Neuron. 1995; 14: 477-485Abstract Full Text PDF PubMed Scopus (1851) Google Scholar]. As in the standard variant, monkeys were required to encode and maintain the location of a saccade target for execution at the end of the trial. However, they were additionally required to attend to a specific spatial location until part way through the WM delay period (Figure 1C). This cognitive manipulation effectively abolished WM-specific delay activity during the dual task period, even on trials in which WM performance was preserved (WM-correct trials). Moreover, when the competing task demands were completed, content-specific delay activity was 'reawakened', presumably in time for WM-guided behaviour. Consistent with the evidence from ramp up, these findings suggest that mnemonic delay activity in the PFC is not always critical for maintaining the continuity of WM but can be dynamically re-established when attention is refocused to the task-relevant content. WM does not seem to depend on delay activity but might be maintained in an 'activity-silent' format such as functional connectivity. According to a synaptic model of WM, information can be maintained during such activity-silent periods as a pattern of synaptic weights [23Mongillo G. et al.Synaptic theory of working memory.Science. 2008; 319: 1543-1546Crossref PubMed Scopus (694) Google Scholar, 24Barak O. Tsodyks M. Working models of working memory.Curr. Opin. Neurobiol. 2014; 25: 20-24Crossref PubMed Scopus (113) Google Scholar], analogous to long-term memory. In this computational model (Figure 2A) , activity during encoding temporarily changes synaptic efficacy within the neural network, leaving behind a temporary synaptic memory trace via activity-dependent short-term synaptic plasticity (STSP) [25Zucker R.S. Regehr W.G. Short-term synaptic plasticity.Annu. Rev. Physiol. 2002; 64: 355-405Crossref PubMed Scopus (3177) Google Scholar]. More specifically, calcium kinetics (see Glossary) provide a window of approximately 2 s for STSP in the PFC [23Mongillo G. et al.Synaptic theory of working memory.Science. 2008; 319: 1543-1546Crossref PubMed Scopus (694) Google Scholar]; however, presumably different time constants could underlie the diversity of mnemonic timescales observed in the PFC [26Bernacchia A. et al.A reservoir of time constants for memory traces in cortical neurons.Nat. Neurosci. 2011; 14: 366-372Crossref PubMed Scopus (190) Google Scholar]. The essential point is that STSP changes the functional connectivity of the network to construct a temporary task-relevant circuit for WM-guided behaviour (see also [27Erickson M.A. et al.A single brief burst induces GluR1-dependent associative short-term potentiation: a potential mechanism for short-term memory.J. Cogn. Neurosci. 2010; 22: 2530-2540Crossref PubMed Scopus (88) Google Scholar]). According to the original computational model [23Mongillo G. et al.Synaptic theory of working memory.Science. 2008; 319: 1543-1546Crossref PubMed Scopus (694) Google Scholar], an activity-silent coding scheme is more efficient than persistent firing models. Persistent firing could be energetically expensive, especially if WM is in near-constant use constructing and maintaining an up-to-date world model of the contents of the environment and the rules that govern them. The brain consumes about 20% of total energy and it is estimated that action potentials and postsynaptic effects of glutamate account for much of this expenditure (47% and 34%, respectively [28Attwell D. Laughlin S.B. An energy budget for signaling in the grey matter of the brain.J. Cereb. Blood Flow Metab. 2001; 21: 1133-1145Crossref PubMed Scopus (2147) Google Scholar]). Therefore, neural economy places important constraints on ecologically optimal coding strategies [29Laughlin S.B. Energy as a constraint on the coding and processing of sensory information.Curr. Opin. Neurobiol. 2001; 11: 475-480Crossref PubMed Scopus (380) Google Scholar]. For example, it has been estimated that less than 1% of neurons can be activated simultaneously [30Lennie P. The cost of cortical computation.Curr. Biol. 2003; 13: 493-497Abstract Full Text Full Text PDF PubMed Scopus (684) Google Scholar]. It is difficult to rule out energy-efficient variants of persistent activity models of WM (e.g., WM codes could be sparsified across a cell population [31Woloszyn L. Sheinberg D.L. Neural dynamics in inferior temporal cortex during a visual working memory task.J. Neurosci. 2009; 29: 5494-5507Crossref PubMed Scopus (71) Google Scholar]). Nevertheless, a coding format that does not depend on an unbroken chain for persistent firing would have a clear cost advantage (i.e., sparsification in time). WM coded in patterns of functional connectivity might also provide a neurobiologically plausible mechanistic account for WM-guided behaviour as a generalised state-dependent processing. Memories are not stored as an active representation but rather change the functional architecture of the neural network for future processing. As in long-term memory, WM is expressed by the way the network responds to new input. State-dependent readout would avoid the need for explicit mechanisms for comparing the internal representation with a separate representation reflecting the new input [23Mongillo G. et al.Synaptic theory of working memory.Science. 2008; 319: 1543-1546Crossref PubMed Scopus (694) Google Scholar, 32Sugase-Miyamoto Y. et al.Short-term memory trace in rapidly adapting synapses of inferior temporal cortex.PLoS Comput. Biol. 2008; 4: e1000073Crossref PubMed Scopus (68) Google Scholar]. Moreover, because memories are stored in a format that is qualitatively different from perceptual representations, their informational content could be more resistant to interference from and/or confusion with other activity-based representations [33Olivers C.N. et al.Different states in visual working memory: when it guides attention and when it does not.Trends Cogn. Sci. 2011; 15: 327-334Abstract Full Text Full Text PDF PubMed Scopus (468) Google Scholar]. Despite considerable theoretical appeal, the synaptic model of WM is relatively difficult to test empirically. Extensive evidence confirms the prevalence of STSP [25Zucker R.S. Regehr W.G. Short-term synaptic plasticity.Annu. Rev. Physiol. 2002; 64: 355-405Crossref PubMed Scopus (3177) Google Scholar] but its functional role has not yet been fully established. Microelectrode neurophysiology can only indirectly infer connectivity via interactions between simultaneous recordings. The gold-standard evidence for a monosynaptic connection is correlated spiking between neurons. In practice, however, the probability of sampling any two neurons with a measurable monosynaptic connection is extremely low (∼1–2% of all recorded pairs in [34Fujisawa S. et al.Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex.Nat. Neurosci. 2008; 11: 823-833Crossref PubMed Scopus (424) Google Scholar]). Such a poor yield has been effectively prohibitive for testing synaptic plasticity within a standard primate neurophysiological study. Turning to the rodent model, researchers were able to collect sufficient samples of simultaneous recordings to show that the pattern of effective synaptic connections was dynamically modulated during a WM-dependent maze task [34Fujisawa S. et al.Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex.Nat. Neurosci. 2008; 11: 823-833Crossref PubMed Scopus (424) Google Scholar] (Figure 2B). Large-scale studies in the primate PFC are required to provide more specific evidence for a synaptic theory of WM. Synaptic plasticity is not the only candidate mechanism for a functional connectivity coding scheme for WM. For example, frequency-specific coherence could provide a complementary mechanism for rapid and temporary shifts in functional connectivity by phase aligning periods of excitability to maximise the opportunity for information transfer (and/or misaligning to suppress communication; communication through coherence [35Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.Trends Cogn. Sci. 2005; 9: 474-480Abstract Full Text Full Text PDF PubMed Scopus (2693) Google Scholar]). Interestingly, researchers have recently reported frequency-specific coherence that dynamically established rule-specific subnetworks within the PFC [36Buschman T.J. et al.Synchronous oscillatory neural ensembles for rules in the prefrontal cortex.Neuron. 2012; 76: 838-846Abstract Full Text Full Text PDF PubMed Scopus (279) Google Scholar] (Figure 2B). A similar mechanism for the rapid configuration of content-specific network architectures could be used to keep other forms of task-relevant material in WM [37Salazar R.F. et al.Content-specific fronto-parietal synchronization during visual working memory.Science. 2012; 338: 1097-1100Crossref PubMed Scopus (248) Google Scholar]. Population decoding methods using a sliding temporal window on high-temporal-resolution data show that brain activity is highly dynamic [38Buonomano D.V. Maass W. State-dependent computations: spatiotemporal processing in cortical networks.Nat. Rev. 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This complex spatiotemporal activation pattern implies that population coding is time-specific: the same sensory information is represented over time, but in different spatial patterns that depend on the specific time point. Although the population eventually returned to a stable state, the most discriminative information for odour classification was observed along the most dynamic phase of the response. Downstream neurons that receive output signals from these projection neurons respond most vigorously during the most dynamic phase of the response. By contrast, activity is relatively mute in these putative readout neurons after the projection cells have returned to a relatively stable state. Importantly, these dynamics are not specific to locust olfaction but are likely to constitute a general property of neural processing [38Buonomano D.V. Maass W. State-dependent computations: spatiotemporal processing in cortical networks.Nat. Rev. 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Neurophysiol. 2008; 100: 1407-1419Crossref PubMed Scopus (232) Google Scholar] and could be especially important for WM-based decision making [44Stokes M.G. et al.Dynamic coding for cognitive control in prefrontal cortex.Neuron. 2013; 78: 364-375Abstract Full Text Full Text PDF PubMed Scopus (411) Google Scholar]. At the single-unit level, dynamic coding can be distinguished from transient or sustained firing by time-specific selectivity (Figure 3A). A dynamic cell might be engaged in processing across numerous time points (cf. transient cell-firing pattern) but the coding qualitatively depends on time during the processing window (cf. sustained firing pattern). Although it might be helpful to conceptualise distinct forms of temporal profile, these temporal characteristics vary along a continuum in the PFC rather than forming very distinct categories [45Rigotti M. et al.The importance of mixed selectivity in complex cognitive tasks.Nature. 2013; 497: 585-590Crossref PubMed Scopus (720) Google Scholar, 46Miller E.K. Fusi S. Limber neurons for a nimble mind.Neuron. 2013; 78: 211-213Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar]. Such coding characteristics are best reflected in the full population response [46Miller E.K. Fusi S. Limber neurons for a nimble mind.Neuron. 2013; 78: 211-213Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar] rather than focusing on a priori defined categories of cell type [47Raposo D. et al.A category-free neural population supports evolving demands during decision-making.Nat. Neurosci. 2014; 17: 1784-1792Crossref PubMed Scopus (245) Google Scholar]. At the population level, time dependency can be expressed as a dynamic trajectory through activity state space (Figure 3B), but this is difficult to visualise beyond three dimensions. Alternatively, time dependency can be expressed in a 2D cross-temporal pattern analysis (Figure 3C), where discriminative patterns at each time point are statistically compared with discriminative patterns at every other time point [39King J.R. Dehaene S. Characterizing the dynamics of mental representations: the temporal generalization method.Trends Cogn. Sci. 2014; 18: 203-210Abstract Full Text Full Text PDF PubMed Scopus (330) Google Scholar]. In this scheme, dynamic coding gives rise to a hallmark pattern of robust decoding along the within-time diagonal axis but little or no cross-generalisation between time points [39King J.R. Dehaene S. Characterizing the dynamics of mental representations: the temporal generalization method.Trends Cogn. Sci. 2014; 18: 203-210Abstract Full Text Full Text PDF PubMed Scopus (330) Google Scholar]. Although the same information is decodable from the ensemble activity (e.g., WM content), the underlying patterns of activity differ qualitatively over time. We previously observed hallmark features of dynamic coding in a delayed paired-associate task [44Stokes M.G. et al.Dynamic coding for cognitive control in prefrontal cortex.Neuron. 2013; 78: 364-375Abstract Full Text Full Text PDF PubMed Scopus (411) Google Scholar]. The initial response to a to-be-remembered memory item triggered a dynamic trajectory through activity state space (Figure 4A) resulting in strong time dependency (Figure 4B). Yet despite the spatiotemporal complexity of this dynamic code, information decodability peaked during the most energetic and dynamic phase before settling into a low-energy stable state during the delay period. Memory content could still be decoded during the delay period but the discriminative pattern did not resemble a straightforward continuation of the initial encoding of the information during the cue period. This contrasts with a simple form of a persistent coding in which activity patterns elicited by the presentation of task-relevant information are just clamped down and maintained throughout a memory delay. The discriminative pattern was also distinct from the population code for the expected target, in contrast to a simple preactivation form of prospective coding [48Rainer G. et al.Prospective coding for objects in primate prefrontal cortex.J. Neurosci. 1999; 19: 5493-5505PubMed Google Scholar]. This pattern of dynamic coding is, however, consistent with a synaptic model of WM as outlined below. Complex population dynamics can be predicted from the evolution of state-dependent processing in which the underlying response sensitivity of the network varies over time [38Buonomano D.V. Maass W. State-dependent computations: spatiotemporal processing in cortical networks.Nat. Rev. Neurosci. 2009; 10: 113-125Crossref PubMed Scopus (569) Google Scholar]. The response sensitivity, or hidden state, of a neural network is determined by the various neurophysiological parameters that determine how the network will respond to input at any given time point, especially functional connectivity (e.g., activity-dependent STSP). This neural state is 'hidden' simply because our recording techniques typically measure activity states only; that is, the response of the hidden state to input (extrinsic or intrinsic). Importantly, the hidden state is not stationary but is influenced by past experience, including long-term synaptic plasticity but also temporary changes in functional connectivity [25Zucker R.S. Regehr W.G. Short-term synaptic plasticity.Annu. Rev. Physiol. 2002; 64: 355-405Crossref PubMed Scopus (3177) Google Scholar]. Temporal variability at the very shortest timescales means that the activity state is in constant flux due to a cascading interaction between hidden states and activity states. While input drives a specific response to a network according to the hidden state, the resultant activity state in turn reconfigures the hidden state. Therefore, the response to subsequent stimulation will trigger a unique response pattern according to the new hidden state. Moreover, this new pattern will further modulate the new hidden state of the system, thus determining the response to the next input, and so on [38Buonomano D.V. Maass W. State-dependent computations: spatiotemporal processing in cortical networks.Nat. Rev. Neurosci. 2009; 10: 113-125Crossref PubMed Scopus (569) Google Scholar]. The reciprocal interaction between the activity state and the hidden state results in a complex spatiotemporal trajectory through state space observed throughout different animal models and brain areas [49Stokes M. The spatiotemporal structure of population coding in monkey parietal cortex.J. Neurosci. 2011; 31: 1167-1169Crossref PubMed Scopus (8) Google Scholar]. The full trajectory is reproducible across trials to the extent that the hidden state relaxes back to the baseline connective pattern after some time period. This baseline hidden state is determined by more stable connections established via long-term plasticity, whereas the time constants that influence temporary changes in the hidden state determine the duration of activity-silent WM. Maintenance for longer durations presumably requires periodic refreshing; for example, via intrinsic oscillations [23Mongillo G. et al.Synaptic theory of working memory.Science. 2008; 319: 1543-1546Crossref PubMed Scopus (694) Google Scholar, 50Lisman J.E. Idiart M.A. Storage of 7 ± 2 short-term memories in oscillatory subcycles.Science. 1995; 267: 1512-1515Crossref PubMed Scopus (1029) Google Scholar] or more explicit attention-related rehearsal [51Awh E. et al.Rehearsal in spatial working memory.J. Exp. Psychol. Hum. Percept. Perform. 1998; 24: 780-790Crossref PubMed Scopus (374) Google Scholar]. Although hidden states are effectively activity silent, their structure can be inferred from the input–output behaviour of the network (i.e., state-dependent response; see Figure 5). This is a basic readout mechanism proposed for synaptic WM [23Mongillo G. et al.Synaptic theory of working memory.Science. 2008; 319: 1543-1546Crossref PubMed Scopus (694) Google Scholar, 32Sugase-Miyamoto Y. et al.Short-term memory trace in rapidly adapting synapses of inferior temporal cortex.PLoS Comput. 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