Publications

• Elissa Sutlief, Charlie Walters, Tanya Marton, Marshall G Hussain Shuler (2025) The value of initiating a pursuit in temporal decision-making eLife 13:RP99957  https://doi.org/10.7554/eLife.99957.3

• Allard S, Hussain Shuler MG. Cholinergic reinforcement signaling is impaired by amyloidosis prior to its synaptic loss. J Neurosci. 2023 Aug 30:JN-RM-0967-23. doi: 10.1523/JNEUROSCI.0967-23.2023. PMID: 37648452.

• Vogelstein JT, Verstynen T, …Hussain Shuler MG, et al. 2022. “Prospective Learning: Back to the Future.” ArXiv [Cs.LG]. arXiv. http://arxiv.org/abs/2201.07372.

• Fung BJ, Sutlief E, Hussain Shuler MG. Dopamine and the interdependency of time perception and reward. Neurosci Biobehav Rev. 2021 Feb 27;125:380-391. doi: 10.1016/j.neubiorev.2021.02.030. PMID: 33652021.

• Monk KJ, Allard S, Hussain Shuler MG. Visual Cues Predictive of Behaviorally Neutral Outcomes Evoke Persistent but Not Interval Timing Activity in V1, Whereas Aversive Conditioning Suppresses This Activity. Front. Syst. Neurosci. 2021 March 5;15:18. doi: 10.3389/fnsys.2021.611744.

• Monk KJ, Allard S, Hussain Shuler MG. Reward Timing and Its Expression by Inhibitory Interneurons in the Mouse Primary Visual Cortex. Cereb Cortex. 2020 Jun 30;30(8):4662-4676. doi: 10.1093/cercor/bhaa068. PMID: 32202618; PMCID: PMC7325719. 

• Monk KJ, Hussain Shuler MG. Are You There, Cortex? It's Me, Acetylcholine. Neuron. 2019 Sep 25;103(6):954-956. doi: 10.1016/j.neuron.2019.08.039. PMID: 31557457. 

• Marton T, Samuels J, Nestadt P, Krasnow J, Wang Y, Shuler M, Kamath V, Chib VS, Bakker A, Nestadt G. Validating a dimension of doubt in decision-making: A proposed endophenotype for obsessive-compulsive disorder. PLoS One. 2019 Jun 13;14(6):e0218182. doi: 10.1371/journal.pone.0218182. PMID: 31194808; PMCID: PMC6564001.

• Shuler M, Namboodiri VMK. Time's weird in the brain-that's a good thing, and here's why. In Think Tank: Forty Neuroscientists Explore the Biological Roots of Human Experience. Yale University Press. 2018. p. 135-144 

• Levy JM, Zold CL, Namboodiri VMK, Hussain Shuler MG. The Timing of Reward-Seeking Action Tracks Visually Cued Theta Oscillations in Primary Visual Cortex. J Neurosci. 2017 Oct 25;37(43):10408-10420. doi: 10.1523/JNEUROSCI.0923-17.2017. Epub 2017 Sep 25. PMID: 28947572; PMCID: PMC5656994.

• Namboodiri VM, Hussain Shuler MG. The hunt for the perfect discounting function and a reckoning of time perception. Curr Opin Neurobiol. 2016 Oct;40:135-141. doi: 10.1016/j.conb.2016.06.019. Epub 2016 Jul 29. Review. PMID: 27479656; PMCID: PMC5056825.

• Namboodiri VM, Levy JM, Mihalas S, Sims DW, Hussain Shuler MG. Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework. Proc Natl Acad Sci U S A. 2016 Aug 2;113(31):8747-52. doi: 10.1073/pnas.1601664113. Epub 2016 Jul 6. PMID: 27385831; PMCID: PMC4978240.

• Shuler MG. Timing in the visual cortex and its investigation. Curr Opin Behav Sci. 2016 Apr;8:73-77. PMID: 26949724; PMCID: PMC4772165.

• Namboodiri VM, Mihalas S, Hussain Shuler MG. Analytical Calculation of Errors in Time and Value Perception Due to a Subjective Time Accumulator: A Mechanistic Model and the Generation of Weber's Law. Neural Comput. 2016 Jan;28(1):89-117. doi: 10.1162/NECO_a_00792. Epub 2015 Nov 24. PMID: 26599714.

• Marton TM, Hussain Shuler MG, Worley PF. Homer 1a and mGluR5 phosphorylation in reward-sensitive metaplasticity: A hypothesis of neuronal selection and bidirectional synaptic plasticity. Brain Res. 2015 Dec 2;1628(Pt A):17-28. doi: 10.1016/j.brainres.2015.06.037. Epub 2015 Jul 14. Review. PMID: 26187757.

• Lin SC, Brown RE, Hussain Shuler MG, Petersen CC, Kepecs A. Optogenetic Dissection of the Basal Forebrain Neuromodulatory Control of Cortical Activation, Plasticity, and Cognition. J Neurosci. 2015 Oct 14;35(41):13896-903. doi: 10.1523/JNEUROSCI.2590-15.2015. Review. PMID: 26468190; PMCID: PMC4604228.

• Huertas MA, Hussain Shuler MG, Shouval HZ. A Simple Network Architecture Accounts for Diverse Reward Time Responses in Primary Visual Cortex. J Neurosci. 2015 Sep 16;35(37):12659-72. doi: 10.1523/JNEUROSCI.0871-15.2015. PMID: 26377457; PMCID: PMC4571602.

• Zold CL, Hussain Shuler MG. Theta Oscillations in Visual Cortex Emerge with Experience to Convey Expected Reward Time and Experienced Reward Rate. J Neurosci. 2015 Jul 1;35(26):9603-14. doi: 10.1523/JNEUROSCI.0296-15.2015. PMID: 26134643; PMCID: PMC4571501.

• Liu CH, Coleman JE, Davoudi H, Zhang K, Hussain Shuler MG. Selective activation of a putative reinforcement signal conditions cued interval timing in primary visual cortex. Curr Biol. 2015 Jun 15;25(12):1551-61. doi: 10.1016/j.cub.2015.04.028. Epub 2015 May 21. PMID: 26004763; PMCID: PMC4470722.

• Namboodiri VM, Huertas MA, Monk KJ, Shouval HZ, Hussain Shuler MG. Visually cued action timing in the primary visual cortex. Neuron. 2015 Apr 8;86(1):319-30. doi: 10.1016/j.neuron.2015.02.043. Epub 2015 Mar 26. PubMed PMID: 25819611; PubMed Central PMCID: PMC4393368.

• Levy JM, Namboodiri VM, Hussain Shuler MG. Memory bias in the temporal bisection point. Front Integr Neurosci. 2015 Jul 7;9:44. doi: 10.3389/fnint.2015.00044. eCollection 2015. PubMed PMID: 26217198; PubMed Central PMCID: PMC4493391.

• Namboodiri VM, Hussain Shuler MG. Report of interval timing or action? Proc Natl Acad Sci U S A. 2014 Jun 3;111(22):E2239. doi: 10.1073/pnas.1404555111. Epub 2014 May 12. PubMed PMID: 24821818; PubMed Central PMCID: PMC4050533.

• Namboodiri VM, Mihalas S, Hussain Shuler MG. Rationalizing decision-making: understanding the cost and perception of time. Time & Time Perception Reviews. 2014; 1(4)

• Namboodiri VM, Mihalas S, Marton TM, Hussain Shuler MG. A general theory of intertemporal decision-making and the perception of time. Front Behav Neurosci. 2014 Feb 28;8:61. doi: 10.3389/fnbeh.2014.00061. eCollection 2014. PubMed PMID: 24616677; PubMed Central PMCID: PMC3937698.

• Shouval HZ, Hussain Shuler MG, Agarwal A, Gavornik JP. What does scalar timing tell us about neural dynamics? Front Hum Neurosci. 2014 Jun 19;8:438. doi: 10.3389/fnhum.2014.00438. eCollection 2014. PubMed PMID: 24994976; PubMed Central PMCID: PMC4063330.

• Namboodiri VM, Mihalas S, Hussain Shuler MG. A temporal basis for Weber's law in value perception.Front Integr Neurosci. 2014 Oct 14;8:79. doi: 10.3389/fnint.2014.00079. eCollection 2014. PubMed PMID: 25352791; PubMed Central PMCID: PMC4196632.

• Worley P, Shuler M. Solving the mystery of memory. Cerebrum. 2014 Feb 1;2014:2. eCollection 2014 Jan. PubMed PMID: 25009692; PubMed Central PMCID: PMC4087188.

• Chubykin AA, Roach EB, Bear MF, Shuler MG. A cholinergic mechanism for reward timing within primary visual cortex. Neuron. 2013 Feb 20;77(4):723-35. doi: 10.1016/j.neuron.2012.12.039. PubMed PMID: 23439124; PubMed Central PMCID: PMC3597441.

• Gavornik JP, Shuler MG, Loewenstein Y, Bear MF, Shouval HZ. Learning reward timing in cortex through reward dependent expression of synaptic plasticity. Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6826-31. doi: 10.1073/pnas.0901835106. Epub 2009 Apr 3. PubMed PMID: 19346478; PubMed Central PMCID: PMC2672535.

• Liu CH, Heynen AJ, Shuler MG, Bear MF. Cannabinoid receptor blockade reveals parallel plasticity mechanisms in different layers of mouse visual cortex. Neuron. 2008 May 8;58(3):340-5. doi: 10.1016/j.neuron.2008.02.020. PubMed PMID: 18466745.

• Whitlock JR, Heynen AJ, Shuler MG, Bear MF. Learning induces long-term potentiation in the hippocampus. Science. 2006 Aug 25;313(5790):1093-7. PubMed PMID: 16931756.

• Shuler MG, Bear MF. Reward timing in the primary visual cortex. Science. 2006 Mar 17;311(5767):1606-9. PubMed PMID: 16543459.

• Krupa DJ, Wiest MC, Shuler MG, Laubach M, Nicolelis MA. Layer-specific somatosensory cortical activation during active tactile discrimination. Science. 2004 Jun 25;304(5679):1989-92. PubMed PMID: 15218154.

• Shuler MG, Krimm RF, Hill DL. Neuron/target plasticity in the peripheral gustatory system. J Comp Neurol. 2004 Apr 26;472(2):183-92. PubMed PMID: 15048686; PubMed Central PMCID: PMC2799684.

• Shuler MG, Krupa DJ, Nicolelis MA. Integration of bilateral whisker stimuli in rats: role of the whisker barrel cortices. Cereb Cortex. 2002 Jan;12(1):86-97. PubMed PMID: 11734535.

• Shuler MG, Krupa DJ, Nicolelis MA. Bilateral integration of whisker information in the primary somatosensory cortex of rats. J Neurosci. 2001 Jul 15;21(14):5251-61. PubMed PMID: 11438600.

• Nicolelis MA, Shuler M. Thalamocortical and corticocortical interactions in the somatosensory system.Prog Brain Res. 2001;130:90-110. Review. PubMed PMID: 11480292.

• Laubach M, Shuler M, Nicolelis MA. Independent component analyses for quantifying neuronal ensemble interactions. J Neurosci Methods. 1999 Dec 15;94(1):141-54. PubMed PMID: 10638821.

Summaries of recent papers: findings and significance

(Sutlief, Zhang, Foresberg, Kaneko, and Hussain Shuler 2025)*
Reward-reset interval timing drives patch foraging decisions through neural state transitions in dorsomedial striatum [BioRXiv]
Summary: The dorsomedial striatum (DMS) encodes the timing of patch-leaving decisions during foraging through a reward-triggered, time-cost-sensitive accumulation of neural state switches, integrating recent reward timing, patch occupancy, and environmental context, with dopaminergic signals encoding reward prediction errors that reflect the declining reward probability over time.  This study investigates how the dorsomedial striatum (DMS) encodes the timing of patch-leaving decisions in a foraging task. Mice used a 'reward-reset' strategy, timing exits primarily by the interval since the last reward, but being modulated by patch occupancy time and environment’s reward-rate context. DMS neurons exhibited discrete, reward-triggered firing state transitions whose rate of accumulation predicted intended exit times. At the population level, these transitions accumulated linearly after each reward, reaching a threshold at patch exit, with the accumulation rate sensitive to both time-cost context and elapsed time in the patch. Dopaminergic signals in DMS encoded reward prediction errors relating to the reward-time distribution of the patch, scaling with reward uncertainty. These findings reveal a neural mechanism in DMS that integrates timing and value information to guide time investment decisions during foraging.

(Sutlief et al. 2024)
Summary: When analyzed from the framework of reward-rate maximization, temporal decisions about whether to begin a pursuit reveal that time’s cost contains both an opportunity and apportionment cost, leading to the insight that a hyperbolic discounting function and other ‘anomalies’ are in fact consistent with reward-rate maximization. By examining parameter mis-estimation that enables reward-rate maximization, we identify that systematic errors in temporal decision-making, observed in animals and humans arise from mis-weighting time spent outside versus inside a considered pursuit (‘Malapportionment Hypothesis’).  Overview: This theoretical paper develops a normative framework for temporal decision-making, focusing on when to initiate a pursuit to maximize reward rate. The authors derive equations showing that the cost of time includes both opportunity and apportionment costs, leading to a hyperbolic discounting function for optimal agents. They demonstrate that classic behavioral phenomena (hyperbolic discounting, Delay, Magnitude, and Sign effects) are consistent with optimal reward-rate maximization, challenging the view that these are signs of irrationality. By simulating parameter misestimations, they propose the 'Malapportionment Hypothesis': humans and animals tend to underweight time spent outside pursuits, explaining observed suboptimal impatience in choice tasks but near-optimality in forgo tasks. This insight refines our understanding of the sources of error in temporal decision-making and has implications for models of learning and neural representation.

(De Silva et al. 2023)
This paper introduces 'prospective learning,' a framework for machine learning that focuses on predicting and adapting to future, dynamically evolving data distributions, rather than assuming stationarity as in traditional (retrospective) learning. Through theoretical analysis and simulations, the authors show that standard learning algorithms—including continual, meta-, and online learning—fail to exploit predictable patterns in task evolution, leading to suboptimal performance when distributions change in structured ways. They formalize prospective learnability, distinguishing it from PAC-learnability, and argue that many real-world problems require this anticipatory approach. The work lays a theoretical foundation for developing algorithms that can model and anticipate future changes, potentially bridging gaps between artificial and natural intelligence.

(Allard and Hussain Shuler 2023)
This study demonstrates that in a mouse model of Alzheimer's disease (APP mice), cholinergic reinforcement signaling in the visual cortex is progressively impaired by amyloidosis before any loss of cholinergic synapses occurs. Using optical acetylcholine sensors, the authors show that APP mice develop deficits in ACh responses to unexpected rewards and punishments, while responses to neutral visual cues remain intact. Experience-dependent increases in ACh signaling to predictive cues and decreases to predicted outcomes, observed in wild-type mice during conditioning, are attenuated in APP mice as amyloidosis advances. Notably, a transient hyperactivity in spontaneous ACh release marks the onset of these deficits. These findings suggest that disrupted cholinergic signaling is an early functional biomarker of Alzheimer's pathology, preceding anatomical degeneration, and highlight the importance of early intervention.

(Fung, Sutlief, and Hussain Shuler 2021)
This review synthesizes evidence linking dopamine to both time perception and reward processing, highlighting their interdependence. Experimental manipulations—pharmacological, genetic, behavioral, and optogenetic—demonstrate that dopamine can modulate the speed of the internal clock, affecting how durations are perceived and produced. While the 'dopamine clock hypothesis' posits a monotonic relationship (more dopamine quickens the clock), recent findings, including optogenetic studies, reveal more nuanced and sometimes contradictory effects. Reward and motivational states also bias time perception, with effects varying by context, reward type, and individual differences. We concludes that dopamine influences timing at multiple stages, including clock speed, error correction, and action initiation, and that time perception and reward processing are deeply intertwined in the brain.

(Monk, Allard, and Hussain Shuler 2021)
Neurons in mouse primary visual cortex encode the interval between a visual cue and expected reward, inhibitory interneurons participate in this timing code, and the pattern of their participation accords with a theorized network architecture for generating reward-timing activity. This study investigates how different conditioning strategies affect cue-evoked persistent activity in mouse primary visual cortex (V1). The authors found that familiar visual cues alone (pseudo-conditioning) can evoke persistent neural activity in V1, similar to that seen with reward timing. Pairing visual cues with a neutral outcome increased response magnitude but did not induce interval timing activity. In contrast, pairing cues with an aversive outcome (tail shock) strongly suppressed both the prevalence and magnitude of persistent activity, and also failed to produce interval timing. Thus, while persistent activity in V1 does not require a temporally paired outcome, only rewarding outcomes appear to engender interval timing, and the behavioral relevance of outcomes differentially modulates V1 responses.

(Monk, Allard, and Hussain Shuler 2020)
This study investigates how the mouse primary visual cortex (V1) encodes the timing between a visual cue and a delayed reward. Using behavioral training, electrophysiology, and optogenetics, the authors show that V1 neurons—including identified PV+ and SOM+ interneurons—express reward timing activity in distinct forms. PV+ interneurons predominantly show sustained increases in activity, and neurons inhibited by PV+ cells are enriched for sustained decreases, supporting a computational model of local circuit architecture. These findings reveal that V1 reward timing is generated by specific inhibitory-excitatory interactions, deepening our understanding of how sensory cortex encodes learned temporal predictions.

(Marton et al. 2019)
This study introduces and validates the Doubt Questionnaire (DQ) as a reliable tool for measuring subjective doubt, a trait linked to decision-making difficulties in obsessive-compulsive disorder (OCD). The DQ demonstrated strong psychometric properties and effectively distinguished OCD patients from controls. Using a perceptual decision-making task (random dot motion), the study found that both OCD and high-doubt individuals accumulate evidence more slowly and report lower confidence in their decisions, particularly under conditions of low uncertainty. Drift diffusion modeling revealed that higher doubt scores are associated with slower evidence accumulation (lower drift rates), but not with changes in decision thresholds. These findings support the concept of doubt as a dimensional cognitive trait and propose it as a useful endophenotype for OCD, potentially aiding future research into the neurobiological and computational mechanisms underlying decision-making in OCD and related disorders.

(Levy et al. 2017)
Theta oscillations in the primary visual cortex (V1) of rats, evoked by reward-predicting visual cues, are closely linked to the precision and accuracy of timed reward-seeking actions. Using a visually cued timing task with V1 electrophysiological recordings, the authors show that trials with strong theta oscillations exhibit improved behavioral timing, and the duration of these oscillations predicts the timing of action execution. Single-unit activity is phase-locked to these oscillations and further predicts behavioral performance. The likelihood of evoking oscillatory states is modulated by experienced reward rate, linking V1 oscillations to motivational state. These findings extend the role of V1 beyond perception, implicating it in the timing and decision-making processes of visually guided behaviors.

(V. M. Namboodiri and Hussain Shuler 2016)
This review critiques existing models of intertemporal decision-making and time perception, highlighting their inability to fully explain experimental data. The authors introduce the TIMERR model, which posits that agents evaluate delayed rewards by integrating recent reward experiences, leading to hyperbolic discounting and context-dependent patience. TIMERR unifies decision-making and time perception by deriving a nonlinear, bounded subjective time representation that maximizes reward rate. The model accounts for diverse behavioral phenomena, including magnitude and sign effects, and predicts relationships between timing precision and discounting. A mechanistic drift-diffusion implementation (TIMERR-DDM) further explains timing errors and deviations from scalar timing. Overall, TIMERR provides a normative, biologically plausible framework that reconciles intertemporal choice and time perception.

(V. M. K. Namboodiri et al. 2016)
This paper challenges the prevailing view that animal and human foraging path lengths follow power law distributions due to random Lévy walks. The authors develop a decision-theoretic model incorporating temporal discounting, predicting that cognitively complex foragers will exhibit hyperbolic path length distributions, which closely approximate power laws. Laboratory experiments show that humans explore space systematically and adjust their search patterns in response to temporal costs, supporting the model. Analysis of wild marine animal movement data reveals that hyperbolic models fit observed path lengths better than power law or exponential models. The model is further refined to account for noisy time perception and competition, suggesting that apparent power law patterns in foraging arise from optimal learning strategies shaped by temporal discounting, rather than purely random search.

(Shuler 2016)
This paper reviews and synthesizes evidence that the primary visual cortex (V1) is not only involved in processing visual features but also plays a direct role in interval timing. Through a series of experiments, it is shown that V1 can learn and express visually-cued temporal intervals via cholinergic reinforcement, that this timing activity is locally generated, and that it predicts and causally informs the timing of visually-guided actions. V1 timing signals exhibit the temporal scalar property, and perturbing V1 during timing tasks lawfully shifts behavioral timing, establishing its causal role. These findings collectively argue that early sensory cortex is a substrate for learning and expressing temporal expectations that guide behavior.

(V. M. K. Namboodiri, Mihalas, and Hussain Shuler 2016)
This paper presents a mechanistic accumulator model for subjective time perception and intertemporal decision making, grounded in the TIMERR theory. The model analytically and numerically demonstrates that errors in time and value perception can obey Weber’s law (constant coefficient of variation) when feedback dominates and the past integration interval is large. Deviations from Weber’s law are predicted when these conditions are not met, and the model provides exact quantitative predictions for these cases. Simulations show that the model can reproduce scalar invariance in timing behavior and explain observed experimental deviations. The work also predicts how errors in time perception affect value-based decisions and highlights the influence of memory and read-out noise, predicting an optimal integration interval for maximal precision. These findings offer falsifiable predictions for future experiments and deepen the mechanistic understanding of timing and decision errors.