1
|
Zhou J, Gong L, Huang X, Mu C, Mi Y. The synaptic correlates of serial position effects in sequential working memory. Front Comput Neurosci 2024; 18:1430244. [PMID: 39077153 PMCID: PMC11284078 DOI: 10.3389/fncom.2024.1430244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 06/26/2024] [Indexed: 07/31/2024] Open
Abstract
Sequential working memory (SWM), referring to the temporary storage and manipulation of information in order, plays a fundamental role in brain cognitive functions. The serial position effect refers to the phenomena that recall accuracy of an item is associated to the order of the item being presented. The neural mechanism underpinning the serial position effect remains unclear. The synaptic mechanism of working memory proposes that information is stored as hidden states in the form of facilitated neuronal synapse connections. Here, we build a continuous attractor neural network with synaptic short-term plasticity (STP) to explore the neural mechanism of the serial position effect. Using a delay recall task, our model reproduces the the experimental finding that as the maintenance period extends, the serial position effect transitions from the primacy to the recency effect. Using both numerical simulation and theoretical analysis, we show that the transition moment is determined by the parameters of STP and the interval between presented stimulus items. Our results highlight the pivotal role of STP in processing the order information in SWM.
Collapse
Affiliation(s)
- Jiaqi Zhou
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Liping Gong
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
| | - Xiaodong Huang
- Department of Physics, South China University of Technology, Guangzhou, China
| | - Chunlai Mu
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
| | - Yuanyuan Mi
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China
| |
Collapse
|
2
|
Jones B, Snyder L, Ching S. Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention. Neural Comput 2024; 36:1022-1040. [PMID: 38658026 PMCID: PMC11045047 DOI: 10.1162/neco_a_01655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/10/2024] [Indexed: 04/26/2024]
Abstract
A key question in the neuroscience of memory encoding pertains to the mechanisms by which afferent stimuli are allocated within memory networks. This issue is especially pronounced in the domain of working memory, where capacity is finite. Presumably the brain must embed some "policy" by which to allocate these mnemonic resources in an online manner in order to maximally represent and store afferent information for as long as possible and without interference from subsequent stimuli. Here, we engage this question through a top-down theoretical modeling framework. We formally optimize a gating mechanism that projects afferent stimuli onto a finite number of memory slots within a recurrent network architecture. In the absence of external input, the activity in each slot attenuates over time (i.e., a process of gradual forgetting). It turns out that the optimal gating policy consists of a direct projection from sensory activity to memory slots, alongside an activity-dependent lateral inhibition. Interestingly, allocating resources myopically (greedily with respect to the current stimulus) leads to efficient utilization of slots over time. In other words, later-arriving stimuli are distributed across slots in such a way that the network state is minimally shifted and so prior signals are minimally "overwritten." Further, networks with heterogeneity in the timescales of their forgetting rates retain stimuli better than those that are more homogeneous. Our results suggest how online, recurrent networks working on temporally localized objectives without high-level supervision can nonetheless implement efficient allocation of memory resources over time.
Collapse
Affiliation(s)
- BethAnna Jones
- Department of Electrical and Systems Science, Washington University in St. Louis, St. Louis, MO 63130, U.S.A.
| | - Lawrence Snyder
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO 63130, U.S.A.
| | - ShiNung Ching
- Department of Electrical and Systems Science, Washington University in St. Louis, St. Louis, MO 63130, U.S.A.
| |
Collapse
|
3
|
Lee H, Choi W, Lee D, Paik SB. Comparison of visual quantities in untrained neural networks. Cell Rep 2023; 42:112900. [PMID: 37516959 DOI: 10.1016/j.celrep.2023.112900] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 05/25/2023] [Accepted: 07/13/2023] [Indexed: 08/01/2023] Open
Abstract
The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains.
Collapse
Affiliation(s)
- Hyeonsu Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Woochul Choi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Dongil Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Se-Bum Paik
- Department of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
| |
Collapse
|
4
|
Bocincova A, Buschman TJ, Stokes MG, Manohar SG. Neural signature of flexible coding in prefrontal cortex. Proc Natl Acad Sci U S A 2022; 119:e2200400119. [PMID: 36161948 PMCID: PMC9546590 DOI: 10.1073/pnas.2200400119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
The ability of prefrontal cortex to quickly encode novel associations is crucial for adaptive behavior and central to working memory. Fast Hebbian changes in synaptic strength permit forming new associations, but neuronal signatures of this have been elusive. We devised a trialwise index of pattern similarity to look for rapid changes in population codes. Based on a computational model of working memory, we hypothesized that synaptic strength-and consequently, the tuning of neurons-could change if features of a subsequent stimulus need to be "reassociated," i.e., if bindings between features need to be broken to encode the new item. As a result, identical stimuli might elicit different neural responses. As predicted, neural response similarity dropped following rebinding, but only in prefrontal cortex. The history-dependent changes were expressed on top of traditional, fixed selectivity and were not explainable by carryover of previous firing into the current trial or by neural adaptation.
Collapse
Affiliation(s)
- Andrea Bocincova
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Timothy J. Buschman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540
- Department of Psychology, Princeton University, Princeton, NJ 08540
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Sanjay G. Manohar
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
| |
Collapse
|
5
|
Abstract
Value signals in the brain are important for learning, decision-making, and orienting behavior toward relevant goals. Although they can play different roles in behavior and cognition, value representations are often considered to be uniform and static signals. Nonetheless, contextual and mixed representations of value have been widely reported. Here, we review the evidence for heterogeneity in value coding and dynamics in the orbitofrontal cortex. We argue that this diversity plays a key role in the representation of value itself and allows neurons to integrate value with other behaviorally relevant information. We also discuss modeling approaches that can dissociate potential functions of heterogeneous value codes and provide further insight into its importance in behavior and cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Collapse
Affiliation(s)
- Pierre Enel
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Aster Q. Perkins
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erin L. Rich
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| |
Collapse
|
6
|
Jang J, Song M, Paik SB. Retino-Cortical Mapping Ratio Predicts Columnar and Salt-and-Pepper Organization in Mammalian Visual Cortex. Cell Rep 2021; 30:3270-3279.e3. [PMID: 32160536 DOI: 10.1016/j.celrep.2020.02.038] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 12/27/2019] [Accepted: 02/07/2020] [Indexed: 12/22/2022] Open
Abstract
In the mammalian primary visual cortex, neural tuning to stimulus orientation is organized in either columnar or salt-and-pepper patterns across species. For decades, this sharp contrast has spawned fundamental questions about the origin of functional architectures in visual cortex. However, it is unknown whether these patterns reflect disparate developmental mechanisms across mammalian taxa or simply originate from variation of biological parameters under a universal development process. In this work, after the analysis of data from eight mammalian species, we show that cortical organization is predictable by a single factor, the retino-cortical mapping ratio. Groups of species with or without columnar clustering are distinguished by the feedforward sampling ratio, and model simulations with controlled mapping conditions reproduce both types of organization. Prediction from the Nyquist theorem explains this parametric division of the patterns with high accuracy. Our results imply that evolutionary variation of physical parameters may induce development of distinct functional circuitry.
Collapse
Affiliation(s)
- Jaeson Jang
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Min Song
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Se-Bum Paik
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
| |
Collapse
|
7
|
Kim G, Jang J, Baek S, Song M, Paik SB. Visual number sense in untrained deep neural networks. SCIENCE ADVANCES 2021; 7:7/1/eabd6127. [PMID: 33523851 PMCID: PMC7775775 DOI: 10.1126/sciadv.abd6127] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 11/03/2020] [Indexed: 05/25/2023]
Abstract
Number sense, the ability to estimate numerosity, is observed in naïve animals, but how this cognitive function emerges in the brain remains unclear. Here, using an artificial deep neural network that models the ventral visual stream of the brain, we show that number-selective neurons can arise spontaneously, even in the complete absence of learning. We also show that the responses of these neurons can induce the abstract number sense, the ability to discriminate numerosity independent of low-level visual cues. We found number tuning in a randomly initialized network originating from a combination of monotonically decreasing and increasing neuronal activities, which emerges spontaneously from the statistical properties of bottom-up projections. We confirmed that the responses of these number-selective neurons show the single- and multineuron characteristics observed in the brain and enable the network to perform number comparison tasks. These findings provide insight into the origin of innate cognitive functions.
Collapse
Affiliation(s)
- Gwangsu Kim
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Jaeson Jang
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Seungdae Baek
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Min Song
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Se-Bum Paik
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| |
Collapse
|
8
|
Kim J, Song M, Jang J, Paik SB. Spontaneous Retinal Waves Can Generate Long-Range Horizontal Connectivity in Visual Cortex. J Neurosci 2020; 40:6584-6599. [PMID: 32680939 PMCID: PMC7486661 DOI: 10.1523/jneurosci.0649-20.2020] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 06/02/2020] [Accepted: 06/26/2020] [Indexed: 12/27/2022] Open
Abstract
In the primary visual cortex (V1) of higher mammals, long-range horizontal connections (LHCs) are observed to develop, linking iso-orientation domains of cortical tuning. It is unknown how this feature-specific wiring of circuitry develops before eye-opening. Here, we suggest that LHCs in V1 may originate from spatiotemporally structured feedforward activities generated from spontaneous retinal waves. Using model simulations based on the anatomy and observed activity patterns of the retina, we show that waves propagating in retinal mosaics can initialize the wiring of LHCs by coactivating neurons of similar tuning, whereas equivalent random activities cannot induce such organizations. Simulations showed that emerged LHCs can produce the patterned activities observed in V1, matching the topography of the underlying orientation map. The model can also reproduce feature-specific microcircuits in the salt-and-pepper organizations found in rodents. Our results imply that early peripheral activities contribute significantly to cortical development of functional circuits.SIGNIFICANCE STATEMENT Long-range horizontal connections (LHCs) in the primary visual cortex (V1) are observed to emerge before the onset of visual experience, thereby selectively connecting iso-domains of orientation map. However, it is unknown how such feature-specific wirings develop before eye-opening. Here, we show that LHCs in V1 may originate from the feature-specific activation of cortical neurons by spontaneous retinal waves during early developmental stages. Our simulations of a visual cortex model show that feedforward activities from the retina initialize the spatial organization of activity patterns in V1, which induces visual feature-specific wirings in the V1 neurons. Our model also explains the origin of cortical microcircuits observed in rodents, suggesting that the proposed developmental mechanism is universally applicable to circuits of various mammalian species.
Collapse
Affiliation(s)
| | - Min Song
- Department of Bio and Brain Engineering
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | | | - Se-Bum Paik
- Department of Bio and Brain Engineering
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| |
Collapse
|
9
|
Maes A, Barahona M, Clopath C. Learning spatiotemporal signals using a recurrent spiking network that discretizes time. PLoS Comput Biol 2020; 16:e1007606. [PMID: 31961853 PMCID: PMC7028299 DOI: 10.1371/journal.pcbi.1007606] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 02/18/2020] [Accepted: 12/13/2019] [Indexed: 12/15/2022] Open
Abstract
Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.
Collapse
Affiliation(s)
- Amadeus Maes
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, United Kingdom
| |
Collapse
|