Publications by authors named "Maneesh Sahani"

50 Publications

Deep learning, reinforcement learning, and world models.

Neural Netw 2022 Aug 19;152:267-275. Epub 2022 Apr 19.

Advanced Telecommunication Research International (ATR), Japan; Kyoto University, Japan. Electronic address:

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neunet.2022.03.037DOI Listing
August 2022

Learning and attention increase visual response selectivity through distinct mechanisms.

Neuron 2022 02 13;110(4):686-697.e6. Epub 2021 Dec 13.

Biozentrum, University of Basel, Basel, Switzerland; Centre for Developmental Neurobiology, King's College London, London, UK. Electronic address:

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance and across seconds when animals switch attention. While both phenomena occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuron.2021.11.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860382PMC
February 2022

Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings.

Curr Opin Neurobiol 2021 10 24;70:163-170. Epub 2021 Nov 24.

Gatsby Computational Neuroscience Unit, University College London, London, UK. Electronic address:

The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory perception, decision making, and motor control. It is thought that such computations are implemented through the dynamical evolution of distributed activity in recurrent circuits. Thus, identifying dynamical structure in neural population activity is a key challenge towards a better understanding of neural computation. At the same time, interpreting this structure in light of the computation of interest is essential for linking the time-varying activity patterns of the neural population to ongoing computational processes. Here, we review methods that aim to quantify structure in neural population recordings through a dynamical system defined in a low-dimensional latent variable space. We discuss advantages and limitations of different modelling approaches and address future challenges for the field.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.conb.2021.10.014DOI Listing
October 2021

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface.

Nat Commun 2021 06 17;12(1):3689. Epub 2021 Jun 17.

Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.

Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-23884-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211867PMC
June 2021

Sustained Activation of PV+ Interneurons in Core Auditory Cortex Enables Robust Divisive Gain Control for Complex and Naturalistic Stimuli.

Cereb Cortex 2021 03;31(5):2364-2381

Department of Neuroscience, University of Oldenburg, 26126 Oldenburg, Germany.

Sensory cortices must flexibly adapt their operations to internal states and external requirements. Sustained modulation of activity levels in different inhibitory interneuron populations may provide network-level mechanisms for adjustment of sensory cortical processing on behaviorally relevant timescales. However, understanding of the computational roles of inhibitory interneuron modulation has mostly been restricted to effects at short timescales, through the use of phasic optogenetic activation and transient stimuli. Here, we investigated how modulation of inhibitory interneurons affects cortical computation on longer timescales, by using sustained, network-wide optogenetic activation of parvalbumin-positive interneurons (the largest class of cortical inhibitory interneurons) to study modulation of auditory cortical responses to prolonged and naturalistic as well as transient stimuli. We found highly conserved spectral and temporal tuning in auditory cortical neurons, despite a profound reduction in overall network activity. This reduction was predominantly divisive, and consistent across simple, complex, and naturalistic stimuli. A recurrent network model with power-law input-output functions replicated our results. We conclude that modulation of parvalbumin-positive interneurons on timescales typical of sustained neuromodulation may provide a means for robust divisive gain control conserving stimulus representations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/cercor/bhaa347DOI Listing
March 2021

Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons.

Elife 2020 07 6;9. Epub 2020 Jul 6.

Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London, United Kingdom.

Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and (M1) provide direct input to spinal circuitry and are critical for skilled movement control. Contrary to initial hypotheses, they can also be active during action observation, in the absence of any movement. A population-level understanding of this phenomenon is currently lacking. We recorded from single neurons, including identified PTNs, in (M1) (n = 187), and F5 (n = 115) as two adult male macaques executed, observed, or withheld (NoGo) reach-to-grasp actions. F5 maintained a similar representation of grasping actions during both execution and observation. In contrast, although many individual M1 neurons were active during observation, M1 population activity was distinct from execution, and more closely aligned to NoGo activity, suggesting this activity contributes to withholding of self-movement. M1 and its outputs may dissociate initiation of movement from representation of grasp in order to flexibly guide behaviour.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.54139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384858PMC
July 2020

Perceptual bias reveals slow-updating in autism and fast-forgetting in dyslexia.

Nat Neurosci 2019 02 14;22(2):256-264. Epub 2019 Jan 14.

Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel.

Individuals with autism and individuals with dyslexia both show reduced use of previous sensory information (stimuli statistics) in perceptual tasks, even though these are very different neurodevelopmental disorders. To better understand how past sensory information influences the perceptual experience in these disorders, we first investigated the trial-by-trial performance of neurotypical participants in a serial discrimination task. Neurotypical participants overweighted recent stimuli, revealing fast updating of internal sensory models, which is adaptive in changing environments. They also weighted the detailed stimuli distribution inferred by longer-term accumulation of stimuli statistics, which is adaptive in stable environments. Compared to neurotypical participants, individuals with dyslexia weighted earlier stimuli less heavily, whereas individuals with autism spectrum disorder weighted recent stimuli less heavily. Investigating the dynamics of perceptual inference reveals that individuals with dyslexia rely more on information about the immediate past, whereas perception in individuals with autism is dominated by longer-term statistics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41593-018-0308-9DOI Listing
February 2019

A Head-Mounted Camera System Integrates Detailed Behavioral Monitoring with Multichannel Electrophysiology in Freely Moving Mice.

Neuron 2018 10;100(1):46-60.e7

Ear Institute, UCL, London WC1X 8EE, UK; Department of Neuroscience, Physiology and Pharmacology, UCL, London WC1E 6BT, UK. Electronic address:

Breakthroughs in understanding the neural basis of natural behavior require neural recording and intervention to be paired with high-fidelity multimodal behavioral monitoring. An extensive genetic toolkit for neural circuit dissection, and well-developed neural recording technology, make the mouse a powerful model organism for systems neuroscience. However, most methods for high-bandwidth acquisition of behavioral data in mice rely upon fixed-position cameras and other off-animal devices, complicating the monitoring of animals freely engaged in natural behaviors. Here, we report the development of a lightweight head-mounted camera system combined with head-movement sensors to simultaneously monitor eye position, pupil dilation, whisking, and pinna movements along with head motion in unrestrained, freely behaving mice. The power of the combined technology is demonstrated by observations linking eye position to head orientation; whisking to non-tactile stimulation; and, in electrophysiological experiments, visual cortical activity to volitional head movements.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuron.2018.09.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195680PMC
October 2018

Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex.

Nat Neurosci 2018 06 21;21(6):851-859. Epub 2018 May 21.

Biozentrum, University of Basel, Basel, Switzerland.

How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR-PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR-SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41593-018-0143-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6390950PMC
June 2018

The Impact of Anesthetic State on Spike-Sorting Success in the Cortex: A Comparison of Ketamine and Urethane Anesthesia.

Front Neural Circuits 2017 29;11:95. Epub 2017 Nov 29.

Ear Institute, University College London, London, United Kingdom.

Spike sorting is an essential first step in most analyses of extracellular electrophysiological recordings. Here we show that spike-sorting success depends critically on characteristics of coordinated population activity that can differ between anesthetic states. In tetrode recordings from mouse auditory cortex, spike sorting was significantly less successful under ketamine/medetomidine (ket/med) than urethane anesthesia. Surprisingly, this difficulty with sorting under ket/med anesthesia did not appear to result from either greater millisecond-scale burstiness of neural activity or increased coordination of activity among neighboring neurons. Rather, the key factor affecting sorting success appeared to be the amount of coordinated population activity at long time intervals and across large cortical distances. We propose that spike-sorting success is directly dependent on overall coordination of activity, and is most disrupted by large-scale fluctuations in cortical population activity. Reliability of single-unit recording may therefore differ not only between urethane-anesthetized and ket/med-anesthetized states as demonstrated here, but also between synchronized and desynchronized states, asleep and awake states, or inattentive and attentive states in unanesthetized animals.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fncir.2017.00095DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712555PMC
August 2018

Prior context in audition informs binding and shapes simple features.

Nat Commun 2017 04 20;8:15027. Epub 2017 Apr 20.

Laboratoire des Systèmes Perceptifs, CNRS UMR 8248, Paris 75005, France.

A perceptual phenomenon is reported, whereby prior acoustic context has a large, rapid and long-lasting effect on a basic auditory judgement. Pairs of tones were devised to include ambiguous transitions between frequency components, such that listeners were equally likely to report an upward or downward 'pitch' shift between tones. We show that presenting context tones before the ambiguous pair almost fully determines the perceived direction of shift. The context effect generalizes to a wide range of temporal and spectral scales, encompassing the characteristics of most realistic auditory scenes. Magnetoencephalographic recordings show that a relative reduction in neural responsivity is correlated to the behavioural effect. Finally, a computational model reproduces behavioural results, by implementing a simple constraint of continuity for binding successive sounds in a probabilistic manner. Contextual processing, mediated by ubiquitous neural mechanisms such as adaptation, may be crucial to track complex sound sources over time.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/ncomms15027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411480PMC
April 2017

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation.

Front Syst Neurosci 2016 12;10:109. Epub 2017 Jan 12.

Gatsby Computational Neuroscience Unit, University College London London, UK.

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far-ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or "priors." In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fnsys.2016.00109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226961PMC
January 2017

Inhibitory control of correlated intrinsic variability in cortical networks.

Elife 2016 12 7;5. Epub 2016 Dec 7.

Ear Institute, University College London, London, United Kingdom.

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.19695DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142814PMC
December 2016

The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces.

Exp Neurol 2017 01 7;287(Pt 4):437-451. Epub 2016 Aug 7.

Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, United States; Deparment of Neurobiology, Stanford University, Stanford, CA 94305, United States. Electronic address:

A central goal of neuroscience is to understand how populations of neurons coordinate and cooperate in order to give rise to perception, cognition, and action. Nonhuman primates (NHPs) are an attractive model with which to understand these mechanisms in humans, primarily due to the strong homology of their brains and the cognitively sophisticated behaviors they can be trained to perform. Using electrode recordings, the activity of one to a few hundred individual neurons may be measured electrically, which has enabled many scientific findings and the development of brain-machine interfaces. Despite these successes, electrophysiology samples sparsely from neural populations and provides little information about the genetic identity and spatial micro-organization of recorded neurons. These limitations have spurred the development of all-optical methods for neural circuit interrogation. Fluorescent calcium signals serve as a reporter of neuronal responses, and when combined with post-mortem optical clearing techniques such as CLARITY, provide dense recordings of neuronal populations, spatially organized and annotated with genetic and anatomical information. Here, we advocate that this methodology, which has been of tremendous utility in smaller animal models, can and should be developed for use with NHPs. We review here several of the key opportunities and challenges for calcium-based optical imaging in NHPs. We focus on motor neuroscience and brain-machine interface design as representative domains of opportunity within the larger field of NHP neuroscience.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.expneurol.2016.08.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5154795PMC
January 2017

Subliminal stimulation and somatosensory signal detection.

Acta Psychol (Amst) 2016 Oct 4;170:103-11. Epub 2016 Jul 4.

Institute of Cognitive Neuroscience, University College London, Alexandra House, 17 Queen Square, London WC1N 3AR, UK. Electronic address:

Only a small fraction of sensory signals is consciously perceived. The brain's perceptual systems may include mechanisms of feedforward inhibition that protect the cortex from subliminal noise, thus reserving cortical capacity and conscious awareness for significant stimuli. Here we provide a new view of these mechanisms based on signal detection theory, and gain control. We demonstrated that subliminal somatosensory stimulation decreased sensitivity for the detection of a subsequent somatosensory input, largely due to increased false alarm rates. By delivering the subliminal somatosensory stimulus and the to-be-detected somatosensory stimulus to different digits of the same hand, we show that this effect spreads across the sensory surface. In addition, subliminal somatosensory stimulation tended to produce an increased probability of responding "yes", whether the somatosensory stimulus was present or not. Our results suggest that subliminal stimuli temporarily reduce input gain, avoiding excessive responses to further small inputs. This gain control may be automatic, and may precede discriminative classification of inputs into signals or noise. Crucially, we found that subliminal inputs influenced false alarm rates only on blocks where the to-be-detected stimuli were present, and not on pre-test control blocks where they were absent. Participants appeared to adjust their perceptual criterion according to a statistical distribution of stimuli in the current context, with the presence of supraliminal stimuli having an important role in the criterion-setting process. These findings clarify the cognitive mechanisms that reserve conscious perception for salient and important signals.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.actpsy.2016.06.009DOI Listing
October 2016

Input-Specific Gain Modulation by Local Sensory Context Shapes Cortical and Thalamic Responses to Complex Sounds.

Neuron 2016 07 23;91(2):467-81. Epub 2016 Jun 23.

Gatsby Computational Neuroscience Unit, University College London, London W1T 4JG, UK. Electronic address:

Sensory neurons are customarily characterized by one or more linearly weighted receptive fields describing sensitivity in sensory space and time. We show that in auditory cortical and thalamic neurons, the weight of each receptive field element depends on the pattern of sound falling within a local neighborhood surrounding it in time and frequency. Accounting for this change in effective receptive field with spectrotemporal context improves predictions of both cortical and thalamic responses to stationary complex sounds. Although context dependence varies among neurons and across brain areas, there are strong shared qualitative characteristics. In a spectrotemporally rich soundscape, sound elements modulate neuronal responsiveness more effectively when they coincide with sounds at other frequencies, and less effectively when they are preceded by sounds at similar frequencies. This local-context-driven lability in the representation of complex sounds-a modulation of "input-specific gain" rather than "output gain"-may be a widespread motif in sensory processing.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuron.2016.05.041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4961224PMC
July 2016

Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex.

Neuron 2015 Jun 4;86(6):1478-90. Epub 2015 Jun 4.

Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland; University College London, 21 University Street, London WC1E 6DE, UK. Electronic address:

We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuron.2015.05.037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503798PMC
June 2015

Five key factors determining pairwise correlations in visual cortex.

J Neurophysiol 2015 Aug 27;114(2):1022-33. Epub 2015 May 27.

Institute of Ophthalmology, University College London, London, United Kingdom.

The responses of cortical neurons to repeated presentation of a stimulus are highly variable, yet correlated. These "noise correlations" reflect a low-dimensional structure of population dynamics. Here, we examine noise correlations in 22,705 pairs of neurons in primary visual cortex (V1) of anesthetized cats, during ongoing activity and in response to artificial and natural visual stimuli. We measured how noise correlations depend on 11 factors. Because these factors are themselves not independent, we distinguished their influences using a nonlinear additive model. The model revealed that five key factors play a predominant role in determining pairwise correlations. Two of these are distance in cortex and difference in sensory tuning: these are known to decrease correlation. A third factor is firing rate: confirming most earlier observations, it markedly increased pairwise correlations. A fourth factor is spike width: cells with a broad spike were more strongly correlated amongst each other. A fifth factor is spike isolation: neurons with worse isolation were more correlated, even if they were recorded on different electrodes. For pairs of neurons with poor isolation, this last factor was the main determinant of correlations. These results were generally independent of stimulus type and timescale of analysis, but there were exceptions. For instance, pairwise correlations depended on difference in orientation tuning more during responses to gratings than to natural stimuli. These results consolidate disjoint observations in a vast literature on pairwise correlations and point towards regularities of population coding in sensory cortex.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00094.2015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4725109PMC
August 2015

The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction.

PLoS Comput Biol 2015 Apr 1;11(4):e1004141. Epub 2015 Apr 1.

Princeton Neuroscience Institute, Department of Psychology, Princeton University, Princeton, New Jersey, USA.

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1004141DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382343PMC
April 2015

State-dependent population coding in primary auditory cortex.

J Neurosci 2015 Feb;35(5):2058-73

Ear Institute, University College London, London WC1E 6BT, United Kingdom, and

Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and desynchronized cortical states, i.e., in the presence or absence of intrinsically generated up and down states. Here we investigated the impact of cortical state on the population coding of tones and speech in the primary auditory cortex (A1) of gerbils, and found that responses were qualitatively different in synchronized and desynchronized cortical states. Activity in synchronized A1 was only weakly modulated by sensory input, and the spike patterns evoked by tones and speech were unreliable and constrained to a small range of patterns. In contrast, responses to tones and speech in desynchronized A1 were temporally precise and reliable across trials, and different speech tokens evoked diverse spike patterns with extremely weak noise correlations, allowing responses to be decoded with nearly perfect accuracy. Restricting the analysis of synchronized A1 to activity within up states yielded similar results, suggesting that up states are not equivalent to brief periods of desynchronization. These findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.3318-14.2015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315834PMC
February 2015

Cortical control of arm movements: a dynamical systems perspective.

Annu Rev Neurosci 2013 Jul 29;36:337-59. Epub 2013 May 29.

Department of Electrical Engineering, Stanford Institute for Neuro-Innovation and TranslationalNeuroscience, Stanford University, Stanford, CA 94305, USA.

Our ability to move is central to everyday life. Investigating the neural control of movement in general, and the cortical control of volitional arm movements in particular, has been a major research focus in recent decades. Studies have involved primarily either attempts to account for single-neuron responses in terms of tuning for movement parameters or attempts to decode movement parameters from populations of tuned neurons. Even though this focus on encoding and decoding has led to many seminal advances, it has not produced an agreed-upon conceptual framework. Interest in understanding the underlying neural dynamics has recently increased, leading to questions such as how does the current population response determine the future population response, and to what purpose? We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1146/annurev-neuro-062111-150509DOI Listing
July 2013

Outlier responses reflect sensitivity to statistical structure in the human brain.

PLoS Comput Biol 2013 28;9(3):e1002999. Epub 2013 Mar 28.

University College London, Wellcome Trust Centre for Neuroimaging, London, United Kingdom.

We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1002999DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3610625PMC
November 2013

Attention in a bayesian framework.

Front Hum Neurosci 2012 14;6:100. Epub 2012 Jun 14.

Gatsby Computational Neuroscience Unit, University College London London, UK.

The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of perception, and use this observation to frame a new computational account of the need for, and action of, attention - unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental settings, where cues shape expectations about a small number of upcoming stimuli and thus convey "prior" information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its selective and integrative roles, and thus cannot be easily extended to complex environments. We suggest that the resource bottleneck stems from the computational intractability of exact perceptual inference in complex settings, and that attention reflects an evolved mechanism for approximate inference which can be shaped to refine the local accuracy of perception. We show that this approach extends the simple picture of attention as prior, so as to provide a unified and computationally driven account of both selective and integrative attentional phenomena.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fnhum.2012.00100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3375068PMC
October 2012

Learning stable, regularised latent models of neural population dynamics.

Network 2012 ;23(1-2):24-47

Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London, WC1N 3AR, UK.

Ongoing advances in experimental technique are making commonplace simultaneous recordings of the activity of tens to hundreds of cortical neurons at high temporal resolution. Latent population models, including Gaussian-process factor analysis and hidden linear dynamical system (LDS) models, have proven effective at capturing the statistical structure of such data sets. They can be estimated efficiently, yield useful visualisations of population activity, and are also integral building-blocks of decoding algorithms for brain-machine interfaces (BMI). One practical challenge, particularly to LDS models, is that when parameters are learned using realistic volumes of data the resulting models often fail to reflect the true temporal continuity of the dynamics; and indeed may describe a biologically-implausible unstable population dynamic that is, it may predict neural activity that grows without bound. We propose a method for learning LDS models based on expectation maximisation that constrains parameters to yield stable systems and at the same time promotes capture of temporal structure by appropriate regularisation. We show that when only little training data is available our method yields LDS parameter estimates which provide a substantially better statistical description of the data than alternatives, whilst guaranteeing stable dynamics. We demonstrate our methods using both synthetic data and extracellular multi-electrode recordings from motor cortex.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3109/0954898X.2012.677095DOI Listing
October 2012

Functional evidence for a dual route to amygdala.

Curr Biol 2012 Jan 29;22(2):129-34. Epub 2011 Dec 29.

Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK.

The amygdala plays a central role in evaluating the behavioral importance of sensory information. Anatomical subcortical pathways provide direct input to the amygdala from early sensory systems and may support an adaptively valuable rapid appraisal of salient information. However, the functional significance of these subcortical inputs remains controversial. We recorded magnetoencephalographic activity evoked by tones in the context of emotionally valent faces and tested two competing biologically motivated dynamic causal models against these data: the dual and cortical models. The dual model comprised two parallel (cortical and subcortical) routes to the amygdala, whereas the cortical model excluded the subcortical path. We found that neuronal responses elicited by salient information were better explained when a subcortical pathway was included. In keeping with its putative functional role of rapid stimulus appraisal, the subcortical pathway was most important early in stimulus processing. However, as often assumed, its action was not limited to the context of fear, pointing to a more widespread information processing role. Thus, our data supports the idea that an expedited evaluation of sensory input is best explained by an architecture that involves a subcortical path to the amygdala.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cub.2011.11.056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3267035PMC
January 2012

Depth-dependent temporal response properties in core auditory cortex.

J Neurosci 2011 Sep;31(36):12837-48

UCL Ear Institute, University College London, London WC1X 8EE, UK.

The computational role of cortical layers within auditory cortex has proven difficult to establish. One hypothesis is that interlaminar cortical processing might be dedicated to analyzing temporal properties of sounds; if so, then there should be systematic depth-dependent changes in cortical sensitivity to the temporal context in which a stimulus occurs. We recorded neural responses simultaneously across cortical depth in primary auditory cortex and anterior auditory field of CBA/Ca mice, and found systematic depth dependencies in responses to second-and-later noise bursts in slow (1-10 bursts/s) trains of noise bursts. At all depths, responses to noise bursts within a train usually decreased with increasing train rate; however, the rolloff with increasing train rate occurred at faster rates in more superficial layers. Moreover, in some recordings from mid-to-superficial layers, responses to noise bursts within a 3-4 bursts/s train were stronger than responses to noise bursts in slower trains. This non-monotonicity with train rate was especially pronounced in more superficial layers of the anterior auditory field, where responses to noise bursts within the context of a slow train were sometimes even stronger than responses to the noise burst at train onset. These findings may reflect depth dependence in suppression and recovery of cortical activity following a stimulus, which we suggest could arise from laminar differences in synaptic depression at feedforward and recurrent synapses.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.2863-11.2011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673482PMC
September 2011

Single-trial neural correlates of arm movement preparation.

Neuron 2011 Aug;71(3):555-64

Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.

The process by which neural circuitry in the brain plans and executes movements is not well understood. Until recently, most available data were limited either to single-neuron electrophysiological recordings or to measures of aggregate field or metabolism. Neither approach reveals how individual neurons' activities are coordinated within the population, and thus inferences about how the neural circuit forms a motor plan for an upcoming movement have been indirect. Here we build on recent advances in the measurement and description of population activity to frame and test an "initial condition hypothesis" of arm movement preparation and initiation. This hypothesis leads to a model in which the timing of movements may be predicted on each trial using neurons' moment-by-moment firing rates and rates of change of those rates. Using simultaneous microelectrode array recordings from premotor cortex of monkeys performing delayed-reach movements, we compare such single-trial predictions to those of other theories. We show that our model can explain approximately 4-fold more arm-movement reaction-time variance than the best alternative method. Thus, the initial condition hypothesis elucidates a view of the relationship between single-trial preparatory neural population dynamics and single-trial behavior.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuron.2011.05.047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155684PMC
August 2011

A dynamical systems view of motor preparation: implications for neural prosthetic system design.

Prog Brain Res 2011 ;192:33-58

Department of Electrical Engineering, Stanford University, Stanford, California, USA.

Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/B978-0-444-53355-5.00003-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665515PMC
November 2011

Observers exploit stochastic models of sensory change to help judge the passage of time.

Curr Biol 2011 Feb 20;21(3):200-6. Epub 2011 Jan 20.

Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, UK.

Sensory stimulation can systematically bias the perceived passage of time, but why and how this happens is mysterious. In this report, we provide evidence that such biases may ultimately derive from an innate and adaptive use of stochastically evolving dynamic stimuli to help refine estimates derived from internal timekeeping mechanisms. A simplified statistical model based on probabilistic expectations of stimulus change derived from the second-order temporal statistics of the natural environment makes three predictions. First, random noise-like stimuli whose statistics violate natural expectations should induce timing bias. Second, a previously unexplored obverse of this effect is that similar noise stimuli with natural statistics should reduce the variability of timing estimates. Finally, this reduction in variability should scale with the interval being timed, so as to preserve the overall Weber law of interval timing. All three predictions are borne out experimentally. Thus, in the context of our novel theoretical framework, these results suggest that observers routinely rely on sensory input to augment their sense of the passage of time, through a process of Bayesian inference based on expectations of change in the natural environment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cub.2010.12.043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3094759PMC
February 2011
-->