Learning and recognition of tactile temporal sequences by mice and humans

Bale, Michael R.; Bitzidou, Malamati; Pitas, Anna; Brebner, Leonie S.; Khazim, Lina; Anagnou, Stavros; Stevenson, Caitlin; Maravall, Miguel

doi:10.7554/elife.27333

Cited by 16 publications

(14 citation statements)

References 66 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Building on our earlier finding that mice learn to recognize tactile sequences constructed as a concatenation of noise segments, 16 we used a simple sequence design in which each stimulus consisted of a tactile ''word'' (Figure 1B). Each segment within the word comprised either filtered noise (with different amplitudes) or sinusoidal stimulation.…”

Section: Discrimination Of Elementary Tactile Sequences In Micementioning

confidence: 99%

“…Analyses were conducted in MATLAB and R. We quantified behavioral performance as in, 16 using the percent correct metric (hits + CRs)/(number of trials in sliding window) determined over a 50-trial sliding window during the course of a session and corrected for the proportions of GO and NOGO trials. To obtain an upper bound on average decision times in a session, we determined when, on average, the lick rates for GO and NOGO trials began to diverge during the course of a trial (discriminative lick latency, DLL).…”

Section: Behavioral Analysismentioning

confidence: 99%

“…Mice can discriminate between sequential patterns of whisker vibration, reaching performance levels comparable to humans using their fingertips. 16 In this modality, sensory information from the whiskers first reaches the cortex through the ''barrel field'' of the primary somatosensory cortex (S1bf), the main cortical target for direct somatosensory input from the thalamus. 17 Neurons in S1bf encode well-defined stimulus features, but in naive animals, they do not integrate sensory information over time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex

et al. 2021

Self Cite

View full text Add to dashboard Cite

Sequential temporal ordering and patterning are key features of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. To explore how cortical neuronal activity underpins sequence discrimination, we developed a task in which mice distinguished between tactile ''word'' sequences constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Mice could respond to the earliest possible cues allowing discrimination, effectively solving the task as a ''detection of change'' problem, but enhanced their performance when responding later. Optogenetic inactivation showed that the somatosensory cortex was necessary for sequence discrimination. Two-photon imaging in layer 2/3 of the primary somatosensory ''barrel'' cortex (S1bf) revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal's action decision and the trial outcome (presence or absence of the predicted reward). Many neurons were activated preceding goal-directed licking, thus reflecting the animal's learned action in response to the target sequence; these neurons were found as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory response tuning: neurons responding to stimulus features were found in naive mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf, sequence learning results in neurons whose activity reflects the learned association between target sequence and licking rather than a refined representation of sensory features.

show abstract

Section: Discrimination Of Elementary Tactile Sequences In Micementioning

confidence: 99%

Section: Behavioral Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Memory is a crucial component of sensory perception, on multiple processing levels ( Bale et al, 2017 ; Muckli and Petro, 2017 ). In the auditory modality, the ability to identify essentially any sound source, from footsteps to musical melody, requires the capacity to hold consecutive events in memory so as to link past and incoming information into a coherent emerging representation ( Koelsch et al, 2019 ; McDermott et al, 2013 ; Winkler et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Long-term implicit memory for sequential auditory patterns in humans

Bianco

Harrison

et al. 2020

eLife

View full text Add to dashboard Cite

Memory, on multiple timescales, is critical to our ability to discover the structure of our surroundings, and efficiently interact with the environment. We combined behavioural manipulation and modelling to investigate the dynamics of memory formation for rarely reoccurring acoustic patterns. In a series of experiments, participants detected the emergence of regularly repeating patterns within rapid tone-pip sequences. Unbeknownst to them, a few patterns reoccurred every ~3 min. All sequences consisted of the same 20 frequencies and were distinguishable only by the order of tone-pips. Despite this, reoccurring patterns were associated with a rapidly growing detection-time advantage over novel patterns. This effect was implicit, robust to interference, and persisted for 7 weeks. The results implicate an interplay between short (a few seconds) and long-term (over many minutes) integration in memory formation and demonstrate the remarkable sensitivity of the human auditory system to sporadically reoccurring structure within the acoustic environment.

show abstract

“…Rats and mice explore objects by probing them with back-and-forth movements of their whiskers (“whisking”; Vincent, 1912; Welker, 1964) and can solve a wide range of tasks in this way (Hutson and Masterton, 1986; Guić-Robles et al, 1989; Carvell and Simons, 1990; Krupa et al, 2001; Polley et al, 2005; Anjum et al, 2006; Knutsen et al, 2006; Mehta et al, 2007; Favaro et al, 2011; Fassihi et al, 2014; Sofroniew et al, 2014; Bale et al, 2017; Evans et al, 2018; Nikbakht et al, 2018). Contact causes whiskers to bend, and the associated torque (“bending moment”) is a major driver of spikes fired by primary whisker neurons (PWNs) located in the trigeminal ganglion (Bush et al, 2016; Campagner et al, 2016; Severson et al, 2017; for review, see Campagner et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Choice from Competing Mechanosensory and Choice-Memory Cues during Active Tactile Decision Making

et al. 2019

View full text Add to dashboard Cite

Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Male mice were trained to localize a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision, we estimated whisker–object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosensory and choice-memory signals, and provides a new task well suited for the future study of the neural basis of active perceptual decisions. SIGNIFICANCE STATEMENT Due to the difficulty of measuring the sensory input to moving sense organs, active perceptual decision making remains poorly understood. The whisker system provides a way forward since it is now possible to measure the mechanical forces due to whisker–object contact during behavior. Here we train mice in a novel behavioral task that challenges them to use rich mechanosensory cues but can be performed using one whisker and enables task-relevant mechanical forces to be precisely estimated. This approach enables rigorous study of how sensory cues translate into action during active, perceptual decision making. Our findings provide new insight into active touch and how sensory/internal signals interact to determine behavioral choices.

show abstract

Learning and recognition of tactile temporal sequences by mice and humans

Cited by 16 publications

References 66 publications

Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex

Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex

Long-term implicit memory for sequential auditory patterns in humans

Prediction of Choice from Competing Mechanosensory and Choice-Memory Cues during Active Tactile Decision Making

Contact Info

Product

Resources

About