Thalamocortical loops as temporal demodulators across senses

Ahissar, Ehud; Nelinger, Guy; Assa, Eldad; Karp, Ofer; Saraf-Sinik, Inbar

doi:10.1038/s42003-023-04881-4

Cited by 3 publications

(2 citation statements)

References 131 publications

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“…Implementing this would be straightforward, given the documented existence of CMOS equivalents for all the constituent building blocks [21][22][23][24]. In this scenario, the integrated circuits could be embodied into robots or prosthetics, enhancing the ability of artificial agents to discern textures, alongside other sensory stimuli such as vision and audio [25], while maintaining extremely low power consumption and minimal latency [26]. A CMOS circuit implementing these functions has been recently designed and fabricated.…”

Section: Discussionmentioning

confidence: 99%

Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition

Mastella,

Tiemens,

Chicca

2024

Preprint

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Neural spikes can encode a rich set of information, ranging from the perceived intensity of light sources to the likelihood associated with decisions made in the cortex. Among these capabilities, previous studies demonstrated that spikes can also encode in their activity multiple frequencies at the same time, such as those generated by skin vibrations during textures scanning. However, the mechanism responsible for decoding spikes containing multiple frequencies is yet to be uncovered. In this paper, we introduce a novel spiking neural network model tailored for frequency decomposition of spike trains. Our model mimics neural microcircuits hypothesized in the somatosensory cortex, making it a biologically plausible candidate for decoding spike trains observed in tactile peripheral nerves. We showcase the ability of simple neurons and synapses to replicate the functionality of a phase-locked loop (PLL) and delve into the emergent properties when multiple spiking phase-locked loops (sPLLs) interact with diverse inputs. Furthermore, we demonstrate how these sPLLs can decode textures by leveraging the spectral features of spike trains generated in peripheral nerves. By harnessing our model's frequency decomposition capabilities, we achieve significant performance enhancements over state-of-the-art approaches on a Multifrequency Spike Train (MST) dataset. Our findings underscore the potential of sPLLs in elucidating the mechanisms behind texture decoding in the brain, while also showcasing their potential to outperform conventional SNNs in handling spike trains with multiple frequencies. We believe this study sheds light into the neuronal mechanisms behind texture decoding, while presenting a practical framework for augmenting the capabilities of artificial neural networks in intricate pattern recognition tasks.

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Section: Discussionmentioning

confidence: 99%

Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition

Mastella,

Tiemens,

Chicca

2024

Preprint

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“…Precise neuronal timing plays a critical role in many brain functions including sensory processing [1][2][3][4][5][6] or the encoding of memory by synaptic plasticity [7][8][9]. In fact, neural information is not solely transmitted in the brain through the modulation of the firing rate (i.e., rate coding), but also transmitted by the fine temporal organization of neuronal discharge (i.e., time coding) [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Release Probability without Changes in Synaptic Delay during Analogue–Digital Facilitation

Boudkkazi,

Debanne

2024

Cells

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Neuronal timing with millisecond precision is critical for many brain functions such as sensory perception, learning and memory formation. At the level of the chemical synapse, the synaptic delay is determined by the presynaptic release probability (Pr) and the waveform of the presynaptic action potential (AP). For instance, paired-pulse facilitation or presynaptic long-term potentiation are associated with reductions in the synaptic delay, whereas paired-pulse depression or presynaptic long-term depression are associated with an increased synaptic delay. Parallelly, the AP broadening that results from the inactivation of voltage gated potassium (Kv) channels responsible for the repolarization phase of the AP delays the synaptic response, and the inactivation of sodium (Nav) channels by voltage reduces the synaptic latency. However, whether synaptic delay is modulated during depolarization-induced analogue–digital facilitation (d-ADF), a form of context-dependent synaptic facilitation induced by prolonged depolarization of the presynaptic neuron and mediated by the voltage-inactivation of presynaptic Kv1 channels, remains unclear. We show here that despite Pr being elevated during d-ADF at pyramidal L5-L5 cell synapses, the synaptic delay is surprisingly unchanged. This finding suggests that both Pr- and AP-dependent changes in synaptic delay compensate for each other during d-ADF. We conclude that, in contrast to other short- or long-term modulations of presynaptic release, synaptic timing is not affected during d-ADF because of the opposite interaction of Pr- and AP-dependent modulations of synaptic delay.

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Predictable sequential structure augments auditory sensitivity at threshold

Marin,

Gérenton,

Jean

et al. 2023

Preprint

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Human hearing is highly sensitive and allows us to detect acoustic events at low levels and at great distances. However, sensitivity is not only a function of the integrity of cochlear mechanisms, but also constrained by central processes such as attention and expectation. While the effects of distraction and attentional orienting are generally acknowledged, the extent to which probabilistic expectations influence sensitivity is not clear. Classical audiological assessment, commonly used to assess hearing sensitivity, does not distinguish between bottom-up sensitivity and top-down gain/filtering. In this study, we aim to decipher the influence of various types of expectations on hearing sensitivity and how this information can be used to improve the assessment of sensitivity to sound. Our results raise important critiques regarding common practices in the assessment of sound sensitivity, both in fundamental research and in audiological clinical assessment.Significance StatementThe principal clinical measure of ear function is the pure-tone audiogram in which a clinician will test for the softest tones one can detect across the frequency range. Clinicians have long known listener strategy can influence these measures whereby patients can guess when tones may occur based on regularities in the tone presentation structure. Our results demonstrate that this predictability and other forms influence not only listener strategy but also the sensitivity of the peripheral auditory system itself. This finding that prediction not only biases low-level perception but also enhances it has wide-ranging consequences for the fields of audiology and cognitive neuroscience, and furthermore suggests that prediction ability should be tracked as well as ear sensitivity as patients age.

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Thalamocortical loops as temporal demodulators across senses

Cited by 3 publications

References 131 publications

Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition

Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition

Enhanced Release Probability without Changes in Synaptic Delay during Analogue–Digital Facilitation

Predictable sequential structure augments auditory sensitivity at threshold

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