Towards a neural-level cognitive architecture: modeling behavior in working memory tasks with neurons

Constrained by results from classic behavioral experiments we provide a neural-level cognitive architecture for modeling behavior in working memory tasks. We propose a canonical microcircuit that can be used as a building block for working memory, decision making and cognitive control. The controller controls gates to route the flow of information between the working memory and the evidence accumulator and sets parameters of the circuits. We show that this type of cognitive architecture can account for results in behavioral experiments such as judgment of recency, probe recognition and delayedmatch- to-sample. In addition, the neural dynamics generated by the cognitive architecture provides a good match with neurophysiological data from rodents and monkeys. For instance, it generates cells tuned to a particular amount of elapsed time (time cells), to a particular position in space (place cells) and to a particular amount of accumulated evidence.http://sites.bu.edu/tcn/files/2019/05/Cogsci2019_TiganjEtal.pdfAccepted manuscrip

Evidence accumulation in a Laplace domain decision space

Evidence accumulation models of simple decision-making have long assumed that the brain estimates a scalar decision variable corresponding to the log-likelihood ratio of the two alternatives. Typical neural implementations of this algorithmic cognitive model assume that large numbers of neurons are each noisy exemplars of the scalar decision variable. Here we propose a neural implementation of the diffusion model in which many neurons construct and maintain the Laplace transform of the distance to each of the decision bounds. As in classic findings from brain regions including LIP, the firing rate of neurons coding for the Laplace transform of net accumulated evidence grows to a bound during random dot motion tasks. However, rather than noisy exemplars of a single mean value, this approach makes the novel prediction that firing rates grow to the bound exponentially, across neurons there should be a distribution of different rates. A second set of neurons records an approximate inversion of the Laplace transform, these neurons directly estimate net accumulated evidence. In analogy to time cells and place cells observed in the hippocampus and other brain regions, the neurons in this second set have receptive fields along a "decision axis." This finding is consistent with recent findings from rodent recordings. This theoretical approach places simple evidence accumulation models in the same mathematical language as recent proposals for representing time and space in cognitive models for memory.Comment: Revised for CB

Evidence accumulation in a Laplace domain decision space

Evidence accumulation models of simple decision-making have long assumed that the brain estimates a scalar decision variable corresponding to the log likelihood ratio of the two alternatives. Typical neural implementations of this algorithmic cognitive model assume that large numbers of neurons are each noisy exemplars of the scalar decision variable. Here, we propose a neural implementation of the diffusion model in which many neurons construct and maintain the Laplace transform of the distance to each of the decision bounds. As in classic findings from brain regions including LIP, the firing rate of neurons coding for the Laplace transform of net accumulated evidence grows to a bound during random dot motion tasks. However, rather than noisy exemplars of a single mean value, this approach makes the novel prediction that firing rates grow to the bound exponentially; across neurons, there should be a distribution of different rates. A second set of neurons records an approximate inversion of the Laplace transform; these neurons directly estimate net accumulated evidence. In analogy to time cells and place cells observed in the hippocampus and other brain regions, the neurons in this second set have receptive fields along a “decision axis.” This finding is consistent with recent findings from rodent recordings. This theoretical approach places simple evidence accumulation models in the same mathematical language as recent proposals for representing time and space in cognitive models for memory.Accepted manuscrip

Is working memory stored along a logarithmic timeline? Converging evidence from neuroscience, behavior and models

A growing body of evidence suggests that short-term memory does not only store the identity of recently experienced stimuli, but also information about when they were presented. This representation of 'what' happened 'when' constitutes a neural timeline of recent past. Behavioral results suggest that people can sequentially access memories for the recent past, as if they were stored along a timeline to which attention is sequentially directed. In the short-term judgment of recency (JOR) task, the time to choose between two probe items depends on the recency of the more recent probe but not on the recency of the more remote probe. This pattern of results suggests a backward self-terminating search model. We review recent neural evidence from the macaque lateral prefrontal cortex (lPFC) (Tiganj, Cromer, Roy, Miller, & Howard, in press) and behavioral evidence from human JOR task (Singh & Howard, 2017) bearing on this question. Notably, both lines of evidence suggest that the timeline is logarithmically compressed as predicted by Weber-Fechner scaling. Taken together, these findings provide an integrative perspective on temporal organization and neural underpinnings of short-term memory.R01 EB022864 - NIBIB NIH HHS; R01 MH112169 - NIMH NIH HHSAccepted manuscrip

REKONSTRUKCIJSKI PRETVORNIK S/PDIF ZAPISA ZA AVDIO APLIKACIJE

Pričujoče delo opisuje problematiko integracije sklopa za rekonstrukcijo avdio signala v visoko kakovostni avdio ojačevalnik, ki temelji na realnem modelu digitalne stereofonske avdio distribucije za končnega uporabnika. V uvodnem poglavju so povzeti cilji dela, fizikalne lastnosti zvoka in kratek pregled področja, kjer je opisano ozadje problematike. Drugo poglavje vsebuje podrobnejši opis obravnavane problematike, začenši s predstavitvijo modela hišnega in avtomobilskega avdio sistema za katerega se razvija rešitev. Sledi opis pričakovanih težav in tehnoloških omejitev ter predstavitev zahtev in robnih pogojev, ki temeljijo na pregledu trendov. V tretjem poglavju je rešitev načrtana in realizirana z upoštevanjem načrtovalskih smernic in ponujenih informacij s strani proizvajalcev. V četrtem poglavju je skozi meritev posameznih sklopov podana ocena uspešnosti zasnove vezja, ki potrjuje zmožnost realizacije rešitve. V zadnjem poglavju so povzete ugotovitve, ki kažejo na to, da integracija sklopov dvigne nivo kompleksnosti načrtovanja pri združevanju analognih in digitalnih sklopov. Povzete so tudi možnosti za izboljšavo vezja med katere sodijo npr. uporaba fiksnih napetostnih regulatorjev, večslojno TIV, izboljšano krmiljenje z uporabo mikrokrmilnikov, uporaba brezžične komunikacije, ipd.Present work describes the integration issues of the reconstruction module for audio applications into high fidelity audio amplifier based on a real model of consumer grade digital stereophonic audio distribution. First chapter summarizes the work objectives, describes physical properties of sound and gives a brief background overview of the related issues. Second chapter describes addressed issues in detail, in particular the model of home and car audio system, the target module application fields. Followed by the introduction of expected problems and technological limitations, and a presentation of requirements and boundary conditions based on a review of trends. Third chapter presents the design and implementation of proposed solution with respect to design guidelines consideration and information provided by manufacturers. Fourth chapter summarizes designed circuit performance, which is evaluated through a series of comprising parts measurements. Final evaluation confirms the feasibility of the proposed design approach. In conclusion, the results show that the integration of parts raises the design complexity level caused by combining analogue and digital components. Improvements and future module extensions, which include the use of fixed voltage regulators, multilayer PCB, improved control of the circuit with microcontrollers, the use of wireless communication and many more are also presented

An algebraic method for eye blink artifacts detection in single channel EEG recordings

International audienceSingle channel EEG systems are very useful in EEG based applications where real time processing, low computational complexity and low cumbersomeness are critical constrains. These include brain-computer interface and biofeedback devices and also some clinical applications such as EEG recording on babies or Alzheimer's disease recognition. In this paper we address the problem of eye blink artifacts detection in such systems. We study an algebraic approach based on numerical differentiation, which is recently introduced from operational calculus. The occurrence of an artifact is modeled as an irregularity which appears explicitly in the time (generalized) derivative of the EEG signal as a delay. Manipulating such delay is easy with the operational calculus and it leads to a simple joint detection and localization algorithm. While the algorithm is devised based on continuous-time arguments, the final implementation step is fully realized in a discrete-time context, using very classical discrete-time FIR filters. The proposed approach is compared with three other approaches: (1) the very basic threshold approach, (2) the approach that combines the use of median filter, matched filter and nonlinear energy operator (NEO) and (3) the wavelet based approach. Comparison is done on: (a) the artificially created signal where the eye activity is synthesized from real EEG recordings and (b) the real single channel EEG recordings from 32 different brain locations. Results are presented with Receiver Operating Characteristics curves. The results show that the proposed approach compares to the other approaches better or as good as, while having lower computational complexity with simple real time implementation. Comparison of the results on artificially created and real signal leads to conclusions that with detection techniques based on derivative estimation we are able to detect not only eye blink artifacts, but also any spike shaped artifact, even if it is very low in amplitude

DeepSITH: efficient learning via decomposition of what and when across time scales

Extracting temporal relationships over a range of scales is a hallmark of human perception and cognition -- and thus it is a critical feature of machine learning applied to real-world problems. Neural networks are either plagued by the exploding/vanishing gradient problem in recurrent neural networks (RNNs) or must adjust their parameters to learn the relevant time scales (e.g., in LSTMs). This paper introduces DeepSITH, a network comprising biologically-inspired Scale-Invariant Temporal History (SITH) modules in series with dense connections between layers. SITH modules respond to their inputs with a geometrically-spaced set of time constants, enabling the DeepSITH network to learn problems along a continuum of time-scales. We compare DeepSITH to LSTMs and other recent RNNs on several time series prediction and decoding tasks. DeepSITH achieves state-of-the-art performance on these problems.https://papers.nips.cc/paper/2021/file/e7dfca01f394755c11f853602cb2608a-Paper.pdfPublished versio