Computational Neuroscience: Mathematical and Statistical Perspectives

Kass, Robert E.; Amari, S.; Arai, Kohei; Brown, Emery N.; Diekman, Casey O.; Diesmann, Markus; Doiron, Brent; Eden, Uri T.; Fairhall, Adrienne L.; Fiddyment, Grant M.; Fukai, Tomoki; Grün, Sonja; Harrison, Matthew T.; Helias, Moritz; Nakahara, Hiroyuki; Teramae, Jun-nosuke; Thomas, Peter J.; Reimers, Mark; Rodu, Jordan; Rotstein, Horacio G.; Shea‐Brown, Eric; Shimazaki, Hideaki; Shinomoto, Shigeru; Yu, Byron M.; Kramer, Mark A.

doi:10.1146/annurev-statistics-041715-033733

Cited by 56 publications

(40 citation statements)

References 194 publications

(232 reference statements)

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“…Networks with large, balanced excitatory and inhibitory connections are an attractive model framework for cortical activity because they capture several aspects of reported in vivo population response. When large synaptic connections are paired with random network wiring they naturally produce significant heterogeneity in spiking activities across the network 42,54,55 . Indeed, in our spatially ordered network the L2/3 neurons have very heterogeneous tuning curves with various widths and magnitudes (Fig.…”

Section: Resultsmentioning

confidence: 99%

Internally generated population activity in cortical networks hinders information transmission

Huang

Pouget

Doiron

2020

Preprint

Self Cite

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4How neuronal variability impacts neuronal codes is a central question in systems neuroscience, often 5 with complex and model dependent answers. Most population models are parametric, with a tacitly 6 assumed structure of neuronal tuning and population-wide variability. While these models provide 7 key insights, they purposely divorce any mechanistic relationship between trial average and trial vari-8 able neuronal activity. By contrast, circuit based models produce activity with response statistics that 9 are reflection of the underlying circuit structure, and thus any relations between trial averaged and 10 trial variable activity are emergent rather than assumed. In this work, we study information transfer 11 in networks of spatially ordered spiking neuron models with strong excitatory and inhibitory interac-12 tions, capable of producing rich population-wide neuronal variability. Motivated by work in the visual 13 system we embed a columnar stimulus orientation map in the network and measure the population 14 estimation of an orientated input. We show that the spatial structure of feedforward and recurrent 15 connectivity are critical determinants for population code performance. In particular, when network 16 wiring supports stable firing rate activity then with a sufficiently large number of decoded neurons all 17 1 available stimulus information is transmitted. However, if the inhibitory projections place network ac-18 tivity in a pattern forming regime then the population-wide dynamics compromise information flow. 19 In total, network connectivity determines both the stimulus tuning as well as internally generated 20 population-wide fluctuations and thereby dictates population code performance in complicated ways 21 where modeling efforts provide essential understanding. 23A prominent feature of cortical response to sensory stimuli is that neuronal activity varies significantly 24 across presentation trials 1,2 , even when efforts are taken to control or account for variable animal 25 behavior 3-6 . A component of this variability is coordinated among neurons in a brain area, often 26 leading to shared fluctuations in spiking activity [7][8][9][10][11] . How stimulus processing is affected by this large, 27 population-wide neuronal variability is a longstanding question in both experimental and theoretical 28 neuroscience communities. 29 Recording from neuronal populations while simultaneously monitoring an animal's behavior dur-30 ing a structured task offers a glimpse into how neuronal activity supports computation. Correlations 31 between spike counts from pairs of neurons in response to repeated stimulus, often referred to as 32 noise correlations, are modulated by a variety of cognitive factors that are known to affect task perfor-33 mance 4 . For example, noise correlations decrease with animal arousal 12,13 or task engagement 14 . In 34 the visual pathway noise correlations are decreased when spatial attention is directed into the recep-35 tive field of a recorded population 15,16 ...

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Section: Resultsmentioning

confidence: 99%

Internally generated population activity in cortical networks hinders information transmission

Huang

Pouget

Doiron

2020

Preprint

Self Cite

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“…In order to explain this idea, I will focus on computational neuroscience. This area of neuroscience is grounded on pioneering artificial intelligence work (such as McCulloch and Pitts, as well as Rosenblatt) and biophysics (such as Hodgkin and Huxley) (Kass, 2018). These proposals advanced a new domain of research in which computational models were proposed to explain neural activity and brain function at all levels of detail and abstraction, from subcellular biophysics to human behavior (Kass, 2018).…”

Section: The Brain Is Not Digitalmentioning

confidence: 99%

“…This area of neuroscience is grounded on pioneering artificial intelligence work (such as McCulloch and Pitts, as well as Rosenblatt) and biophysics (such as Hodgkin and Huxley) (Kass, 2018). These proposals advanced a new domain of research in which computational models were proposed to explain neural activity and brain function at all levels of detail and abstraction, from subcellular biophysics to human behavior (Kass, 2018). The current models of computational neuroscience are fed by concrete biophysical data and it is commonly believed that computational neuroscience also offers methods for the analysis of neural data (Kass, 2018).…”

Section: The Brain Is Not Digitalmentioning

confidence: 99%

El problema de ensamble

Destéfano¹

2020

Anál. Filos.

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Desde la bioloinguistica, ensamble sería una operación digital realizada en el cerebro que, en tanto tal, estaría asociada a principios específicos de la computación neural. En una primera aproximación, la computación digital consiste en el procesamiento de cadenas de dígitos de acuerdo a reglas generales. Sin embargo, los procesos neurales no se desarrollarían de acuerdo a los principios de la computación digital. Estas afirmaciones en conflicto, e.g., la caracterización digital de ensamble y la caracterización no digital del cerebro, llevan al siguiente escenario: o bien ensamble es una operación que no realiza el cerebro, o bien es realizada por el cerebro pero no digitalmente. El propósito de este artículo es evaluar los problemas de estas dos tesis.

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“…These issues are particularly salient in systems neuroscience, where parametric models are often used to understand how neural activity is modulated by external factors (e.g., stimuli or a behavioral task) and internal factors (e.g., other neurons) [9,10]. The fitted parameter values, therefore, specify which factors are important in modulating neural activity, and how important they are.…”

Section: Introductionmentioning

confidence: 99%

Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery

Sachdeva

Livezey

Dougherty

et al. 2020

Preprint

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A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations and their modulation by external factors using high-dimensional and stochastic neural recordings. Statistical models, particularly parametric models, play an instrumental role in accomplishing this goal, because their fitted parameters can provide insight into the underlying biological processes that generated the data. However, extracting conclusions from a parametric model requires that it is fit using an inference procedure capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. Recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. Here, we used the Union of Intersections, a statistical inference framework capable of state-of-the-art selection and estimation performance, to fit functional coupling, encoding, and decoding models across a battery of neural datasets. We found that, compared to baseline procedures, UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance across recording modality, brain region, and task. Specifically, we obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that rely on fewer single-units. Together, these results demonstrate that accurate parameter inference reshapes interpretation in diverse neuroscience contexts. The ubiquity of model-based data-driven discovery in biology suggests that analogous results would be seen in other fields.

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Computational Neuroscience: Mathematical and Statistical Perspectives

Cited by 56 publications

References 194 publications

Internally generated population activity in cortical networks hinders information transmission

Internally generated population activity in cortical networks hinders information transmission

El problema de ensamble

Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery

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