Grid cells generate an analog error-correcting code for singularly precise neural computation

Sreenivasan, Sameet; Fiete, Ila

doi:10.1038/nn.2901

Cited by 186 publications

(262 citation statements)

References 48 publications

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“…Models of distributed state encodings were shown to be compatible with experimental observations of perceptual dominance (53), position encoding in grid cells (54), context and task rule encodings (31), and taste processing (55). Extensions to the SSM of this sort can enable the neuromorphic implementation of more versatile hidden Markov models (55), which can be used to solve a wide variety of machine-learning tasks (34).…”

Section: Discussionmentioning

confidence: 89%

Synthesizing cognition in neuromorphic electronic systems

Neftci

Binas

Rutishauser

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

125

122

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The quest to implement intelligent processing in electronic neuromorphic systems lacks methods for achieving reliable behavioral dynamics on substrates of inherently imprecise and noisy neurons. Here we report a solution to this problem that involves first mapping an unreliable hardware layer of spiking silicon neurons into an abstract computational layer composed of generic reliable subnetworks of model neurons and then composing the target behavioral dynamics as a "soft state machine" running on these reliable subnets. In the first step, the neural networks of the abstract layer are realized on the hardware substrate by mapping the neuron circuit bias voltages to the model parameters. This mapping is obtained by an automatic method in which the electronic circuit biases are calibrated against the model parameters by a series of population activity measurements. The abstract computational layer is formed by configuring neural networks as generic soft winner-take-all subnetworks that provide reliable processing by virtue of their active gain, signal restoration, and multistability. The necessary states and transitions of the desired high-level behavior are then easily embedded in the computational layer by introducing only sparse connections between some neurons of the various subnets. We demonstrate this synthesis method for a neuromorphic sensory agent that performs real-time context-dependent classification of motion patterns observed by a silicon retina.decision making | sensorimotor | working memory | analog very large-scale integration | artificial neural systems U nlike digital simulations, in which the dynamics of neuronal models are encoded and calculated on general purpose digital hardware, "neuromorphic" emulations express the dynamics of the neural systems directly on an analogous physical substrate (1). Digital simulations have the advantage that they can be exactly and reliably programmed using numerical operations of very high precision. However, they suffer the disadvantage that they are cast on abstract binary electronic circuits whose operation is entirely divorced from the physical processes being simulated. Consequently, such simulations do not readily advance our understanding of how biological neural systems are able to attain their extraordinary physical performance, using only large numbers of apparently unreliable, slow, and imprecise neural components, a problem first recognized by von Neumann more than a half century ago (2). Furthermore, the reliability of digital systems comes at high cost of the circuit complexity necessary for the orchestration of communication and processing, an overhead that declares itself also in the costs of system construction and power dissipation (3).The alternative, neuromorphic, approach to information processing strives to capture in complementary metal-oxide semiconductor (CMOS) very large-scale integration (VLSI) electronic technology the more distributed, asynchronous, and limited precision nature of biological intelligent systems (1, 4).†, ‡ Research...

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Section: Discussionmentioning

confidence: 89%

Synthesizing cognition in neuromorphic electronic systems

Neftci

Binas

Rutishauser

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

125

122

View full text Add to dashboard Cite

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“…Further, it is possible that theta oscillations could be an essential component of widespread neurocomputational processes beyond location, because computational models indicate that other analogous grid-like signals could represent different types of behavioural information [45].…”

Section: Theta Oscillations and Grid Cells In Bats And Ratsmentioning

confidence: 99%

Hippocampal theta oscillations are slower in humans than in rodents: implications for models of spatial navigation and memory

Jacobs

2014

Phil. Trans. R. Soc. B

231

233

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ReviewCite this article: Jacobs J. 2014 Hippocampal theta oscillations are slower in humans than in rodents: implications for models of spatial navigation and memory. The theta oscillation is a neuroscience enigma. When a rat runs through an environment, large-amplitude theta oscillations (4-10 Hz) reliably appear in the hippocampus's electrical activity. The consistency of this pattern led to theta playing a central role in theories on the neural basis of mammalian spatial navigation and memory. However, in fact, hippocampal oscillations at 4-10 Hz are rare in humans and in some other species. This presents a challenge for theories proposing theta as an essential component of the mammalian brain, including models of place and grid cells. Here, I examine this issue by reviewing recent research on human hippocampal oscillations using direct brain recordings from neurosurgical patients. This work indicates that the human hippocampus does indeed exhibit rhythms that are functionally similar to theta oscillations found in rodents, but that these signals have a slower frequency of approximately 1-4 Hz. I argue that oscillatory models of navigation and memory derived from rodent data are relevant for humans, but that they should be modified to account for the slower frequency of the human theta rhythm.

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“…But when the space of stimulus x is infinite instead of periodic, different spatial periods can be combined to encode a much larger range of x uniquely than would otherwise be possible [19]. Indeed, the exponential range that can result confers a relative precision that is also exponential [20]. This Letter, in contrast, shows that the absolute precision in x can be exponential in N. Precision is of paramount importance for path integration, for which the mammalian brain is thought to use grid cells [21].…”

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confidence: 99%

Resolution of Nested Neuronal Representations Can Be Exponential in the Number of Neurons

2012

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Collective computation is typically polynomial in the number of computational elements, such as transistors or neurons, whether one considers the storage capacity of a memory device or the number of floating-point operations per second of a CPU. However, we show here that the capacity of a computational network to resolve real-valued signals of arbitrary dimensions can be exponential in N, even if the individual elements are noisy and unreliable. Nested, modular codes that achieve such high resolutions mirror the properties of grid cells in vertebrates, which underlie spatial navigation.

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Grid cells generate an analog error-correcting code for singularly precise neural computation

Cited by 186 publications

References 48 publications

Synthesizing cognition in neuromorphic electronic systems

Synthesizing cognition in neuromorphic electronic systems

Hippocampal theta oscillations are slower in humans than in rodents: implications for models of spatial navigation and memory

Resolution of Nested Neuronal Representations Can Be Exponential in the Number of Neurons

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