Neural Flip-Flops I: Short-Term Memory

Yoder, Lane

doi:10.1101/403196

Cited by 3 publications

(20 citation statements)

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“…All Boolean logic results for the networks presented here follow from the neuron responses to binary (high and low) input signals and the algebra of Boolean logic applied to the networks' connections. Analog signals (intermediate strengths between high and low) were considered in [1,2] only to show how NFFs can generate robust binary signals in the presence of moderate levels of additive noise in binary inputs. That discussion will not be repeated here.…”

Section: Neural Boolean Logicmentioning

confidence: 99%

“…This article is the fourth in a series of articles that show how neurons are likely to be connected to perform certain Boolean logic functions with networks composed of neural flip-flops (NFFs). The first three articles [1][2][3] showed that NFFs and NFFs configured as central pattern generators (CPGs) can produce the major phenomena of short-term memory, electroencephalography, and the lobster's stomatogastric ganglion.…”

Section: Introductionmentioning

confidence: 99%

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Neural Flip-Flops IV: Lamprey Locomotion

Yoder¹

2021

Preprint

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<p>The lamprey is one of the most ancient of extant vertebrate species. It has changed relatively little in 450 million years and is considered a prototype for all vertebrates. Its primitive nervous system has been studied extensively, and the basic architecture of the central pattern generator (CPG) that produces its anguilliform swimming motion is well known. Here it is shown that each segmental component of the lamprey's CPG is a JK flip-flop, with additional excitatory inputs and feedback that cause all of the neurons' states to oscillate. The JK flip-flop is the most widely used flip-flop design in electronic computational systems because of its advantageous features. This is apparently the first discovery that a known network of neurons functions as a logic circuit. The lamprey's oscillator can be implemented with electronic hardware, and the design is apparently unknown in engineering. A simulation based on simple neuron responses to excitation and inhibition illustrates the common period, phase relationships, and burst durations of the segmental cells' oscillations. Simulation software for electronic logic circuits verifies the simulated neuron responses, on vastly different time scales. The simulation methods presented here may aid in further study of CPG neurophysiology. The architecture of the oscillating JK flip-flop may aid in the development of artificial neural network applications such as robotics.</p>

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Section: Neural Boolean Logicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Flip-Flops IV: Lamprey Locomotion

Yoder¹

2021

Preprint

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“…Its significance for the networks presented here is that it can be implemented with a single neuron. It was shown in [2,4] The response is the logical truth value of X AND NOT Y.…”

Section: Binary Neuron Signalsmentioning

confidence: 99%

“…Any of the networks in the figures could be constructed with actual neurons and tested for their predicted behavior. (Constructing and testing NFFs were also discussed in [4,5].) Fig 12 shows a few simple possibilities.…”

Section: Testable Predictions Of Constructed Neural Networkmentioning

confidence: 99%

“…The first three articles [1][2][3] showed how a fuzzy logic decoder can generate the phenomena central to color vision and olfaction and can account for major aspects of the anatomical structure and physiological organization of the neocortex. The next two articles [4,5] (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted December 1, 2020. ; https://doi.org/10.1101/2020.11.29.403154 doi: bioRxiv preprint 5 fill a gap in the patterns produced by inverter rings.…”

Section: Introductionmentioning

confidence: 99%

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Neural Flip-Flops III: Stomatogastric Ganglion

Yoder

2020

Preprint

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The stomatogastric ganglion (STG) is a group of about 30 neurons that resides on the stomach in decapod crustaceans. Its two central pattern generators (CPGs) control the chewing action of the gastric mill and the peristaltic movement of food through the pylorus to the gut. The STG has been studied extensively because it has properties that are common to all nervous systems and because of the small number of neurons and other features that make it convenient to study. So many details are known that the STG is considered a classic test case in neuroscience for the reductionist strategy of explaining the emergence of macro-level phenomena from micro-level data. In spite of the intense scrutiny the STG has received, how it generates its rhythmic patterns of bursts remains unknown. The novel neural networks presented here model the pyloric CPG of the American lobster (Homarus americanus). Each model's connectivity is explicit, and its operation depends only on minimal neuron capabilities of excitation and inhibition. One type of model CPGs, flip-flop ring oscillators, is apparently new to engineering, making it an example of neuroscience and logic circuit design informing each other. Several testable predictions are given here, and STG phenomena are shown to support several predictions of neural flip-flops that were given in a previous paper on short-term memory. The model CPGs are not the same as the more complex pyloric CPG. But they show how neurons can be connected to produce oscillations, and there are enough similarities in significant features that they may be considered first approximations, or perhaps simplified versions, of STG architecture. The similarities include 1) mostly inhibitory synapses; 2) pairs of cells with reciprocal, inhibitory inputs, complementary outputs that are approximately 180 degrees out of phase, and state changes occurring with the high output changing first; 3) cells that have reciprocal, inhibitory inputs with more than one other cell; and 4) six cells that produce coordinated oscillations with the same period, four phases distributed approximately uniformly over the period, and half of the burst durations approximately 1/4 of the period and the other half 3/8. These variables cannot be controlled independently in the design, suggesting a similar architecture in the models and the STG. Some of the neural network designs can be derived from electronic logic circuit designs simply by moving each negation symbol from one end of a connection to the other. This does not change the logic of the network, but it changes each logic gate to one that can be implemented with a single neuron.

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Neural Flip-Flops II: The Role of Cascaded Oscillators in Short-Term Memory, EEGs, and Epilepsy

Yoder

2020

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9For certain brain functions, the theoretical networks presented here almost certainly show 10 how neurons are actually connected. Stripped of details such as redundancies and other error-11 correcting mechanisms, the basic organization of synaptic connections within some of the brain's 12 building blocks is likely to be less complex than it appears. For some brain functions, the 13 network architectures can even be quite simple. 14 Flip-flops are the basic building blocks of sequential logic systems. Certain flip-flops can 15 be configured to function as oscillators. The flip-flops and oscillators proposed here are 16 EEG sample statistics can be compared to the EEG parameters predicted by the delay time 48 statistics. 49

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Neural Flip-Flops I: Short-Term Memory

Cited by 3 publications

References 56 publications

Neural Flip-Flops IV: Lamprey Locomotion

Neural Flip-Flops IV: Lamprey Locomotion

Neural Flip-Flops III: Stomatogastric Ganglion

Neural Flip-Flops II: The Role of Cascaded Oscillators in Short-Term Memory, EEGs, and Epilepsy

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