scholarly journals Actin Automata with Memory

2016 ◽  
Vol 26 (01) ◽  
pp. 1650019 ◽  
Author(s):  
Ramón Alonso-Sanz ◽  
Andy Adamatzky
Keyword(s):  
Actin Filaments ◽  
State Transitions ◽  
State Machines ◽  
Double Chain ◽  
Neighboring Node ◽  
Current State ◽  
Time Patterns ◽  
Finite State ◽  
With Memory ◽  
Momentary State

Actin is a globular protein which forms long polar filaments in eukaryotic. The actin filaments play the roles of cytoskeleton, motility units, information processing and learning. We model actin filament as a double chain of finite state machines, nodes, which take states “0” and “1”. The states are abstractions of absence and presence of a subthreshold charge on actin units corresponding to the nodes. All nodes update their state in parallel to discrete time. A node updates its current state depending on states of two closest neighbors in the node chain and two closest neighbors in the complementary chain. Previous models of actin automata consider momentary state transitions of nodes. We enrich the actin automata model by assuming that states of nodes depend not only on the current states of neighboring node but also on their past states. Thus, we assess the effect of memory of past states on the dynamics of acting automata. We demonstrate in computational experiments that memory slows down propagation of perturbations, decrease entropy of space-time patterns generated, transforms traveling localizations to stationary oscillators, and stationary oscillations to still patterns.

Self-Power Saving Technique in State Machine Circuit of Automatic Teller Machine Application

2019 ◽  
Vol 15 (3) ◽  
pp. 294-301
Author(s):  
Minh-Huan Vo
Keyword(s):  
Finite State Machine ◽  
Power Saving ◽  
State Machine ◽  
Active Power ◽  
The Other ◽  
State Transitions ◽  
Power Gating ◽  
Automatic Teller Machine ◽  
Current State ◽  
Finite State

In a finite state machine (FSM), there is only one active state while the other states are in idle states simultaneously. Thus, only one state is required to power up, the other states can be switched off to save active power. Normally, a backward traversing algorithm is used to label the power gating cells and extract the enable signals for combinational logic gates in reducing the active power consumption. This conventional power gating technique uses the extracted enable signals to turn ON/OFF these inserted NMOS switches. Then, a power management unit is required to manage these enable signals and detect the idle periods. The proposed self-power saving technique uses internally generated enable signals from state transitions to control NMOS switches inserted under the ground rail of each state. All internal enable signals are created to activate/deactivate the machine states at the same time. Based on the next state of the FSM, a decoder creates the enable signals for each state to do power gating in an Automatic Teller Machine (ATM) application. The isolation cell is designed to isolate the current state and next state for retaining data. Simulation results show the power saving from 31.99% at a WAIT state to 82.87% at a LOCK state, depending on the current state of the finite state machine. On average, the power loss is saved up to 63.2% in the FSM. An overhead area is about 12% compared to the conventional technique while timing overhead is under 5%.

Random coding bound for channels with memory decoding function with partial overlapping. Part 2. Examples and Discussion

2018 ◽  
pp. 73-85
Author(s):  
A. N. Trofimov
Keyword(s):  
Finite State Machine ◽  
Full Range ◽  
State Transitions ◽  
Interference Channels ◽  
Random Coding ◽  
Channel Models ◽  
Good Correspondence ◽  
Finite State ◽  
With Memory ◽  
Optimal Decoding

Introduction:Suboptimal random coding exponent Er*(R; ψ) for a wide class of finite-state channel models using a mismatched decoding function tp was obtained and presented in the first part of this work. We used tp function represented as a product of a posteriori probabilities of non-overlapped input subblocks of length 2B+1 relative to the overlapped output subblocks of length 2W+1. It has been shown that the computation of function Er*(R; ψ) is reduced to the calculation of the largest eigenvalue of a square non-negative matrix of an order depending on the B and W values.Purpose:Toillustrate the approach developed in the first part of this study with its application to various channel modelled as a probabilistic finite-state machine.Results:We consider channels with state transitions not depending on the input symbol (channels with freely evolving states), and channels with deterministic state transitions, in particular, intersymbol interference channels. We present and discuss numerical results of calculating this random coding exponent in a full range of code rates for some of channel models for which similar results were not obtained before. Practical computations were carried out for relatively small values of B and W. Nevertheless, even for small values of these parameters a good correspondence with some known results for optimal decoding was shown.

scholarly journals State-Dependent Computation Using Coupled Recurrent Networks

2009 ◽  
Vol 21 (2) ◽  
pp. 478-509 ◽  
Author(s):  
Ueli Rutishauser ◽  
Rodney J. Douglas
Keyword(s):  
State Machines ◽  
Recurrent Networks ◽  
Current State ◽  
State Dependent ◽  
Neuronal Mechanisms ◽  
Simple Rules ◽  
Finite State ◽  
Intelligent Behavior ◽  
Locally Recurrent ◽  
Winner Take All

Although conditional branching between possible behavioral states is a hallmark of intelligent behavior, very little is known about the neuronal mechanisms that support this processing. In a step toward solving this problem, we demonstrate by theoretical analysis and simulation how networks of richly interconnected neurons, such as those observed in the superficial layers of the neocortex, can embed reliable, robust finite state machines. We show how a multistable neuronal network containing a number of states can be created very simply by coupling two recurrent networks whose synaptic weights have been configured for soft winner-take-all (sWTA) performance. These two sWTAs have simple, homogeneous, locally recurrent connectivity except for a small fraction of recurrent cross-connections between them, which are used to embed the required states. This coupling between the maps allows the network to continue to express the current state even after the input that elicited that state is withdrawn. In addition, a small number of transition neurons implement the necessary input-driven transitions between the embedded states. We provide simple rules to systematically design and construct neuronal state machines of this kind. The significance of our finding is that it offers a method whereby the cortex could construct networks supporting a broad range of sophisticated processing by applying only small specializations to the same generic neuronal circuit.

Schizophrenia as a Disorder of Cerebral State Transition

1986 ◽  
Vol 20 (2) ◽  
pp. 167-178 ◽  
Author(s):  
J. J. Wright ◽  
R. R. Kydd
Keyword(s):  
State Transition ◽  
Finite State Machines ◽  
The State ◽  
State Transitions ◽  
Brain State ◽  
State Machines ◽  
Electrocortical Activity ◽  
Wave Nature ◽  
Finite State

This paper offers a speculative consideration of the schizophrenic process in the light of recent findings concerning the wave nature of electrocortical activity. These findings indicate that changes of brain state can be described in the terminology of finite-state machines, and both the instantaneous states and the state transitions can be specified. It is suggested that the mental phenomena of schizophrenia may be reducible to events (some specific type of instability) which could be observed by appropriate analytic techniques applied to EEG. Present empirical EEG findings in schizophrenics are reviewed in this light, and the role of dopamine blockade in treatment is also considered.

scholarly journals Robust finite automata in stochastic chemical reaction networks

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
David Arredondo ◽  
Matthew R. Lakin
Keyword(s):  
Finite Automata ◽  
Chemical Species ◽  
State Transitions ◽  
Biomolecular Systems ◽  
Current State ◽  
Input Signals ◽  
Finite State ◽  
The Face ◽  
Approximate Majority ◽  
Control Circuits

Finite-state automata (FSA) are simple computational devices that can nevertheless illustrate interesting behaviours. We propose that FSA can be employed as control circuits for engineered stochastic biological and biomolecular systems. We present an implementation of FSA using counts of chemical species in the range of hundreds to thousands, which is relevant for the counts of many key molecules such as mRNAs in prokaryotic cells. The challenge here is to ensure a robust representation of the current state in the face of stochastic noise. We achieve this by using a multistable approximate majority algorithm to stabilize and store the current state of the system. Arbitrary finite state machines can thus be compiled into robust stochastic chemical automata. We present two variants: one that consumes its input signals to initiate state transitions and one that does not. We characterize the state change dynamics of these systems and demonstrate their application to solve the four-bit binary square root problem. Our work lays the foundation for the use of chemical automata as control circuits in bioengineered systems and biorobotics.

scholarly journals Structural Decomposition in FSM Design: Roots, Evolution, Current State—A Review

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1174
Author(s):  
Alexander Barkalov ◽  
Larysa Titarenko ◽  
Kazimierz Krzywicki
Keyword(s):  
Experimental Results ◽  
State Machines ◽  
Structural Decomposition ◽  
State Assignment ◽  
New Methods ◽  
Current State ◽  
Control Units ◽  
Joint Application ◽  
Finite State ◽  
Analysis Of Change

The review is devoted to methods of structural decomposition that are used for optimizing characteristics of circuits of finite state machines (FSMs). These methods are connected with the increasing the number of logic levels in resulting FSM circuits. They can be viewed as an alternative to methods of functional decompositions. The roots of these methods are analysed. It is shown that the first methods of structural decomposition have appeared in 1950s together with microprogram control units. The basic methods of structural decomposition are analysed. They are such methods as the replacement of FSM inputs, encoding collections of FSM outputs, and encoding of terms. It is shown that these methods can be used for any element basis. Additionally, the joint application of different methods is shown. The analysis of change in these methods related to the evolution of the logic elements is performed. The application of these methods for optimizing FPGA- based FSMs is shown. Such new methods as twofold state assignment and mixed encoding of outputs are analysed. Some methods are illustrated with examples of FSM synthesis. Additionally, some experimental results are represented. These results prove that the methods of structural decomposition really improve the characteristics of FSM circuits.

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