Papers by Santosh Manicka
Highlights A minimal model shows how cells can sense largescale patterns of cell voltage Model ma... more Highlights A minimal model shows how cells can sense largescale patterns of cell voltage Model makes predictions about the outcome of new bioelectric patterns Model predictions are verified by experiments in Xenopus brain development Higher-order information integration is seen in voltage-transcription dynamics
The Secretary problem is studied with minimal cognitive agents, being a problem that needs memory... more The Secretary problem is studied with minimal cognitive agents, being a problem that needs memory and judgment. A sequence of values, drawn from an unknown range, is presented; the agent has only one chance to pick a single value as they are presented, and should try to maximize the value chosen. In extension of previous work (Tuci et al. 2002), Continuous Time Recurrent Neural Networks (CTRNN) are evolved to solve the problem, and then their strategies are analyzed by relating mechanisms to behavior. Strategies similar to the known optimal strategy are observed, and it is noted that significantly different strategies can be generated by very different mechanisms that perform equally well.
Entropy, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
The Role of Canalization in the Spreading of Perturbations in Boolean Networks
Network Science has provided predictive models of many complex systems from molecular biology to ... more Network Science has provided predictive models of many complex systems from molecular biology to social interactions. Most of this success is achieved by reducing multivariate dynamics to a graph of static interactions. Such network structure approach has provided many insights about the organization of complex systems. However, there is also a need to understand how to control them; for example, to revert a diseased cell to a healthy state or a mature cell to a pluripotent state in systems biology models of biochemical regulation. Based on recent work [2], using various Boolean models of biochemical regulation dynamics and large ensembles of network motifs, we show that in general the control of complex networks cannot be predicted from structure alone. Structure-only methods such as structural controllability and minimum dominating set theory both undershoot and overshoot the number and which sets of variables actually control these models, highlighting the importance of dynamics ...
Living systems operate in a critical dynamical regime—between order and chaos—where they are both... more Living systems operate in a critical dynamical regime—between order and chaos—where they are both resilient to perturbation, and flexible enough to evolve. To characterize such critical dynamics, the established structural theory of criticality uses automata network connectivity and node bias (to be on or off) as tuning parameters. This parsimony in the number of parameters needed sometimes leads to uncertain predictions about the dynamical regime of both random and systems biology models of biochemical regulation. We derive a more accurate theory of criticality by accounting for canalization, the existence of redundancy that buffers automata response to inputs. The new canalization theory of criticality is based on a measure of effective connectivity. It contributes to resolving the problem of finding precise ways to design or control network models of biochemical regulation for desired dynamical behavior. Our analyses reveal that effective connectivity significantly improves the p...
Toward Modeling Regeneration via Adaptable Echo State Networks
The Secretary problem is studied with minimal cognitive agents, being a problem that needs memory... more The Secretary problem is studied with minimal cognitive agents, being a problem that needs memory and judgment. A sequence of values, drawn from an unknown range, is presented; the agent has only one chance to pick a single value as they are presented, and should try to maximize the value chosen. In extension of previous work (Tuci et al. 2002), Continuous Time Recurrent Neural Networks (CTRNN) are evolved to solve the problem, and then their strategies are analyzed by relating mechanisms to behavior. Strategies similar to the known optimal strategy are observed, and it is noted that significantly different strategies can be generated by very different mechanisms that perform equally well.
Gene regulatory networks exhibit several kinds of memory: Quantification of memory in biological and random transcriptional networks
iScience
Artificial Life 13, Jul 2, 2012
A pair of Continuous-time Recurrent Neural Network (CTRNN) based agents called "Sender" and "Rece... more A pair of Continuous-time Recurrent Neural Network (CTRNN) based agents called "Sender" and "Receiver" is evolved on a circular world. Their collective objective is to communicate and move to a target-the Sender needs to communicate the address of a target location on the circle, and the Receiver needs to move to that location after receiving the communication. In extension of previous work (Williams and Beer, 2008), the agents are evolved under conditions different from the original work. Qualitative analysis of the most successful agent-pair shows that the Receiver's behavior is reminiscent of Newton's equations of motion in relating its initial velocity to the target address communicated to it. Further analysis using information-theoretic tools reveals a pair of neurons that hold crucial information required for the successful functioning of the Receiver. They are also shown to employ the same kind of information for slightly different purposes.
Artificial Life 13, Jul 2, 2012
A pair of Continuous-time Recurrent Neural Network (CTRNN) based agents called "Sender" and "Rece... more A pair of Continuous-time Recurrent Neural Network (CTRNN) based agents called "Sender" and "Receiver" is evolved on a circular world. Their collective objective is to communicate and move to a target-the Sender needs to communicate the address of a target location on the circle, and the Receiver needs to move to that location after receiving the communication. In extension of previous work (Williams and Beer, 2008), the agents are evolved under conditions different from the original work. Qualitative analysis of the most successful agent-pair shows that the Receiver's behavior is reminiscent of Newton's equations of motion in relating its initial velocity to the target address communicated to it. Further analysis using information-theoretic tools reveals a pair of neurons that hold crucial information required for the successful functioning of the Receiver. They are also shown to employ the same kind of information for slightly different purposes.
Scientific Reports, 2019
The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs i... more The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.
Philosophical Transaction of the Royal Society B, 2019
Brains exhibit plasticity, multi-scale integration of information, computation and memory, having... more Brains exhibit plasticity, multi-scale integration of information, computation and memory, having evolved by specialization of non-neural cells that already possessed many of the same molecular components and functions. The emerging field of basal cognition provides many examples of decision-making throughout a wide range of non-neural systems. How can biological information processing across scales of size and complexity be quantitatively characterized and exploited in biomedical settings? We use pattern regulation as a context in which to introduce the Cognitive Lens-a strategy using well-established concepts from cognitive and computer science to complement mechanistic investigation in biology. To facilitate the assimilation and application of these approaches across biology, we review tools from various quantitative disciplines, including dynamical systems, information theory and least-action principles. We propose that these tools can be extended beyond neural settings to predict and control systems-level outcomes, and to understand biological patterning as a form of primitive cognition. We hypothesize that a cognitive-level information-processing view of the functions of living systems can complement reductive perspectives, improving efficient top-down control of organism-level outcomes. Exploration of the deep parallels across diverse quantitative paradigms will drive integrative advances in evolutionary biology, regenera-tive medicine, synthetic bioengineering, cognitive neuroscience and artificial intelligence. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.
Independent study
CTRNN-based minimal agents were evolved to solve the simple problem of phototaxis. The Khepera ro... more CTRNN-based minimal agents were evolved to solve the simple problem of phototaxis. The Khepera robot simulator was used to do the same. Two successful agents with qualitatively different behaviors were evolved. Their behavior was analyzed and their underlying dynamical geometry was studied and used to explain certain phenomena that could otherwise not have been explained. More questions were raised as a result of such analyses and they are now part of my on-going work. Further, a different flavor of phototaxis with an "active decision making" tinge added to it is proposed and is also now a part of my on-going work.
International Conference on Artifical Life, 2013
A pair of Continuous-time Recurrent Neural Network (CTRNN) based agents called "Sender" and "Rece... more A pair of Continuous-time Recurrent Neural Network (CTRNN) based agents called "Sender" and "Receiver" is evolved on a circular world. Their collective objective is to communicate and move to a target -the Sender needs to communicate the address of a target location on the circle, and the Receiver needs to move to that location after receiving the communication. In extension of previous work , the agents are evolved under conditions different from the original work. Qualitative analysis of the most successful agent-pair shows that the Receiver's behavior is reminiscent of Newton's equations of motion in relating its initial velocity to the target address communicated to it. Further analysis using information-theoretic tools reveals a pair of neurons that hold crucial information required for the successful functioning of the Receiver. They are also shown to employ the same kind of information for slightly different purposes.
International Conference on Artifical Life, Jan 1, 2008
European Conference on Artificial Life, 2009
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Papers by Santosh Manicka