We investigate how to couple a learnable brain-like'' controller to a cell-like'' Gray--Scott substrate to steer pattern formation with minimal effort. A compact convolutional policy is embedded in a differentiable PyTorch reaction--diffusion simulator, producing spatially smooth, bounded modulations of the feed and kill parameters ($ΔF$, $ΔK$) under a warm--hold--decay gain schedule. Training optimizes Turing-band spectral targets (FFT-based) while penalizing control effort ($\ell_1/\ell_2$) and instability. We compare three regimes: pure reaction--diffusion, NN-dominant, and a hybrid coupling. The hybrid achieves reliable, fast formation of target textures: 100% strict convergence in $\sim 165$ steps, matching cell-only spectral selectivity (0.436 vs.\ 0.434) while using $\sim 15\times$ less $\ell_1$ effort and $>200\times$ less $\ell_2$ power than NN-dominant control. An amplitude sweep reveals a non-monotonic Goldilocks'' zone ($A \approx 0.03$--$0.045$) that yields 100\% quasi convergence in 94--96 steps, whereas weaker or stronger gains fail to converge or degrade selectivity. These results quantify morphological computation: the controller seeds then cedes,'' providing brief, sparse nudges that place the system in the correct basin of attraction, after which local physics maintains the pattern. The study offers a practical recipe for building steerable, robust, and energy-efficient embodied systems that exploit an optimal division of labor between centralized learning and distributed self-organization.

An efficient method of self-organizing associative databases is proposed together with applications to robot eyesight systems. The proposed databases can associate any input with some output. In the first half part of discussion, an algorithm of self-organization is proposed. From an aspect of hardware, it produces a new style of neural network. In the latter half part, an applicability to handwritten letter recognition and that to an autonomous mobile robot system are demonstrated.

We have mathematically shown that cortical maps in the primary sensory cortices can be reproduced by using three hypotheses which have physiological basis and meaning. Here, our main focus is on ocular.dominance Monte Carlo simulations on the segregation of ipsilateral and contralateral afferent terminals are carried out. Based on these, we show that almost all the physiological experimental results concerning the ocular dominance patterns of cats and monkeys reared under normal or various abnormal visual conditions can be explained from a viewpoint of the phase transition phenomena. In order to describe the use-dependent self-organization of neural connections {Singer,1987 and Frank,1987}, we have proposed a set of coupled equations involving the electrical activities and neural connection density {Tanaka, 1988}, by using the following physiologically based hypotheses: (1) Modifiable synapses grow or collapse due to the competition among themselves for some trophic factors, which are secreted retrogradely from the postsynaptic side to the presynaptic side.

We are exploring the significance of biological complexity for neuronal computation. Here we demonstrate that Hebbian synapses in realistical(cid:173) ly-modeled hippocampal pyramidal cells may give rise to two novel forms of self -organization in response to structured synaptic input. First, on the basis of the electrotonic relationships between synaptic contacts, a cell may become tuned to a small subset of its input space. Second, the same mechanisms may produce clusters of potentiated synapses across the space of the dendrites. The latter type of self-organization may be functionally significant in the presence of nonlinear dendritic conduc(cid:173) tances.

Ion channels are the dynamical systems of the nervous system. Their distribution within the membrane governs not only communication of in(cid:173) formation between neurons, but also how that information is integrated within the cell. Here, an argument is presented for an'anti-Hebbian' rule for changing the distribution of voltage-dependent ion channels in order to flatten voltage curvatures in dendrites. Simulations show that this rule can account for the self-organisation of dynamical receptive field properties such as resonance and direction selectivity. It also creates the conditions for the faithful conduction within the cell of signals to which the cell has been exposed.

Large networks of spiking neurons show abrupt changes in their collective dynamics resembling phase transitions studied in statistical physics. An example of this phenomenon is the transition from irregular, noise-driven dynamics to regular, self-sustained behavior observed in networks of integrate-and-fire neurons as the interaction strength between the neurons increases. In this work we show how a network of spiking neurons is able to self-organize towards a critical state for which the range of possible inter-spike-intervals (dynamic range) is maximized. Self-organization occurs via synaptic dynamics that we analytically derive. The resulting plasticity rule is defined locally so that global homeostasis near the critical state is achieved by local regulation of individual synapses.

Despite $1/f$ noise being ubiquitous in both natural and artificial systems, no general explanations for the phenomenon have received widespread acceptance. One well-known system where $1/f$ noise has been observed in is the human brain, with this 'noise' proposed by some to be important to the healthy function of the brain. As deep neural networks (DNNs) are loosely modelled after the human brain, and as they start to achieve human-level performance in specific tasks, it might be worth investigating if the same $1/f$ noise is present in these artificial networks as well. Indeed, we find the existence of $1/f$ noise in DNNs - specifically Long Short-Term Memory (LSTM) networks modelled on real world dataset - by measuring the Power Spectral Density (PSD) of different activations within the network in response to a sequential input of natural language. This was done in analogy to the measurement of $1/f$ noise in human brains with techniques such as electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI). We further examine the exponent values in the $1/f$ noise in "inner" and "outer" activations in the LSTM cell, finding some resemblance in the variations of the exponents in the fMRI signal. In addition, comparing the values of the exponent at "rest" compared to when performing "tasks" of the LSTM network, we find a similar trend to that of the human brain where the exponent while performing tasks is less negative.

In the Report "Self-organization of river channels as a critical filter on climate signals," Figs. 2 and 4 were affected by an error in the treatment of the equation within the Python programming environment; specifically, the exponent 3/2 was mistakenly computed as integer division (3/2 1) instead of float division (3.0/2.0 1.5). Because the integral is mostly dependent on time and not shear velocity, the differences in the corrected figures are slight, and the conclusions of the paper are not affected. The HTML and PDF versions of the paper have been corrected, as have the supplementary materials (figs.

Spatial and temporal variations in rainfall are hypothesized to influence landscape evolution through erosion and sediment transport by rivers. However, determining the relation between rainfall and river dynamics requires a greater understanding of the feedbacks between flooding and a river's capacity to transport sediment. We analyzed channel geometry and stream-flow records from 186 coarse-grained rivers across the United States. We found that channels adjust their shape so that floods slightly exceed the critical shear velocity needed to transport bed sediment, independently of climatic, tectonic, and bedrock controls. The distribution of fluid shear velocity associated with floods is universal, indicating that self-organization of near-critical channels filters the climate signal evident in discharge.

Large networks of spiking neurons show abrupt changes in their collective dynamics resembling phase transitions studied in statistical physics. An example of this phenomenon is the transition from irregular, noise-driven dynamics to regular, self-sustained behavior observed in networks of integrate-and-fire neurons as the interaction strength between the neurons increases. In this work we show how a network of spiking neurons is able to self-organize towards a critical state for which the range of possible inter-spike-intervals (dynamic range) is maximized. Self-organization occurs via synaptic dynamics that we analytically derive. The resulting plasticity rule is defined locally so that global homeostasis near the critical state is achieved by local regulation of individual synapses.