Since the time of McCulloch and Pitts’ Theory (1943) there have been many attempts to model the flow of activity in neural networks. It is possible to simulate neural networks (of rather small size) on a computer, relying on quite reasonable — more or less simplified — assumptions on the dynamic behavior of single neurons. One problem is the arbitrariness of the design of the network (i.e. the connectivity matrix). Here many investigations have studied random connectivity (e.g. Anninos et al. 1970, Griffith 1971, Amari 1974, Dammasch and Wagner 1984) or connectivity that itself changes subject to certain rules (for an overview see Palm 1982)
Neural systems show a wide variety of complex dynamics on different time scales. Specifically, on th...
This thesis is concerned with one important question in artificial neural networks, that is, how bio...
Original article can be found at: http://www.informaworld.com/smpp/title~content=t713411269--Copyrig...
This thesis is concerned with one important question in artificial neural networks, that is, how bio...
We consider a random synaptic pruning in an initially highly interconnected network. It is proved th...
The problem we address in this paper is that of finding effective and parsimonious patterns of conne...
A statement like “$N_\text{s}$ source neurons and $N_\text{t}$ target neurons are connected randomly...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
Connectivity in local cortical networks is far from random: Reciprocal connections are over-represen...
Connectivity in local cortical networks is far from random: Not only are reciprocal connections over...
The comprehension of the mechanisms at the basis of the functioning of complexly interconnected netw...
This paper describes how to analytically characterize the connectivity of neuromorphic networks tak...
<p><b>A</b>, An example of connectivity matrix for 80 excitatory neurons containing a single cluster...
In the absence of sensory stimulation, neocortical circuits display complex patterns of neural activ...
<p>The connectivity of cortical neuronal networks is complex, exhibiting clustered network motifs an...
Neural systems show a wide variety of complex dynamics on different time scales. Specifically, on th...
This thesis is concerned with one important question in artificial neural networks, that is, how bio...
Original article can be found at: http://www.informaworld.com/smpp/title~content=t713411269--Copyrig...
This thesis is concerned with one important question in artificial neural networks, that is, how bio...
We consider a random synaptic pruning in an initially highly interconnected network. It is proved th...
The problem we address in this paper is that of finding effective and parsimonious patterns of conne...
A statement like “$N_\text{s}$ source neurons and $N_\text{t}$ target neurons are connected randomly...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
Connectivity in local cortical networks is far from random: Reciprocal connections are over-represen...
Connectivity in local cortical networks is far from random: Not only are reciprocal connections over...
The comprehension of the mechanisms at the basis of the functioning of complexly interconnected netw...
This paper describes how to analytically characterize the connectivity of neuromorphic networks tak...
<p><b>A</b>, An example of connectivity matrix for 80 excitatory neurons containing a single cluster...
In the absence of sensory stimulation, neocortical circuits display complex patterns of neural activ...
<p>The connectivity of cortical neuronal networks is complex, exhibiting clustered network motifs an...
Neural systems show a wide variety of complex dynamics on different time scales. Specifically, on th...
This thesis is concerned with one important question in artificial neural networks, that is, how bio...
Original article can be found at: http://www.informaworld.com/smpp/title~content=t713411269--Copyrig...